Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-18 M.E. Fagan, D.C. Morton, B.D. Cook, J. Masek, F. Zhao, R.F. Nelson, C. Huang
The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest cover in the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations. Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat-based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.
The utility of Random Forests for wildfire severity mapping Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-17 L. Collins, P. Griffioen, G. Newell, A. Mellor
Reliable fire severity mapping is a vital resource for fire scientists and land management agencies globally. Satellite derived pre- and post-fire differenced severity indices (∆FSI), such as the differenced Normalised Burn Ratio (∆NBR), are widely used to map the severity of large wildfires. Fire severity classification is commonly undertaken through the identification of severity class thresholds in ∆FSI. However, several shortcomings have been identified with severity classifications using ∆FSI, including poor delineation of low fire severity classes, and unsatisfactory performance when ∆FSI classification thresholds are applied to new landscapes. Our study assesses the performance of the Random Forest classifier (RF) for improving the accuracy of satellite based wildfire severity mapping across heterogeneous landscapes using Landsat imagery. We collected point based fire severity training data (n = 10,855) from sixteen large wildfires occurring across south-eastern Australia between 2006 and 2016. The predictive accuracy of fire severity classification using ∆NBR and the RF incorporating numerous spectral indices, was assessed using bootstrapping and cross validation. Image acquisition and index calculation for each fire was undertaken in Google Earth Engine (GEE). Results of the bootstrapping validation show that the RF classifier had very high classification accuracy (>95%) for unburnt (UB), crown scorch (CS) and crown consumption (CC) severity classes, and high classification accuracy (>74%) for low severity classes (crown unburnt, CU; partial crown scorch, PCS). The RF classification outperformed the ∆NBR classification for all severity classes, increasing classification accuracy by between 6%–21%. Cross validation using independent fires produced similar median classification accuracy as the bootstrapping validation, though the RF classification of CU was substantially reduced to ~55%. ∆NBR, ∆NDWI and ∆NDVI were the three most important variables in the RF model. The Landsat satellite platform used to derive the indices had little effect on classification accuracy. Maps produced using the RF classifier in GEE had similar spatial patterns in fire severity classes as maps produced using time-consuming hand digitisation of aerial images. GEE was found to be a highly efficient platform for image acquisition, processing and production of severity maps. Our study shows that fire severity mapping using RF classifiers provides a robust method for broad scale mapping of fire severity across heterogeneous landscapes. Furthermore, the GEE-based classification framework supports the operational application of this approach in a land management agency context for the rapid production of fire severity maps.
Optimal estimation for imaging spectrometer atmospheric correction Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-17 David R. Thompson, Vijay Natraj, Robert O. Green, Mark C. Helmlinger, Bo-Cai Gao, Michael L. Eastwood
We present a new method for atmospheric correction of remote Visible Shortwave Infrared (VSWIR) imaging spectroscopy. Our approach fits a combined model of atmospheric scattering, absorption, and surface reflectance across the solar reflected interval from 380 to 2500 nm. This can estimate spectrally-broad atmospheric perturbations such as aerosol effects that are difficult to retrieve with narrow spectral windows. A probabilistic formulation from Optimal Estimation inversion theory accounts for uncertainties in model parameters and measurement noise. This paper presents a field experiment using NASA's Next Generation Visible/Near Infrared Imaging Spectrometer (AVIRIS-NG) with analysis of retrieval accuracy and information content. The inversion outperforms traditional approaches, achieving mean reflectance accuracy of 1.0% on diverse validation surfaces. Predicted posterior distributions fully explain the observed discrepancies, demonstrating the first closed uncertainty budget for VSWIR imaging spectrometer atmospheric correction. This shows the potential of combined surface/atmosphere fitting to advance the accuracy and statistical rigor of remote reflectance measurements.
Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-13 Joseph J. Erinjery, Mewa Singh, Rafi Kent
Detailed mapping and regular monitoring of tropical rainforests is important for conservation and management of highly fragmented tropical rainforest habitats and biodiversity. Several studies have observed that it is highly challenging to map different vegetation types in tropical rainforests due to large environmental heterogeneity, high topographical variability and near constant cloud cover. In the present study, we assessed the capability of optical multispectral Sentinel-2 MSI bands, their derived NDVI and textures, and SAR Sentinel-1 bands and their textures to discriminate different vegetation types in the tropical rainforests of the Western Ghats using maximum likelihood and random forest classification. We also compared the results of our classification with previous such maps of the study area. Finally, we evaluated the magnitude of habitat fragmentation by using derived landscape metrics. Our classification had high accuracy (>75%), especially compared to previous classification efforts. Our results emphasise the significance of using vegetation indices and textures for vegetation type classification in the Western Ghats. Furthermore, the results suggest that rainforest habitats and other agro-ecosystems, suitable as habitats for numerous plant and animal species, are highly fragmented and require top conservation priority. High spectral and spatial resolution, continuity, affordability and access makes Sentinel-2 one of the best options for regular monitoring of tropical rainforests.
A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-13 Tao Liu, Amr Abd-Elrahman, Alina Zare, Bon A. Dewitt, Luke Flory, Scot E. Smith
Context information is rarely used in the object-based landcover classification. Previous models that attempted to utilize this information usually required the user to input empirical values for critical model parameters, leading to less optimal performance. Multi-view image information is useful for improving classification accuracy, but the methods to assimilate multi-view information to make it usable for context driven models have not been explored in the literature. Here we propose a novel method to exploit the multi-view information for generating class membership probability. Moreover, we develop a new conditional random field model to integrate multi-view information and context information to further improve landcover classification accuracy. This model does not require the user to manually input parameters because all parameters in the Conditional Random Field (CRF) model are fully learned from the training dataset using the gradient descent approach. Using multi-view data extracted from small Unmanned Aerial Systems (UASs), we experimented with Gaussian Mixed Model (GMM), Random Forest (RF), Support Vector Machine (SVM) and Deep Convolutional Neural Networks (DCNN) classifiers to test model performance. The results showed that our model improved average overall accuracies from 58.3% to 74.7% for the GMM classifier, 75.8% to 87.3% for the RF classifier, 75.0% to 84.4% for the SVM classifier and 80.3% to 86.3% for the DCNN classifier. Although the degree of improvement may depend on the specific classifier respectively, the proposed model can significantly improve classification accuracy irrespective of classifier type.
PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-13 Katja Grossmann, Christian Frankenberg, Troy S. Magney, Stephen C. Hurlock, Ulrike Seibt, Jochen Stutz
Solar-Induced Chlorophyll Fluorescence (SIF) is an emission of light in the 650–850 nm spectral range from the excited state of the chlorophyll-a pigment after absorption of photosynthetically active radiation (PAR). As this is directly linked to the electron transport chain in oxygenic photosynthesis, SIF is a powerful proxy for photosynthetic activity. SIF observations are relatively new and, while global scale measurements from satellites using high-resolution spectroscopy of Fraunhofer bands are becoming more available, observations at the intermediate canopy scale using these techniques are sparse. We present a novel ground-based spectrometer system - PhotoSpec - for measuring SIF in the red (670–732 nm) and far-red (729–784 nm) wavelength range as well as canopy reflectance (400–900 nm) to calculate vegetation indices, such as the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the photochemical reflectance index (PRI). PhotoSpec includes a 2D scanning telescope unit which can be pointed to any location in a canopy with a narrow field of view (FOV = 0.7°). PhotoSpec has a high signal-to-noise ratio and spectral resolution, which allows high precision solar Fraunhofer line retrievals over the entire fluorescence wavelength range under all atmospheric conditions using a new two-step linearized least-squares retrieval procedure. Initial PhotoSpec observations include the diurnal SIF cycle of single broad leaves, grass, and dark-light transitions. Results from the first tower-based measurements in Costa Rica show that the instrument can continuously monitor SIF of several tropical species throughout the day. The PhotoSpec instrument can be used to explore the relationship between SIF, photosynthetic efficiencies, Gross Primary Productivity (GPP), and the impact of canopy radiative transfer, viewing geometry, and stress conditions at the canopy scale.
Characterization of indicator tree species in neotropical environments and implications for geological mapping Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-17 Cibele Hummel do Amaral, Teodoro Isnard Ribeiro de Almeida, Carlos Roberto de Souza Filho, Dar A. Roberts, Stephen James Fraser, Marcos Nopper Alves, Moreno Botelho
Geobotanical remote sensing (GbRS) in the strict sense is an indirect approach to obtain geological information in heavily vegetated areas for mineral prospecting and geological mapping. Using ultra- and hyperspectral technologies, the goals of this research comprise the definition and mapping of Neotropical tree species that are associated with geological facies (here called geo-environments) as well as their spectral discrimination at leaf and crown scales. This work also aims to investigate the possible relationship between leaf and crown spectral and chemical properties. The study was developed at the Mogi-Guaçu Ecological Station, in the Cerrado domain, southeastern Brazil. Data from 70 sample units, such as sediment texture and species from inventories, were first analyzed through vectorial quantization using Self-Organizing Maps (SOM). Principal Component Analysis and Spearman's ranked correlation coefficients were used to define geo-environments and target-species, respectively. Biochemical and visible to shortwave infrared (VSWIR) point spectral data (350–2500 nm) were collected from the leaves of the target-species, during both rainy and dry seasons. Spectral data from target-species crowns were obtained from hyperspectral images (530–2.532 nm, ProSpecTIR-VS sensor) with 1 m spatial resolution, and acquired in the beginning of the dry season. These spectra were classified using Multiple Endmember Spectral Mixture Analysis (MESMA) with two endmembers (EMs). Based on the MESMA results with two EMs, the best dataset per target-species was chosen for pixel-based image unmixing with three EMs (target-species, other vegetation types and shade). From 121 species sampled in the field, two proved to be associated with floodplains (Alluvial Deposits sequence), two with hills and plateaus of the Aquidauna Formation (Carboniferous sedimentary rocks, Paraná Basin), and two more with a specific facies of the Aquidauna Formation that has a distinctive presence of coarse and very coarse sand. Five target-species were well discriminated at the leaf scale, reaching 90.0% and 85.0% of global accuracies in the rainy season and in the dry season, respectively. Accurate spectral discrimination appears to be linked to the considerable biochemical variability of their leaves in both seasons. Three species were discriminated at the crown scale, with 70.6% of global accuracy. When eight other landscape scale vegetation classes were included in the analyses, only Qualea grandiflora Mart. produced a satisfactory accuracy (61.1% and 100% of producer and user's accuracies, respectively). The spatial distribution of its fraction in the unmixed image, particularly, matches with the geological facies to which it was associated in the field. Ecological requirements for successfully mapping indicator species include broad and random distribution of the target-species' population, and singular physiological, phenological (and spectral) behavior at the imagery acquisition date. Our study shows that, even in tropical conditions, it is possible to use plant species mapping to support geological delineation, where rock exposures are typically rare.
Alluvial fan surface ages recorded by Landsat-8 imagery in Owens Valley, California Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-17 Mitch D'Arcy, Philippa J. Mason, Duna C. Roda-Boluda, Alexander C. Whittaker, James M.T. Lewis, Jens Najorka
Alluvial fans are important depositional landforms that offer valuable records of terrestrial sedimentation history if their surfaces can be mapped and dated accurately. Unfortunately, as this often depends on detailed field mapping and intensive absolute dating techniques, it can be a challenging, expensive and time-consuming exercise. In this study, we demonstrate that quantitative information about the ages of alluvial fan surfaces in Owens Valley, California, is recorded by Landsat-8 multispectral satellite imagery. We show that systematic changes in the wavelength-dependent brightness of fan surfaces occur gradually over a timescale of ~100 kyr in this semi-arid setting, and are highly correlated with known deposit ages. Using spectro-radiometry and X-ray diffraction analysis of sediment samples collected in the field, we interpret that surface reflectance evolves primarily in response to the in-situ production of secondary illite and iron oxide by weathering in this landscape. Furthermore, we demonstrate that first-order predictions of absolute fan surface age can be derived from multispectral imagery when an initial age calibration is available. These findings suggest that multispectral imagery, such as Landsat data, can be used (i) for preliminary mapping of alluvial fans prior to detailed field work and before choosing sampling sites for conventional dating techniques, and (ii) to extend age models to un-dated neighbouring surfaces with equivalent physical properties, once an age-brightness calibration has been established.
Imaging lichen water content with visible to mid-wave infrared (400–5500 nm) spectroscopy Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-13 Lars Granlund, Sarita Keski-Saari, Timo Kumpula, Elina Oksanen, Markku Keinänen
The unique adaptation of lichens to repeated cycles of drying and rehydration makes them ideal subjects for developing remote sensing methodologies for water content estimation. This laboratory-based study evaluates the suitability of simple ratios (SR) and normalized difference indices (NDI), from several spectral regions; visible to near infrared (VNIR, 400–1000 nm), short-wave infrared (SWIR, 1000–2500 nm) and mid-wave infrared (MWIR, 2500–5500 nm) in the water content estimation of seven lichen species. The utilization of a wide wavelength range and several different lichen species allowed the evaluation of the robustness of the water content indices. Our results show that although there was high variability between different lichen species in their spectral responses to increasing water content, the best indices predicted water content accurately for the combination of species (RMSE 16.3%, 10.3% and 13.9% for the test set, in the VNIR, SWIR and MWIR regions, respectively). Generally the NDI indices were found to be slightly better than the SR indices. We also demonstrate the capability of imaging spectroscopy in creating detailed heat maps of the water content with these indices.
Multi-view object-based classification of wetland land covers using unmanned aircraft system images Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-04 Tao Liu, Amr Abd-Elrahman
Traditionally, the multiple images collected by cameras mounted on Unmanned Aircraft Systems (UAS) are mosaicked into a single orthophoto on which Object-Based Image Analysis (OBIA) is conducted. This approach does not take advantage of the Multi-View (MV) information of the individual images. In this study, we introduce a new OBIA approach utilizing multi-view information of original UAS images and compare its performance with that of traditional OBIA, which uses only the orthophoto (Ortho-OBIA). The proposed approach, called multi-view object-based image analysis (MV-OBIA), classifies multi-view object instances on UAS images corresponding to each orthophoto object and utilizes a voting procedure to assign a final label to the orthophoto object. The proposed MV-OBIA is also compared with the classification approaches based on Bidirectional Reflectance Distribution Function (BRDF) simulation. Finally, to reduce the computational burden of multi-view object-based data generation for MV-OBIA and make the proposed approach more operational in practice, this study proposes two window-based implementations of MV-OBIA that utilize a window positioned at the geometric centroid of the object instance, instead of the object instance itself, to extract features. The first window-based MV-OBIA adopts a fixed window size (denoted as FWMV-OBIA), while the second window-based MV-OBIA uses an adaptive window size (denoted as AWMV-OBIA). Our results show that the MV-OBIA substantially improves the overall accuracy compared with Ortho-OBIA, regardless of the features used for classification and types of wetland land covers in our study site. Furthermore, the MV-OBIA also demonstrates a much higher efficiency in utilizing the multi-view information for classification based on its considerably higher overall accuracy compared with BRDF-based methods. Lastly, FWMV-OBIA and AWMV-OBIA both show potential in generating an equal if not higher overall accuracy compared with MV-OBIA at substantially reduced computational costs.
DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Behnaz Bigdeli, Hamed Amini Amirkolaee, Parham Pahlavani
The penetration ability of Light Detection and Ranging (LiDAR) pulses into vegetation cover makes it a valuable tool in forest inventory. Extraction of a Digital Terrain Model (DTM) using LiDAR data is a challenging topic, especially in steep and complex terrains with forest canopy. This paper presents some approaches for ground filtering and interpolating the point cloud to generate DTM in forested terrains. Interpolating the points in dense forests that have high attitude variation is very difficult and make the popular interpolation methods unsatisfactory. This paper proposed a modified Invasive Weed Optimization (IWO) method for finding the optimized coefficients of the polynomial interpolation method. This method had a good performance with 0.210 mm RMSE value in the forested terrains. The interpolated point cloud was used as the input of the proposed ground filtering method for detecting the non-ground pixels. The proposed ground filtering method was structured with two main sections including, iterative geodesic morphology and scan labeling. In the iterative geodesic morphology, some geometric and structural parameters were introduced to investigate the quality of extracted points in each iteration. The scan labeling searched the data pixel by pixel in four directions and labeled the pixels with the high slope value in all directions. The non-ground pixels were obtained by integrating the result of iterative geodesic and scan labeling. Assessment of the ground filtering results using the International Society for Photogrammetry and Remote Sensing (ISPRS) showed 3.92% total error compared to the other reported algorithms. This demonstrated the ability of the proposed approach in recognition of the non-ground pixels. The extracted pixels were removed and the DTM was generated by filling the gaps using the proposed IWO and polynomial interpolation. Some forested regions with various characteristics such as sparse and dense trees on the hills and steep slopes were utilized to evaluate the accuracy of the generated DTM. The computed RMSE in the test areas was 0.463 m, on average, which was acceptable for the complex and forested terrains.
Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Cindy Delloye, Marie Weiss, Pierre Defourny
One of the most common approaches to reducing the environmental impact of nitrogen (N) fertilisation in intensive agrosystems is to adjust the N input of the crop requirement. This adjustment is frequently related to the nitrogen nutrition index (NNI) based on the concepts of the critical and actual N absorbed (kg/ha) in the crop canopy (respectively, NC and CNC). Accurate estimation of the NC and CNC at the field scale over large areas based on freely available satellite imagery is thus a key issue to address. Relying on a large dataset of farmers' fields, this study highlights the high correlation (R2 = 0.90) between the wheat CNC and canopy chlorophyll content (CCC) retrieved from Sentinel-2 (S2) with an Artificial Neural Network (ANN). The estimation is related to errors of 4 and 21 kg/ha (depending on the growing stage), which is a promising result for evaluating the NNI. There are four major outcomes from this result: (i) the importance of working at the canopy level; (ii) the independence of the relationship to the considered cultivars; (iii) the dependence of the relationship on the growing stage; and (iv) the potential to use only the 10 m S2 bands, opening the way for precision agriculture. In parallel, estimation accuracies were investigated for the three biophysical variables (BV) related to the CNC and NC, i.e., the green area index (GAI), leaf chlorophyll content (Cab) and CCC. From this analysis, the added value of the red-edge bands for improving the estimation of the 3 BVs of interest was quantified as was the performance reduction related to the field heterogeneity.
Assessing macrophyte seasonal dynamics using dense time series of medium resolution satellite data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Paolo Villa, Monica Pinardi, Rossano Bolpagni, Jean-Marc Gillier, Peggy Zinke, Florin Nedelcuţ, Mariano Bresciani
The improved spatial and temporal resolution of latest-generation Earth Observation missions, such as Landsat 8 and Sentinel-2, has increased the potential of remote sensing for mapping land surface phenology in inland water systems. The ability of a time series of medium-resolution satellite data to generate quantitative information on macrophyte phenology was examined, focusing on three temperate shallow lakes with connected wetlands in Italy, France, and Romania. Leaf area index (LAI) maps for floating and emergent macrophyte growth forms were derived from a semi-empirical regression model based on the best-performing spectral index, with an error level of 0.11 m2 m−2. Phenology metrics were computed from LAI time series using TIMESAT to analyze the seasonal dynamics of macrophyte spatial distribution patterns and species-dependent variability. Particular seasonal patterns seen in the autochthonous and allochthonous species across the three study areas related to local ecological and hydrological conditions. How characteristics of the satellite dataset (cloud cover threshold, temporal resolution, and missing acquisitions) influenced the phenology metrics obtained was also assessed. Our results indicate that, with a full-resolution time series (5-day revisit time), cloud cover introduced a bias in the phenology metrics of less than 2 days. Even when the temporal resolution was reduced to 15 days (like the Landsat revisit time) the timing of the start and the peak of macrophyte growth could still be mapped with an error of no more than 2–3 days.
Generation and evaluation of the VIIRS land surface phenology product Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Xiaoyang Zhang, Lingling Liu, Yan Liu, Senthilnath Jayavelu, Jianmin Wang, Minkyu Moon, Geoffrey M. Henebry, Mark A. Friedl, Crystal B. Schaaf
Vegetation phenology is widely acknowledged to be a sensitive indicator of the response of ecosystems to climate change, and phenological shifts have been shown to exert substantial impacts on ecosystem function, biodiversity, and carbon budgets at multiple scales. Therefore, long-term records of the phenology of the vegetated land surface are critical in exploring the biological response to environmental change at regional to global scales. Land surface phenology (LSP) from satellite observations has been extensively used to monitor the dynamics of terrestrial ecosystems in the face of a changing climate. Here we introduce and describe the global land surface phenology (GLSP) product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) data at a gridded spatial resolution of 500 m. This new product will provide continuity for the Moderate Resolution Imaging Spectroradiometer (MODIS) GLSP product that has been produced on an operational basis since 2001. The VIIRS GLSP algorithm uses daily VIIRS Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR) data as the primary input to calculate the two-band enhanced vegetation index (EVI2) at each 500 m pixel. The temporal EVI2 trajectory is modeled using a hybrid piecewise logistic function to track the seasonal vegetation development, detect phenological transition dates, calculate the magnitude of vegetation greenness development, and characterize the confidence of phenology detections. The VIIRS GLSP algorithm has been implemented across the contiguous United States, and the resulting phenological metrics have been evaluated through comparisons with species-specific field phenological observations, Landsat phenology retrievals, and the MODIS phenology detections. The results demonstrate that the VIIRS GLSP metrics are of high quality and are in a good agreement with the other independent satellite and field observations. The results also indicate that the uncertainty in the VIIRS GLSP retrievals is primarily associated with missing high quality observations in VIIRS EVI2 time series.
Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Matthias Baumann, Christian Levers, Leandro Macchi, Hendrik Bluhm, Björn Waske, Nestor Ignacio Gasparri, Tobias Kuemmerle
Tropical dry forests and savannas provide important ecosystem services and harbor high biodiversity, yet are globally under pressure from land-use change. Mapping changes in the condition of dry forests and savannas is therefore critical. This can be challenging given that these ecosystems are characterized by continuous gradients of tree and shrub cover, resulting in considerable structural complexity. We developed a novel approach to map, separately, continuous fields of tree cover and shrub cover across the South American Gran Chaco (1,100,000 km2), making full use of the Landsat-8 optical and Sentinel-1 synthetic aperture radar (SAR) image archives. We gathered a large training dataset digitized from very-high resolution imagery and used a gradient-boosting framework to model continuous fields of tree cover and shrub cover at 30-m resolution. Our regression models had high to moderate predictive power (85.5% for tree cover, and 68.5% for shrub cover) and resulted in reliable tree and shrub cover maps (mean squared error of 4.4% and 6.4% for tree- and shrub cover respectively). Models jointly using optical and SAR imagery performed substantially better than models using single-sensor imagery, and model predictors differed strongly in some regions, especially in areas of dense vegetation cover. Mapping tree and shrub cover separately allowed identifying distinct vegetation formations, with shrub-dominated systems mainly in the very dry Chaco, woodlands with large trees mainly in the dry Chaco, and tree-dominated savannas in the wet Chaco. Our tree and shrub cover layers also revealed considerable edge effects in terms of woody cover away from agricultural fields (edge effects extending about 2 km), smallholder ranches (about 1.2 km), and roads and railways (about 1.4 and 0.9 km, respectively). Our analyses highlight both the substantial footprint of land-use on remaining natural vegetation in the Chaco, and the potential of multi-sensoral approaches to monitor forest degradation. More broadly, our approach shows that mapping canopy structure and distinct layers of woody vegetation in dry forest and savannas is possible across large areas, and highlights the value of the growing Landsat and Sentinel archives for doing so.
Multi-decade, multi-sensor time-series modelling—based on geostatistical concepts—to predict broad groups of crops Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-05 Matthew J. Pringle, Michael Schmidt, Daniel R. Tindall
We have mapped the broad groups of crops grown each summer and winter, from 1987 to 2017, for a 300,000-km2 region of Queensland, Australia. These maps are part of a legislated decision-making process for the protection of prime agricultural land. For summer, the two groups of crops are ‘Coarse-grain & Pulse’ and ‘Cotton’. For winter, the two groups of crops are ‘Cereal’ and ‘Pulse’. Non-crop groups, present in both summer and winter, are ‘Bare soil’ and ‘Other’ (comprising pastures, woody vegetation, and crop residues). The foundation of the maps is time-series modelling—specifically, applying the concepts of geostatistics in the temporal domain—to model the variation in land-surface phenology within a growing season. The time-series model is flexible, robust, parsimonious, parallelisable, and able to deal with irregular observations. We combined satellite imagery from the Landsat sensors, as well as, when available, Sentinel-2A and MODIS (with the last two reprojected to the 30-m grid of Landsat). We applied the time-series model pixel-wise across the study region, to three variables derived from satellite imagery gathered for an individual growing season: enhanced vegetation index, and the sub-pixel proportions of bare-ground and non-photosynthetic vegetation. Weekly-averaged predicted phenological metrics then served as explanatory variables in a tiered, tree-based classification model, for the prediction of the groups. The classification model comprised two expert rules and two random forests. Prior to fitting the classification model, geospatial object-based image analysis was used to change the scale of analysis from individual pixels to (approximately) field-based segments. From the perspective of a map-user, in any given growing season we predicted ‘Coarse-grain & Pulse’ correctly in 79% of cases; the values for ‘Cotton’, ‘Cereal’, and ‘Pulse’ were 90%, 84%, and 73%, respectively; ‘Bare soil’ was 72% in summer, and 88% in winter. ‘Other’ was the most accurately mapped group (98% correct in summer, and 99% correct in winter).
Vertically resolved physical and radiative response of ice clouds to aerosols during the Indian summer monsoon season Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-05 Feiyue Mao, Zengxin Pan, David S. Henderson, Wei Wang, Wei Gong
Changes in aerosol loading affect cloud albedo and emission and Earth's radiative balance with a low level of scientific understanding. In this study, we investigate the vertical response of ice clouds to aerosols within the Indian subcontinent during monsoon season (2006–2010) based on multiple satellite observations. As a function of aerosol loading, we find that the cloud optical depth, cloud geometrical depth and ice water path decrease by 0.23 (from 0.39 to 0.16), 0.8 km (from 2.6 to 1.8 km), 5.1 g/m2 (from 7.9 to 2.8 g/m2), respectively, and that ice particles possibly decrease in size and become more spherical in shape as aerosol optical depth (AOD) increases from 0.1 to 1; these changes tend to plateau as AOD increases beyond 1. The absolute negative response between ice clouds and aerosols under moist and unstable atmospheric conditions is stronger than that under drier and stable atmospheric conditions, and vice versa. Moreover, the negative impact of smoke on ice clouds is stronger than dust and polluted dust, which is likely related to the strong absorption properties and poor ice nucleation efficiency of smoke. Aerosol impacts on ice clouds lead to a decrease in the net cloud radiative effect of 7.3 W/m2 (from 18.5 to 11.2 W/m2) as AOD increases from 0.1 to 1. This change in ice cloud properties mainly results in the decrease in downwelling LW radiation to the surface and consequently weakened radiative forcing of ice clouds during the Indian summer monsoon season.
Remote sensing of water constituent concentrations using time series of in-situ hyperspectral measurements in the Wadden Sea Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Behnaz Arabi, Mhd Suhyb Salama, Marcel Robert Wernand, Wouter Verhoef
This study aimed to investigate the capability of the two-stream radiative transfer model 2SeaColor for the simultaneous retrieval of Chlorophyll-a (Chla), Suspended Particulate Matter (SPM) and Colored Dissolved Organic Matter (CDOM) concentrations from remote sensing measurements under various conditions (i.e., solar zenith angle values (SZAs) and water turbidity levels). For this evaluation, a time series of diurnal in-situ hyperspectral measurements of remote sensing reflectance (Rrs) concurrent with in-situ measured Chla and SPM concentrations between 2008 and 2010 by the NIOZ jetty station (the NJS), located in the Dutch part of the Wadden Sea, was used. Validation of the model retrievals against in-situ measurements showed an acceptable accuracy (Chla: R2 = 0.80 and RMSE = 2.98 [mg m−3]; SPM: R2 = 0.89 and RMSE = 2.53 [g m−3]) with good agreement between the temporal trends of measured and retrieved concentration values over multiple years. However, the model inversion results yielded less good estimates at SZA > 60° during winter. Furthermore, the effect of the tide on the variation of daily time series of Chla and SPM concentrations was analyzed. At the particular NJS location, the tidal effects on the concentrations of SPM and Chla were found to be small. The capability of the 2SeaColor model to retrieve reliable estimates, and the favorable location of the NJS, which is little influenced by tidal phase variations, contribute to a better understanding of the long-term variability of Chla and SPM concentrations. The results of this study may support the ongoing efforts on Sentinel-3 Ocean and Land Color Instrument (OLCI) calibration and validation at the Dutch Wadden Sea.
Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-04 Benjamin Kellenberger, Diego Marcos, Devis Tuia
Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large datasets from computer vision. However, the majority of works related to wildlife focuses only on small datasets (typically subsets of UAV campaigns), which might be detrimental when presented with the sheer scale of real study areas for large mammal census. Methods may yield thousands of false alarms in such cases. In this paper, we study how to scale CNNs to large wildlife census tasks and present a number of recommendations to train a CNN on a large UAV dataset. We further introduce novel evaluation protocols that are tailored to censuses and model suitability for subsequent human verification of detections. Using our recommendations, we are able to train a CNN reducing the number of false positives by an order of magnitude compared to previous state-of-the-art. Setting the requirements at 90% recall, our CNN allows to reduce the amount of data required for manual verification by three times, thus making it possible for rangers to screen all the data acquired efficiently and to detect almost all animals in the reserve automatically.
Confirmation of post-harvest spectral recovery from Landsat time series using measures of forest cover and height derived from airborne laser scanning data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Joanne C. White, Ninni Saarinen, Ville Kankare, Michael A. Wulder, Txomin Hermosilla, Nicholas C. Coops, Paul D. Pickell, Markus Holopainen, Juha Hyyppä, Mikko Vastaranta
Landsat time series (LTS) enable the characterization of forest recovery post-disturbance over large areas; however, there is a gap in our current knowledge concerning the linkage between spectral measures of recovery derived from LTS and actual manifestations of forest structure in regenerating stands. Airborne laser scanning (ALS) data provide useful measures of forest structure that can be used to corroborate spectral measures of forest recovery. The objective of this study was to evaluate the utility of a spectral index of recovery based on the Normalized Burn Ratio (NBR): the years to recovery, or Y2R metric, as an indicator of the return of forest vegetation following forest harvest (clearcutting). The Y2R metric has previously been defined as the number of years required for a pixel to return to 80% of its pre-disturbance NBR (NBRpre) value. In this study, the Composite2Change (C2C) algorithm was used to generate a time series of gap-free, cloud-free Landsat surface reflectance composites (1985–2012), associated change metrics, and a spatially-explicit dataset of detected changes for an actively managed forest area in southern Finland (5.3 Mha). The overall accuracy of change detection, determined using independent validation data, was 89%. Areas of forest harvesting in 1991 were then used to evaluate the Y2R metric. Four alternative recovery scenarios were evaluated, representing variations in the spectral threshold used to define Y2R: 60%, 80%, and 100% of NBRpre, and a critical value of z (i.e. the year in which the pixel's NBR value is no longer significantly different from NBRpre). The Y2R for each scenario were classified into five groups: recovery within <10 years, 10–13 years, 14–17 years, >17 years, and not recovered. Measures of forest structure (canopy height and cover) were obtained from ALS data. Benchmarks for height (>5 m) and canopy cover (>10%) were applied to each recovery scenario, and the percentage of pixels that attained both of these benchmarks for each recovery group, was determined for each Y2R scenario. Our results indicated that the Y2R metric using the 80% threshold provided the most realistic assessment of forest recovery: all pixels considered in our analysis were spectrally recovered within the analysis period, with 88.88% of recovered pixels attaining the benchmarks for both cover and height. Moreover, false positives (pixels that had recovered spectrally, but not structurally) and false negatives (pixels that had recovered structurally, but not spectrally) were minimized with the 80% threshold. This research demonstrates the efficacy of LTS-derived assessments of recovery, which can be spatially exhaustive and retrospective, providing important baseline data for forest monitoring.
Hyperspectral remote sensing of fire: State-of-the-art and future perspectives Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-03 Sander Veraverbeke, Philip Dennison, Ioannis Gitas, Glynn Hulley, Olga Kalashnikova, Thomas Katagis, Le Kuai, Ran Meng, Dar Roberts, Natasha Stavros
Fire is a widespread Earth system process with important carbon and climate feedbacks. Multispectral remote sensing has enabled mapping of global spatiotemporal patterns of fire and fire effects, which has significantly improved our understanding of interactions between ecosystems, climate, humans and fire. With several upcoming spaceborne hyperspectral missions like the Environmental Mapping And Analysis Program (EnMAP), the Hyperspectral Infrared Imager (HyspIRI) and the Precursore Iperspettrale Della Missione Applicativa (PRISMA), we provide a review of the state-of-the-art and perspectives of hyperspectral remote sensing of fire. Hyperspectral remote sensing leverages information in many (often more than 100) narrow (smaller than 20 nm) spectrally contiguous bands, in contrast to multispectral remote sensing of few (up to 15) non-contiguous wider (greater than 20 nm) bands. To date, hyperspectral fire applications have primarily used airborne data in the visible to short-wave infrared region (VSWIR, 0.4 to 2.5 μm). This has resulted in detailed and accurate discrimination and quantification of fuel types and condition, fire temperatures and emissions, fire severity and vegetation recovery. Many of these applications use processing techniques that take advantage of the high spectral resolution and dimensionality such as advanced spectral mixture analysis. So far, hyperspectral VSWIR fire applications are based on a limited number of airborne acquisitions, yet techniques will approach maturity for larger scale application when spaceborne imagery becomes available. Recent innovations in airborne hyperspectral thermal (8 to 12 μm) remote sensing show potential to improve retrievals of temperature and emissions from active fires, yet these applications need more investigation over more fires to verify consistency over space and time, and overcome sensor saturation issues. Furthermore, hyperspectral information and structural data from, for example, light detection and ranging (LiDAR) sensors are highly complementary. Their combined use has demonstrated advantages for fuel mapping, yet its potential for post-fire severity and combustion retrievals remains largely unexplored.
Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-03 Tianhao Zhang, Zhongmin Zhu, Wei Gong, Zerun Zhu, Kun Sun, Lunche Wang, Yusi Huang, Feiyue Mao, Huanfeng Shen, Zhiwei Li, Kai Xu
Satellite-derived aerosol optical depth (AOD) has been widely used to estimate ground-level PM2.5 concentrations due to its spatially continuous observation. However, the coarse spatial resolutions (e.g., 3 km, 6 km, or 10 km) of the primary satellite AOD products have weakness to capture the characteristics of urban-scale PM2.5 patterns. Moreover, high-resolution (e.g., 1 km) PM2.5 estimations are still unable to be related to the urban landscape or to small geographical units, which is crucial for analyzing the urban pollution structure. In this study, the daily PM2.5 concentrations were estimated using the new AOD data with a 160 m spatial resolution retrieved by the Gaofen-1 (GF) wide field of view (WFV) along with the nested linear mixed effects model and ancillary variables from the Weather Research & Forecasting (WRF) meteorological simulation data. The experiment was conducted in Wuhan, Beijing, and Shanghai, which suffers from severe atmospheric fine particle pollution in recent years. The proposed model performed well for both GF and Moderate Resolution Imaging Spectroradiometer (MODIS), with slight over-fitting and little spatial autocorrelation. Regarding to the GF PM2.5 estimation, model fitting yielded R2 values of 0.96, 0.91 and 0.95 and mean prediction error (MPE) of 10.13, 11.89 and 7.34 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The site-based cross validation achieved R2 values of 0.92, 0.88 and 0.89, and MPE of 13.69, 16.76 and 12.59 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The day-of-years based cross validation resulted in R2 of 0.54, 0.58 and 0.50, and MPE of 30.46, 27.12 and 31.58 μg/m3 for Wuhan, Beijing and Shanghai, respectively, indicating that it was practicable to estimate the GF PM2.5 in the days without enough AOD-PM2.5 matchups. The ultrahigh resolution PM2.5 estimations offer substantial advantages for providing finer spatially resolved PM2.5 trends. Additionally, it offers new approaches to locate main PM2.5 emission sources, evaluate the local PM2.5 contribution proportion, and quantify the daily PM2.5 emission level via remote sensing techniques. Along with the joint observations via other high-resolution satellites, the temporal resolution of GF PM2.5 will be further improved. In all, this study not only provides possibilities for further applications in the precise analysis of urban inner PM2.5 pollution patterns but also establishes a foundation for constructing a high-resolution satellite air monitoring network in China.
Mapping surface deformation and thermal dilation of arch bridges by structure-driven multi-temporal DInSAR analysis Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-03 Xiaoqiong Qin, Lu Zhang, Mengshi Yang, Heng Luo, Mingsheng Liao, Xiaoli Ding
Arch bridges are important transportation infrastructures widely distributed in China, but they are prone to structural defects due to aging without routine inspection and maintenance. Therefore, Structural Health Monitoring (SHM) of these bridges is urgently needed by civil engineers to effectively reduce the risk of bridge damage or collapse on public safety. An essential method for SHM, the modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, can detect subtle deformation of bridges at relatively low costs. Nevertheless, identifying dense point-like targets (PTs) on such partially coherent arch bridges in SAR image is more difficult than that for other man-made objects, owing to their complex structures and backscattering behaviors. Furthermore, the complex mechanical properties of arch bridges, due to the varying arch-beam interactions, make it hard to separate the surface deformation and thermal dilation accurately, and the lack of specific structural knowledge, that can help to understand the deformation evolution process, limits the global structural risk assessment. Aiming at these problems, we developed a structure-driven multi-temporal DInSAR approach for arch bridge-specific SHM. By introducing three structure-driven steps, i.e. backscattering geometrical interpretation, linear thermal dilation estimation and validation, and Deformation Feature Points (DFPs) based risk assessment, into the traditional DInSAR method, the reliability of PTs identification, thermal dilation separation, and structural risk assessment for arch bridges are significantly improved. The effectiveness of our approach was fairly presented by two case studies of the Rainbow and Lupu bridges, and the experimental results were verified by leveling benchmark validation, cross-sensor comparison, as well as structural-reliability assessment. Our results revealed that arch bridges exhibit a similar pattern of PTs distribution that is dense around piers and sparse on the spans, as well as a symmetrical progressive pattern of surface deformation with the subsidence increasing from piers and reaching a peak at the central spans. In contrast, magnitudes and mechanisms of thermal dilation are different, and highly dependent on the materials and structural characteristics of specific bridges.
An object-based convolutional neural network (OCNN) for urban land use classification Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-03 Ce Zhang, Isabel Sargent, Xin Pan, Huapeng Li, Andy Gardiner, Jonathon Hare, Peter M. Atkinson
Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than Pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the Pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.
Fractional vegetation cover estimation by using multi-angle vegetation index Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-30 Xihan Mu, Wanjuan Song, Zhan Gao, Tim R. McVicar, Randall J. Donohue, Guangjian Yan
The vegetation index-based (VI-based) mixture model is widely used to derive green fractional vegetation cover (FVC) from remotely sensed data. Two critical parameters of the model are the vegetation index values of fully-vegetated and bare soil pixels (denoted Vx and Vn hereafter). These are commonly empirically set according to spatial and/or temporal statistics. The uncertainty and difficulty of accurately determining Vx and Vn in many ecosystems limits the accuracy of resultant FVC estimates and hence reduces the utility of VI-based mixture model for FVC estimation. Here, an improved method called MultiVI is developed to quantitatively estimate Vx and Vn from angular VI acquired at two viewing angles. The directional VI is calculated from the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43A1) data. The results of simulated evaluation with 10% added noise show that the root mean square deviation (RMSD) of FVC is approximately 0.1 (the valid FVC range is [0, 1]). Direct evaluation against 34 globally-distributed FVC measurements from VAlidation of Land European Remote sensing Instruments (VALERI) sites during 2000 to 2014 demonstrated that the accuracy of MultiVI FVC (R2 = 0.866, RMSD = 0.092) exceeds than from SPOT/VEGETATION bioGEOphysical product version 1 (GEOV1) FVC (R2 = 0.795, RMSD = 0.159). MultiVI FVC also exhibits higher correlation to the VALERI reference FVC than does the MODIS fraction of photosynthetically active radiation product (MCD15A2H; R2 is 0.696). A key advantage of the MultiVI method is obvious in areas where fully-vegetated and/or bare soil pixels do not exist in moderate-coarse spatial resolution imagery when compared to the conventional VI-based mixture modelling. The MultiVI method can be flexibly implemented over regional or global scales to monitor FVC, with maps of Vx and Vn generated as two important byproducts.
Wheat crop biophysical parameters retrieval using hybrid-polarized RISAT-1 SAR data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-29 Sugandh Chauhan, Hari Shanker Srivastava, Parul Patel
The main goal of this study was to assess the potential of SAR backscatter signatures (RH and RV) retrieved from hybrid-polarized RISAT-1 SAR data in providing relevant information about the wheat growth parameters (leaf area index or LAI, plant water content or PWC, plant volume or PV and wet biomass or WB) over the entire growing season. The study was carried out over the parts of Bharatpur and Mathura districts located in Rajasthan and Uttar Pradesh (India), respectively. The three-date time series hybrid-polarized dataset was collected coincident to which a comprehensive ground truth campaign was organised. We propose that refining the total backscatter (σtotal0) values after minimising the effect of underlying/background soil cover, would result in more accurate retrieval of plant parameters since it is the vegetation backscatter, which ultimately has a direct correlation with the crop biophysical parameters. It was achieved using a semi-empirical water cloud model (WCM) based approach. The applicability of four different combinations of canopy descriptors, i.e. leaf area index (LAI), plant water content (PWC), leaf water area index (LWAI) and interaction factor (IF that takes into consideration the moisture distribution per unit volume) was tested on the RH and RV backscatter. We found that WCM based on LAI and IF as the two canopy descriptors modelled the total backscatter with a significantly high coefficient of determination (R2 = 0.90 and 0.85, respectively) and RMSE of 1.18 and 1.25 dB, respectively. Subsequently, this set was used to retrieve the soil-corrected vegetation backscatter (σveg0) values. A comparative evaluation of the retrieval accuracy between plant parameters estimated from σtotal0 (σT_RHo, σT_RVo) and σveg0 (σV_RHo, σV_RVo) was performed using rigorously trained multi-layer perceptron (MLP) neural networks. The findings suggest that the prediction accuracy considerably improved when the backscatter of underlying/background soil cover was eliminated. The designed networks (with σtotal0 as input) retrieved plant water content and plant volume with the highest accuracy of 0.82 and 0.80, respectively while it increased dramatically to 0.87 and 0.89 when the inputs were substituted by σveg0. The present study is a first step towards retrieving crop parameters from hybrid-polarized data and thus possesses the potential to serve as a reference for further research initiatives.
Multi-sensor land data assimilation: Toward a robust global soil moisture and snow estimation Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-29 Long Zhao, Zong-Liang Yang
Global monitoring of soil moisture and snow is now available through various satellite observations from optical, microwave, and gravitational sensors. However, very few modeling frameworks exist that conjointly use the above sensors to produce mutually and physically consistent earth system records. To this goal, a prototype of multi-sensor land data assimilation system is developed by linking the Community Land Model version 4 (CLM4) and a series of forward models with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global soil moisture and snow by assimilating brightness temperature, snow cover fraction, and daily total water storage observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Moderate Resolution Imaging Spectroradiometer (MODIS), and Gravity Recovery and Climate Experiment (GRACE), respectively. A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to introduce ensemble spread in CLM4 land states and some methods are used to reduce the computational load. Data assimilation with different combinations of sensors is implemented for 2003–2009 to investigate individual contributions from different satellite observations. Evaluation results and cross-comparison of open-loop and data assimilation cases suggest that 1) assimilation of MODIS snow cover fraction slightly improves snow estimation in mid and high latitudes; 2) lower and higher frequencies of AMSR-E brightness temperature play complementary roles in improving global soil moisture and snow estimation; 3) assimilation of GRACE tends to degrade soil moisture estimation but poses potential in improving snow depth estimation in most high-latitude regions. Generally, the combination of MODIS, GRACE, and AMSR-E observations with regard to spatial locations holds promise to provide a robust global soil moisture and snow estimation through the multi-sensor land data assimilation system.
The correlation between GNSS-derived precipitable water vapor and sea surface temperature and its responses to El Niño–Southern Oscillation Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-28 Xiaoming Wang, Kefei Zhang, Suqin Wu, Zishen Li, Yingyan Cheng, Li Li, Hong Yuan
EI Niño–Southern Oscillation (ENSO) is a complex ocean-atmosphere interaction phenomenon occurring in nature that has a profound impact on global atmospheric circulation. As ENSO is a coupled ocean-atmosphere phenomenon, in addition to the commonly used sea surface temperature (SST), water vapor in the atmosphere can be used to monitor the evolution of ENSO and to investigate its consequences (e.g., droughts and flooding). The Global Navigation Satellite System (GNSS), in addition to its applications for positioning, timing, and navigation, is another established atmospheric observing system used to remotely sense precipitable water vapor (PWV) in the atmosphere. The accuracy of the GNSS-derived PWV measurements was assessed from 12 stations based on observations made at co-located radiosonde stations as a reference. The results show that mean values of the root-mean-square error (RMSE) and biases of 6-hourly GNSS-derived PWV derived from all 12 stations are valued at 1.48 mm and −0.30 mm, respectively. Regarding monthly means, mean values of the RMSE and biases of the GNSS-derived PWV are valued at 0.66 mm and −0.23 mm, respectively. The variability in PWV estimated from 56 GNSS stations positioned close to the sea indicates that it is significantly affected by ENSO events. Generally, a 1-K increase in SST will lead to an 11% increase in PWV across all of the stations. A case study conducted at the TOW2 station in Australia shows that the non-linear trend of the PWV depicts the evolution of two severe flood events and one severe drought event occurring in this region. Comparative results derived from TOW2 and from another 24 stations show a good agreement between PWV and total precipitation. These results suggest that GNSS-derived PWV together with other climatic variables (e.g., SST) can be used as an indication of the evolution of ENSO events and as a possible indicator of drought and flood occurrence.
Ground subsidence monitoring with SAR interferometry techniques in the rural area of Al Wagan, UAE Remote Sens. Environ. (IF 6.457) Pub Date : 2018-07-11 Nikolaos Liosis, Prashanth Reddy Marpu, Kosmas Pavlopoulos, Taha B.M.J. Ouarda
In this work, we investigate the past and present land deformation in Al Wagan area in the United Arab Emirates. The area is primarily an agricultural region where dependence on groundwater is documented. Such a reliance on ground water resources in a region which is characterized by very low precipitation can lead to significant land subsidence as was observed in this study which identified fast and localized deformation trends. The quantification of ground deformations of large magnitude and small amplitude in this area with SAR Interferometry is a challenging task using moderate resolution data due to the incoherent surface background. Even though SAR acquisitions were sparse over this region, the available ENVISAT, ALOS and Sentinel-1A imagery was analysed with differential interferometry and the Small Baseline Subset technique in order to provide estimates about the evolution of the deformation pattern in a limited area. A clear evidence of subsidence phenomena has been identified in the study area. During the period 2003–2010 the subsidence was estimated to reach 18 cm/year as observed in the DInSAR processing results of data from ENVISAT and ALOS Satellites. However it appears to be slightly more stable during the recent past (Dec/2016–March/2018) as observed in the results with recent Sentinel-1 data where a maximum localized subsidence in the order of 10 cm was estimated. The depletion of the aquifer resources which is confirmed from groundwater level data is speculated to be the most probable cause.
Generalized space-time classifiers for monitoring sugarcane areas in Brazil Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-29 Ana Cláudia dos Santos Luciano, Michelle Cristina Araújo Picoli, Jansle Vieira Rocha, Henrique Coutinho Junqueira Franco, Guilherme Martineli Sanches, Manoel Regis Lima Verde Leal, Guerric le Maire
Spatially and temporally accurate information on crop areas is a prerequisite for monitoring the multiannual dynamics of crop production. Satellite images have proven their high potential for mapping crop areas at large scales, even at the crop-species level, when a classifier is calibrated on the same image with reference data corresponding to the same period. For operational monitoring purposes, however, it is critical to develop generalized classification methodologies applicable to large scales and different years. Generalized classifiers were presented in this study as follows: a) simple cross-year calibration and application (M1); b) multiyear calibrations (M2); and c) map updating through change detection with multiyear calibrations (M3). These three methods were developed in a classical frame of object-based classifications for a time series of Landsat images with the Random Forest machine learning algorithm. Therein, we tested these methods for sugarcane classification in Sao Paulo state, Brazil, as sugarcane is an economically important crop that has developed substantially in the past decades. Eight years of sugarcane reference maps were used to calibrate and validate the classifiers at four different sites. The cross-year application of M1 provided a low average accuracy Dice coefficient (DC) of 0.84, while it was, on average, 0.94 for the classical same-year calibration. When the classifier was trained on a multiyear dataset (M2), the accuracies achieved average values of 0.91 in independent years. The map updating method M3 showed promising results but was not able to reach the accuracy of visual interpretation methods for detecting annual sugarcane land use change. The multiyear classifier M2 was applied to four contrasting sites and provided reliable results for new sites and years for sugarcane classification. Calibration of the machine learning algorithm on a multiyear dataset of standardized and gap-filled satellite images and reference data proved to give an accurate and space-time generalized classifier, reducing the time, cost and resources for mapping sugarcane areas at large scales.
A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-28 Yun Bai, Jiahua Zhang, Sha Zhang, Fengmei Yao, Vincenzo Magliulo
The temporal dynamics of optimum stomatal conductance (gsmax), as well differences between C3 and C4 crops, have rarely been considered in previous remote sensing (RS)-based Jarvis-type canopy conductance (Gc) models. To address this issue, a RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China. RST-Gc included restrictive functions for air temperature, vapor pressure deficit, and soil water deficit, and it used satellite-retrieved NDVI to formulate the temporal variation of gsmax defined at a photosynthetic photon flux density (PPFD) of 2000 μmol m−2 s−1 (gsm, 2000). Results showed that the parameters of RST-Gc differed between C3 and C4 crops. RST-Gc successfully simulated variations in Penman–Monteith (PM)-derived daytime Gc with R2 = 0.57 for both C3 and C4 crops. RST-Gc was incorporated into a revised evapotranspiration (ET) model and a new gross primary productivity (GPP) model. The two models were validated at 19 crop flux sites. Daily mean inputs were generally incorporated into a PM approach to model daily transpiration. This is inappropriate because available energy and stomatal conductance vary significantly on a diurnal basis, with both non-linearly regulating transpiration rate. The PM approach with daily mean inputs produced unreasonable transpiration rate estimates. Efforts were made in the revised ET model (denoted as RS-WBPM2), which was modified from the water balance based RS-PM (RS-WBPM) model of Bai et al. (2017), to address this issue by calculating transpiration using daytime inputs. The photosynthesis-based stomatal conductance model, developed by Ball et al. (1987a) and improved by Leuning (1995) (BBL model), was inverted to calculate GPP using canopy conductance; the inverted model was denoted as IBBL. Cross validation showed good agreement between flux tower measurements and modeled ET (R2 = 0.79, RMSE (root mean standard error) = 20.66 W m−2 for daily ET and R2 = 0.87, RMSE = 15.32 W m−2 for 16-day ET) and GPP (R2 = 0.83, RMSE = 2.49 gC m−2 d−1 for daily GPP and R2 = 0.86, RMSE = 1.96 gC m−2 d−1 for 16-day GPP) for the two models. Within-site validations demonstrated the successful performance of the two models at 18 sites (albeit with one outlier). Inter-site variations in ET and GPP were also successfully reproduced by the models. NDVI-derived gsm, 2000 outperformed the fixed gsm, 2000 in both ET and GPP estimates. The results imply that the RS-WBPM2 and IBBL models are useful tools for modeling regional and global ET and GPP.
Modeling biases in laser-altimetry measurements caused by scattering of green light in snow Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-27 Benjamin E. Smith, Alex Gardner, Adam Schneider, Mark Flanner
Laser altimetry offers the potential to monitor ice-sheet elevation changes with millimeter accuracy. While previous missions have used infrared lasers to make these measurements, NASA's upcoming ICESat (Ice, Cloud, and land Elevation Satellite)-2 mission will use a green laser. Because ice absorbs green light very weakly, in the absence of light-absorbing impurities, green photons can scatter off many snow grains before returning to the surface, delaying the return pulse and leading to an apparent downward shift in the snow surface. In this paper, we explore the effects of snow-grain size and impurity content on these measurements, and investigate strategies that might help minimize the biases they introduce. We find that an uninformed choice of measurement parameters (a windowed mean including a large range of photons around the surface) can result in >0.45 m of apparent surface-height variation between large and small grain sizes. Other choices of measurement parameters, such as a windowed median, can reduce this uncertainty by a factor of two to three. In addition, measurements of surface reflectance at green and infrared wavelengths, and interpretation of return-pulse shapes may be used to estimate and correct for these biases.
Evaluating the effects of surface properties on methane retrievals using a synthetic airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG) image Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-27 Alana K. Ayasse, Andrew K. Thorpe, Dar A. Roberts, Christopher C. Funk, Philip E. Dennison, Christian Frankenberg, Andrea Steffke, Andrew D. Aubrey
Atmospheric methane has been increasing since the beginning of the industrial era due to anthropogenic emissions. Methane has many sources, both natural and anthropogenic, and there continues to be considerable uncertainty regarding the contribution of each source to the total methane budget. Thus, remote sensing techniques for monitoring and measuring methane emissions are of increasing interest. Recently, the Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) has proven to be a valuable instrument for quantitative mapping of methane plumes. Despite this success, uncertainties remain regarding the sensitivity of the retrieval algorithms, including the influence of albedo and the impact of surfaces that may cause spurious signals. To explore these sensitivities, we applied the Iterative Maximum a Posterior Differential Optical Absorption Spectroscopy (IMAP-DOAS) methane retrieval algorithm to synthetic reflected radiances with variable methane concentrations, albedo, surface cover, and aerosols. This allowed for characterizing retrieval performance, including potential sensitivity to variable surfaces, low albedo surfaces, and surfaces known to cause spurious signals. We found that dark surfaces (below 0.10 μWcm−2nm−1sr−1 at 2139 nm), such as water and green vegetation, and materials with absorption features in the 2200–2400 nm range caused higher errors in retrieval results. We also found that aerosols have little influence on retrievals in the SWIR. Results from the synthetic scene are consistent with those observed in IMAP-DOAS retrievals for real AVIRIS-NG scenes containing methane plumes from a waste dairy lagoon and coal mine ventilation shafts. Understanding the effect of surface properties on methane retrievals is important given the increased use of AVIRIS-NG to map gas plumes from a diversity of sources over variable landscapes.
The influence of spatial resolution on the angular variation patterns of optical reflectance as retrieved from MODIS and POLDER measurements Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-27 Ziti Jiao, Xiaoning Zhang, Francois-Marie Bréon, Yadong Dong, Crystal B. Schaaf, Miguel Román, Zhuosen Wang, Lei Cui, Siyang Yin, Anxin Ding, Jindi Wang
Multiangle remote sensing plays a central role in the development of algorithms for the retrieval of various surface biophysical parameters that are influenced by the reflectance anisotropy. Surface reflectance anisotropy is characterized by the Bidirectional Reflectance Distribution Function (BRDF). Within the past decade, space-borne multiangle observations acquired by the MODerate resolution Imaging Spectroradiometer (MODIS) sensor (which has a gridded spatial resolution of 500 m) and by the POLarization and Directionality of Earth Reflectances (POLDER) sensor (which has a spatial resolution of 6 × 7 km) have been used for a wide variety of global applications. However, it is necessary to fully understand the variability inherent in the surface BRDF information as retrieved from MODIS and POLDER at these two spatial resolutions to optimize their use. In this study, we make use of extensive POLDER Bidirectional Reflectance Factors (BRFs) selected from the entire archive of the POLDER BRDF database and standard MODIS BRDF parameter products (MCD43A1, Collection V005) that were geolocated within the same spatial extents as the POLDER data. The variability in surface BRDF is characterized by investigation of three BRDF model parameters as retrieved from MODIS and POLDER and a comprehensive index indicating the variations in the primary dome-bowl BRDF patterns (the anisotropic flat index (AFX)). The principal information content contained in these BRDF data is characterized by the general BRDF shapes (the BRDF archetypes) that account for >90% of the total variance in these BRDF data. A hotspot-revised BRDF model is used directly on top of the retrieved model BRDF parameters to capture the hotspot effect associated with these BRDF parameters. The main findings of this study show that the variability in surface BRDF, as extracted from the MODIS and POLDER datasets, shares six reciprocal BRDF archetypes. However, the 500-m MODIS BRDF data can uniquely capture some additional extreme BRDF shapes mainly due to the data's finer pixel scales. These original findings are very important, because subsequent albedo retrievals can be significantly impacted by the use of BRDFs of different resolutions. This study provides evidence concerning the influence of spatial resolution on angular variation patterns of optical reflectance as retrieved from the MODIS and POLDER BRDF products.
Estimators and confidence intervals for plant area density at voxel scale with T-LiDAR Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-27 François Pimont, Denis Allard, Maxime Soma, Jean-Luc Dupuy
Estimating leaf and plant area density with Terrestrial Laser Scanners (TLS) continues to be more and more popular, as tridimensional point clouds appear as an appealing measurement technique for heterogeneous environments. Some approaches implement a discretization of the point cloud in a grid (referred to as “voxel-based”) to account for this vegetation heterogeneity and significant work has been done in this recent research field, but no general theoretical analysis is available. Although estimators have been proposed and several causes of biases have been identified, their unbiasedness (zero bias) and efficiency (smallest error) have not been evaluated. Also, confidence intervals are almost never provided. In the present paper, we assumed that the vegetation elements were randomly distributed within voxels and that TLS beams were infinitely thin, in order to focus on the remaining sources of biases and errors. In this simplified context, we both solve the transmittance equation and use the Maximum Likelihood Estimation (MLE), to derive some new estimators for the attenuation coefficient, which is proportional to leaf area density at voxel scale in this idealized context. These estimators include bias corrections and confidence intervals, and account for the number of beams crossing the voxel (beam number), the inequality of path lengths in voxel, the size of vegetation elements, as well as for the variability of element positions between vegetation samples. These theoretical derivations are complemented by numerous numerical simulations for the evaluation of estimator bias and efficiency, as well as the assessment of the coverage probabilities of confidence intervals. Our simulations reveal that the usual estimators are biased and exhibit 95% confidence intervals on the order of ±100% of the estimate, when the beam number is smaller than 30. Second, our bias-corrected estimators -especially the bias-corrected MLE- are truly unbiased and efficient in a wider range of validity than the usual ones, even for beam number as low as 5. Third, we found that the confidence intervals can be as high as ≈ ± 50% when the projected area of a single element was on the order of 10% of voxel cross-sectional area and vegetation was dense (optical depth of the voxel equal to 2), even for a beam number larger than 1000. This is explained by the variability of element positions between vegetation samples, which implies that a significant part of residual error is caused by random effects. When LAD estimates are averaged over several small voxels -typically to determine a vertical profile at plot scale or to compute the total leaf area of a single plant-, confidence intervals are typically on the order of ±5 to 10% with bias-corrected estimators, which is twice as small as with usual estimators. Our study provides some new ready-to-use estimators and confidence intervals for attenuation coefficients, which are unbiased and efficient within a fairly large range of parameter values. The unbiasedness is achieved for a fairly low beam number, which is promising for application to airborne LiDAR data. They permit to raise the level of understanding and confidence on LAD estimation. Among other applications, their usage should help determine the most suitable voxel size, for given vegetation types and scanning density, whereas existing guidelines are highly variable among studies, probably because of differences in vegetation, scanning design and estimators. The impact of other sources of biases and errors, such as vegetation heterogeneity inside voxels or TLS specifications are not addressed in the present manuscript and would require further investigations.
Quantifying understory vegetation density using small-footprint airborne lidar Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-24 Michael J. Campbell, Philip E. Dennison, Andrew T. Hudak, Lucy M. Parham, Bret W. Butler
The ability to quantify understory vegetation structure in forested environments on a broad scale has the potential to greatly improve our understanding of wildlife habitats, nutrient cycling, wildland fire behavior, and wildland firefighter safety. Lidar data can be used to model understory vegetation density, but the accuracy of these models is impacted by factors such as the specific lidar metrics used as independent variables, overstory conditions such as density and height, and lidar pulse density. Few previous studies have examined how these factors affect estimation of understory density. In this study we compare two widely-used lidar-derived metrics, overall relative point density (ORD) and normalized relative point density (NRD) in an understory vertical stratum, for their respective abilities to accurately model understory vegetation density. We also use a bootstrapping analysis to examine how lidar pulse density, overstory vegetation density, and canopy height can affect the ability to characterize understory conditions. In doing so, we present a novel application of an automated field photo-based understory cover estimation technique as reference data for comparison to lidar. Our results highlight that NRD is a far superior metric for characterizing understory density than ORD (R2NRD = 0.44 vs. R2ORD = 0.14). In addition, we found that pulse density had the strongest positive effect on predictive power, suggesting that as pulse density increases, the ability to accurately characterize understory density using lidar increases. Overstory density and canopy height had nearly identical negative effects on predictive power, suggesting that shorter, sparser canopies improve lidar's ability to analyze the understory. Our study highlights important considerations and limitations for future studies attempting to use lidar to quantify understory vegetation structure.
Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-26 Antara Dasgupta, Stefania Grimaldi, R.A.A.J. Ramsankaran, Valentijn R.N. Pauwels, Jeffrey P. Walker
Synthetic Aperture Radar (SAR) data are currently the most reliable resource for flood monitoring, though still subject to various uncertainties, which can be objectively represented with probabilistic flood maps. Moreover, the growing number of SAR satellites has increased the likelihood of observing a flood event from space through at least a single SAR image, but generalized methods for flood classification independent of sensor characteristics need to be developed, to fully utilize these images for disaster management. Consequently, a neuro-fuzzy flood mapping technique is proposed for texture-enhanced single SAR images. Accordingly, any SAR image is first processed to generate second-order statistical textures, which are subsequently optimized using a dimensionality reduction technique. The flood and non-flood classes are then modelled within a fuzzy inference system using Gaussian curves. Parameterization is achieved by training a neural network on the image through user-defined polygons. The results of the optimized texture-based neuro-fuzzy classification were compared against the performance of the SAR image alone and that of SAR enhanced with randomly selected texture features. This approach was tested for a COSMO-SkyMed SAR image at two validation sites, for which high resolution aerial photographs were available. An overall accuracy assessment using reliability diagrams demonstrated a reduction of 54.2% in the Weighted Root Mean Squared Error (WRMSE) values compared to the stand-alone use of SAR. WRMSE values estimated for the proposed method varied from 0.027 to 0.196. A fuzzy validation exercise was also proposed to account for the uncertainty in manual flood identification from aerial photography, resulting in fuzzy spatial similarity values ranging from 0.67 to 0.92, with higher values representing better performance. Results suggest that the proposed approach has demonstrated potential to improve operational SAR-based flood mapping.
A new source of multi-spectral high spatial resolution night-time light imagery—JL1-3B Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-26 Qiming Zheng, Qihao Weng, Lingyan Huang, Ke Wang, Jinsong Deng, Ruowei Jiang, Ziran Ye, Muye Gan
Artificial light at night (ALAN) provides a unique footprint of human activities and settlements. However, the adverse effects of ALAN on human health and ecosystems have not been well understood. Because of a lack of high resolution data, studies of ALAN in China have been confined to coarse resolution, and fine-scale details are missing. The fine details of ALAN are pertinent, because the highly dense population in Chinese cities has created a distinctive urban lighting pattern. In this paper, we introduced a new generation of high spatial resolution and multi-spectral night-time light imagery from the satellite JL1-3B. We examined its effectiveness for monitoring the spatial pattern and discriminating the types of artificial light based on a case study of Hangzhou, China. Specifically, local Moran's I analysis was applied to identify artificial light hotspots. Then, we analyzed the relationship between artificial light brightness and land uses at the parcel-level, which were generated from GF-2 imagery and open social datasets. Third, a machine learning based method was proposed to discriminate the type of lighting sources – between high pressure sodium lamps (HPS) and light-emitting diode lamps (LED) – by incorporating their spectral information and morphology feature. The result shows a complicated heterogeneity of illumination characteristics across different land uses, where main roads, commercial and institutional areas were brightly lit while residential area, industrial area and agricultural land were dark at night. It further shows that the proposed method was effective at separating light emitted by HPS and LED, with an overall accuracy and kappa coefficient of 83.86% and 0.67, respectively. This study demonstrates the effectiveness of JL1-3B and its superiority over previous night-time light data in detecting details of lighting objects and the nightscape pattern, and suggests that JL1-3B and alike could open up new opportunities for the advancement of night-time remote sensing.
Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-26 Xiaona Chen, Di Long, Shunlin Liang, Lian He, Chao Zeng, Xiaohua Hao, Yang Hong
Snow cover condition across the Tibetan Plateau (TP) is not only a significant indicator of climate change but also a vital variable in water availability because of its water storage function in high-mountain regions of Southwest China and the surrounding Asian countries. Limited by low spatial resolution, incomplete spatial coverage, and short time span of the current snow cover products, the long-term snow cover change across the TP under the climate change background remains unclear. To resolve this issue, a composite long-term gap-filled TP daily 5-km snow cover extent (SCE) record (TPSCE) is generated by integrating SCE from the Advanced Very High-Resolution Radiometer (AVHRR) surface reflectance climate data record (CDR) and several existing snow cover data sets, with the help of a decision tree snow cover mapping algorithm, for the period 1981–2016. A snow discrimination process was used to classify the land surface into snow (pre-TPSCE) and non-snow using AVHRR surface reflectance CDR. To fill gaps caused by invalid observations and cloud contamination in pre-TPSCE, several existing daily SCE products, including MOD10C1, MYD10C1, IMS, JASMES, and a passive microwave snow depth data set are employed in the composition process. The daily snow discrimination accuracy, tested by ground snow-depth observations during 2000–2014, shows that the TPSCE captures the distribution of snow duration days (R2 = 0.80, bias = 3.93 days) effectively. The comparison between the TPSCE and fine-resolution snow cover maps (MCD10A1-TP) indicates high comparability between the TPSCE and MCD10A1-TP. In addition, cross-comparisons with changes in temperature, precipitation, and land surface albedo indicate that the TPSCE is reliable in climate change studies. In summary, the TPSCE is spatially complete and covers the longest period among all current snow cover products from satellite observations. The TPSCE seamlessly records changes in snow cover across the TP over the past 36 years, thereby providing valuable snow information for climate change and hydrological studies.
A novel method to obtain three-dimensional urban surface temperature from ground-based thermography Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-19 William Morrison, Simone Kotthaus, C.S.B. Grimmond, Atsushi Inagaki, Tiangang Yin, Jean-Philippe Gastellu-Etchegorry, Manabu Kanda, Christopher J. Merchant
Urban geometry and materials combine to create complex spatial, temporal and directional patterns of longwave infrared (LWIR) radiation. Effective anisotropy (or directional variability) of thermal radiance causes remote sensing (RS) derived urban surface temperatures to vary with RS view angles. Here a new and novel method to resolve effective thermal anisotropy processes from LWIR camera observations is demonstrated at the Comprehensive Outdoor Scale MOdel (COSMO) test site. Pixel-level differences of brightness temperatures reach 18.4 K within one hour of a 24-h study period. To understand this variability, the orientation and shadowing of surfaces is explored using the Discrete Anisotropic Radiative Transfer (DART) model and Blender three-dimensional (3D) rendering software. Observed pixels and the entire canopy surface are classified in terms of surface orientation and illumination. To assess the variability of exitant longwave radiation (MLW) from the 3D COSMO surface ( M LW 3 D ), the observations are prescribed based on class. The parameterisation is tested by simulating thermal images using a camera view model to determine camera perspectives of M LW 3 D fluxes. The mean brightness temperature differences per image (simulated and observed) are within 0.65 K throughout a 24-h period. Pixel-level comparisons are possible with the high spatial resolution of M LW 3 D and DART camera view simulations. At this spatial scale (<0.10 m), shadow hysteresis, surface sky view factor and building edge effects are not completely resolved by M LW 3 D . By simulating apparent brightness temperatures from multiple view directions, effective thermal anisotropy of M LW 3 D is shown to be up to 6.18 K. The developed methods can be extended to resolve some of the identified sources of sub-facet variability in realistic urban settings. The extension of DART to the interpretation of ground-based RS is shown to be promising.
Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-19 Jian Peng, Jinglei Jia, Yanxu Liu, Huilei Li, Jiansheng Wu
Urban heat island (UHI) has become an urban eco-environmental problem globally. Land surface temperature (LST) is widely used to quantify UHI. This study used Shenzhen, a southern coastal city in China, as an example to explore the relationship between spatial variation of LST in different seasons and the influencing factors in five dimensions, integrating the methods of ordinary least-squares regression, stepwise regression, all-subsets regression, and hierarchical partitioning analysis. The results showed that the most important factor affecting spatial heterogeneity of LST in summer was the normalized difference build-up index (53.62%, for contributing rate), whereas in the transition season the most important factor was the normalized difference vegetation index (NDVI) (47.84%). In winter the construction land percentage and NDVI (26.84% and 25.56%, respectively) were the most influential. Artificial surface and green space had a dominant effect on LST spatial differentiation. Landscape configuration and diversity were not the dominant influencing factors in summer or in the transition season. Furthermore, the independent contribution rate of the Shannon diversity index (SHDI) reached 8.79% in the transition season, while in winter, the independent contribution rates of SHDI and the landscape shape index were 8.52% and 3.45%, respectively. The influence of landscape diversity and configuration factors tended to increase as LST reduced, while the contribution rate of the important factors such as artificial surface and green space decreased significantly. These relationships indicate that the influence of landscape configuration and diversity factors on LST is relatively weak, and can be easily concealed by the influence of landscape components, especially when the spatial variation of LST is not strong. These findings can help to develop UHI adaptation strategies based on local conditions.
The influence of snow microstructure on dual-frequency radar measurements in a tundra environment Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-18 Joshua King, Chris Derksen, Peter Toose, Alexandre Langlois, Chris Larsen, Juha Lemmetyinen, Phil Marsh, Benoit Montpetit, Alexandre Roy, Nick Rutter, Matthew Sturm
Recent advancement in the understanding of snow-microwave interactions has helped to isolate the considerable potential for radar-based retrieval of snow water equivalent (SWE). There are however, few datasets available to address spatial uncertainties, such as the influence of snow microstructure, at scales relevant to space-borne application. In this study we introduce measurements from SnowSAR, an airborne, dual-frequency (9.6 and 17.2 GHz) synthetic aperture radar (SAR), to evaluate high resolution (10 m) backscatter within a snow-covered tundra basin. Coincident in situ surveys at two sites characterize a generally thin snowpack (50 cm) interspersed with deeper drift features. Structure of the snowpack is found to be predominantly wind slab (65%) with smaller proportions of depth hoar underlain (35%). Objective estimates of snow microstructure (exponential correlation length; lex), show the slab layers to be 2.8 times smaller than the basal depth hoar. In situ measurements are used to parametrize the Microwave Emission Model of Layered Snowpacks (MEMLS3&a) and compare against collocated SnowSAR backscatter. The evaluation shows a scaling factor (ϕ) between 1.37 and 1.08, when applied to input of lex, minimizes MEMLS root mean squared error to <1.1 dB. Model sensitivity experiments demonstrate contrasting contributions from wind slab and depth hoar components, where wind rounded microstructures are identified as a strong control on observed backscatter. Weak sensitivity of SnowSAR to spatial variations in SWE is explained by the smaller contributing microstructures of the wind slab.
On the determination of global ocean wind and wave climate from satellite observations Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 I.R. Young, M.A. Donelan
Three extensive global wind speed and wave height datasets (altimeter, radiometer, model reanalysis) are analysed to investigate the global wind speed and wave height climate. Despite the fact that these datasets have all been carefully calibrated, they show systematic differences in wind speed. At high latitudes both altimeter and radiometer winds are biased high compared to buoy measurements. Altimeter winds are more impacted than radiometer winds. Based on the assumptions that altimeter winds respond primarily to the surface wave spectrum mean squared slope and radiometer winds respond primarily to the surface wave spectrum dissipation, it is shown that the observed differences are a result of changes in atmospheric stability. An analysis which accounts for differences in air and water temperatures describes the observed differences with surprising accuracy. Based on this analysis corrections to both altimeter and radiometer winds are proposed which account for the influence of atmospheric stability. It is also shown that satellites preferentially measure at particular local times of day. As winds have a diurnal variation in magnitude, this preferential measurement time can also bias statistical values obtained from such satellite systems.
An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 M.E. Smith, L. Robertson Lain, S. Bernard
Productive upwelling zones such as the southern Benguela can exhibit phytoplankton biomass variability over several orders of magnitude, from near oligotrophic offshore waters to hypertrophic inshore blooms of >100 mg m −3. This introduces complexity for ocean colour applications such as Harmful Algal Bloom (HAB) monitoring. As low and high biomass algorithmic approaches for ocean colour differ, no single algorithm can optimally retrieve accurate Chl a over such a wide range of biomass. We propose a novel technique to apply and blend two different Chl a algorithms — an empirical blue-green algorithm for low to moderate biomass and a red-NIR band-ratio algorithm for moderate to high biomass. The blending method is based on the 708 and 665 nm reflectance wavelength ratio, where the blue-green algorithm is applied when the ρw(708)/ρw(665) ratio is <0.75, the red-NIR algorithm is applied >1.15, whilst the two are blended using a weighted approach in between these values. When applied to in situ and satellite match-up data this method provides a median absolute relative difference (MARD) of 37.9 and 45.7%, respectively, and a RMSD of 0.27 and 0.35 respectively, over Chl a concentrations spanning three orders of magnitude. Application is demonstrated for both MERIS and OLCI sensors, providing a smooth transition between different biomass levels and algorithm Chl a returns.
A regime-dependent retrieval algorithm for near-surface air temperature and specific humidity from multi-microwave sensors Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 Lisan Yu, Xiangze Jin
Near-surface specific humidity (qa) and air temperature (Ta) over the global ocean are important meteorological variables, but they cannot be retrieved directly from remote sensing. Many efforts have been made to develop algorithms that derive qa and Ta from multisensor microwave and/or infrared observations using in situ measurements as training datasets. However, uncertainty remains large in the resultant qa and Ta retrievals. In this study, 147 moored surface buoys are used to examine how buoy measured qa and Ta are related to satellite microwave brightness temperature (Tb) on the spatial scale from the warm/humid tropics to the cold/dry high latitudes. It is found that the Tb – qa and Tb – Ta relations are structured along two distinct, near-linear bands, with the primary band in the warm/humid regime and a secondary (weaker) band in the cold/dry regime. The step-like transition (or separation) between the two regimes occurs at 8–10 g kg−1 for qa and 14–17 °C for Ta. The evidence suggests that one algorithm may not be sufficient to extract qa and Ta from Tb in all regimes. Therefore, a high-latitude enhancement is added to the global algorithm so that the qa and Ta retrievals in the dry/cold regime can be specifically addressed. The new algorithms are applied to 11 microwave sensors, including SSM/I, SSMIS, and AMSU-A, from 1988 to 2016. Based on the 475,717 buoy collocations during the 29-year period, the retrieved qa and Ta have root-mean-square differences of 0.82 g kg−1 and 0.51 °C, respectively.
PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-19 Gaofei Yin, Ainong Li, Shengbiao Wu, Weiliang Fan, Yelu Zeng, Kai Yan, Baodong Xu, Jing Li, Qinhuo Liu
Rugged terrain distorts optical remote sensing signals, and land-cover classification and biophysical parameter retrieval over mountainous regions must account for topographic effects. Therefore, topographic correction is a prerequisite for many remote sensing applications. In this study, we proposed a semi-physically based and simple topographic correction method for vegetation canopies based on path length correction (PLC). The PLC method was derived from the solution to the classic radiative transfer equation, and the influence of terrain on the radiative transfer process within the canopy is explicitly considered, making PLC physically sound. The radiative transfer equation was simplified to make PLC mathematically simple. Near-nadir observations derived from a Landsat 8 Operational Land Imager (OLI) image covering a mountainous region and wide field-of-view observations derived from simulation using a canopy reflectance model were combined to test the PLC correction method. Multi-criteria were used to provide objective evaluation results. The performances were compared to that of five other methods: CC, SCS + C, and SE, which are empirical parameter-based methods, and SCS and D-S, which are semi-physical methods without empirical parameter. All the six methods could significantly reduce the topographic effects. However, SCS showed obvious overcorrection for near-nadir observations. The correction results from D-S showed an obvious positive bias. For near-nadir observations, the performance of PLC was comparable to the well-validated parameter-based methods. For wide field-of-view observations, PLC obviously outperformed all other methods. Because of the physical soundness and mathematical simplicity, PLC provides an efficient approach to correct the terrain-induced canopy BRDF distortion and will facilitate the exploitation of multi-angular information for biophysical parameter retrieval over mountainous regions.
Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-19 Ran Meng, Philip E. Dennison, Feng Zhao, Iurii Shendryk, Amanda Rickert, Ryan P. Hanavan, Bruce D. Cook, Shawn P. Serbin
Defoliation by herbivorous insects is a widespread forest disturbance driver, affecting global forest health and ecosystem dynamics. Compared with time- and labor-intensive field surveys, remote sensing provides the only realistic approach to mapping canopy defoliation by herbivorous insects over large spatial and temporal scales. However, the spectral and structural signatures of defoliation by insects at the individual tree level have not been well studied. Additionally, the predictive power of spectral and structural metrics for mapping canopy defoliation has seldom been compared. These critical knowledge gaps prevent us from consistently detecting and mapping canopy defoliation by herbivorous insects across multiple scales. During the peak of a gypsy moth outbreak in Long Island, New York in summer 2016, we leveraged bi-temporal airborne imaging spectroscopy (IS, i.e., hyperspectral imaging) and LiDAR measurements at 1 m spatial resolution to explore the spectral and structural signatures of canopy defoliation in a mixed oak-pine forest. We determined that red edge and near-infrared spectral regions within the IS data were most sensitive to crown-scale defoliation severity. LiDAR measurements including B70 (i.e., 70th bincentile height), intensity skewness, and kurtosis were effectively able to detect structural changes caused by herbivorous insects. In addition to canopy leaf loss, increased exposure of understory and non-photosynthetic materials contributed to the detected spectral and structural signatures. Comparing the ability of individual sensors to map canopy defoliation, the LiDAR-only Ordinary Least-Square (OLS) model performed better than the IS-only model (Adj. R-squared = 0.77, RMSE = 15.37% vs. Adj. R-squared = 0.63, RMSE = 19.11%). The IS + LiDAR model improved on performance of the individual sensors (Adj. R-squared = 0.81, RMSE = 14.46%). Our study improves our understanding of spectral and structural signatures of defoliation by herbivorous insects and presents a novel approach for mapping insect defoliation at the individual tree level. Additionally, with the current and next generation of spaceborne sensors (e.g., WorldView-3, Landsat, Sentinel-2, HyspIRI, and GEDI), higher accuracy and frequent monitoring of insect defoliation may become more feasible across a range of spatial scales, which are critical for ecological research and management of forest resources including the economic consequences of forest insect infestations (e.g., reduced growth and increased mortality), as well as for informing and testing of carbon cycle models.
Imaging spectrometer emulates Landsat: A case study with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Operational Land Imager (OLI) data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 Felix C. Seidel, E. Natasha Stavros, Morgan L. Cable, Robert Green, Anthony Freeman
Remote sensing data are most useful if they are available with sufficient precision, accuracy, spatiotemporal and spectral sampling, as well as continuity across decades. The Landsat and Sentinel series, as well other satellites are currently covering significant parts of this observational trade space. It can be expected that growing demands and budget constraints will require new capabilities in orbit that can address as many observables as possible with a single instrument. Recent optical performance improvements of imaging spectrometers make them true alternatives to traditional multispectral imagers. However, they are much more adaptable to a wide range of Earth observation needs due to the combination of continuous high spectral sampling with spatial sampling consistent with previous sensors (e.g., Landsat). Unfortunately, there is a knowledge gap in demonstrating that imaging spectroscopy data can substitute for multi-spectral data while sustaining the long-term record. Thus, the objective of this analysis is to test the hypothesis that imaging spectroscopy data compare radiometrically with multi-spectral data to within 5%. Using a coincident Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flight with over-passing Operational Land Imager (OLI) data on Landsat 8, we document a procedure for simulating OLI multi-spectral bands from AVIRIS data, evaluate influencing factors on the observed radiance, and assess the difference in top-of-atmosphere radiance as compared to OLI. The procedure for simulating OLI data include spectral convolution, accounting for the minimal atmospheric effects between the two sensors, and spatial resampling. The remaining differences between the simulated and the real OLI data result mainly from differences in sensor calibration, surface bi-directional reflectance, and spatial sampling. The median relative radiometric difference for each band ranges from −8.3% to 0.6%. After bias-correction to minimize potential calibration discrepancies, we find no more than a 1.2% relative difference. This analysis therefore successfully demonstrates that imaging spectrometer data can contribute to Landsat-type or other multi-spectral data records. It also shows that cross-calibration from a spectrometer to a radiometer can be easily performed as a result of the imaging spectrometer high spectral sampling and its ability to recreate multi-spectral response functions.
Taxonomic, functional, and phylogenetic diversity of bird assemblages are oppositely associated to productivity and heterogeneity in temperate forests Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 Soyeon Bae, Jörg Müller, Dowon Lee, Kerri T. Vierling, Jody C. Vogeler, Lee A. Vierling, Andrew T. Hudak, Hooman Latifi, Simon Thorn
Conserving multiple facets of biodiversity is important for sustaining ecosystems. However, understanding relationships between faunal diversity and measurable ecosystem quantities, such as heterogeneity and productivity, across continental scales can be complicated by disparate methods. We developed standardized approaches using lidar data and spectral greenness data (via NDVI; Normalized Difference Vegetation Index) from 637 sampling plots across four sites in North America, Europe, and Asia to test the local effects of habitat heterogeneity and productivity on taxonomic, functional, and phylogenetic diversity of breeding bird assemblages using boosted generalized additive models. Our results revealed the 3-D (three dimensional) vegetation structure (horizontal and vertical) to be of similar importance as NDVI in multiple biodiversity measures, and the importance of 3-D structure was higher for functional and phylogenetic biodiversity measures than for taxonomic measures. We found congruent responses between functional and phylogenetic diversity; however, patterns of taxonomic diversity differed from those of functional/phylogenetic diversity for most predictors. For example, NDVI had positive relationships with taxonomic diversity, but negative relationships with functional/phylogenetic diversity. The effect of canopy density on taxonomic diversity was generally bell-shaped, whereas the relationship was U-shaped for functional and phylogenetic diversity. As a result, this study supports a silviculture strategy with a high variety of canopy densities and vertical variabilities across forest stands to create maximum benefits for regional biodiversity. Here, early succession stands and closed stands sustain functionally-rich bird assemblages, while stands with a medium canopy density promote species-rich assemblages.
The retrieval of ice cloud parameters from multi-spectral satellite observations of reflectance using a modified XBAER algorithm Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 Linlu Mei, Vladimir Rozanov, Marco Vountas, John P. Burrows
Retrieval of ice cloud properties from passive remote satellite instruments using multispectral observations of the upwelling radiance at the top of atmosphere is challenging. This requires an accurate separation of the surface spectral reflectance below the cloud and that coming from the ice cloud particle. In this paper, a weighting-function based method is described, which retrieves ice cloud optical thickness (COT) and ice cloud crystal effective radius (CER). The retrieval algorithm belongs to the newly developed eXtensible Bremen Aerosol Retrieval (XBAER) algorithm family, and is called XBAER-ice. The XBAER-ice retrieval uses a surface reflectance parameterization minimizing the impact of surface scattering on the retrieval of optically thin ice cloud properties. A fractal model is used to describe the ice cloud optical properties. The comparison of COT and CER between XBAER-ice and latest Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products shows good agreement. We note that ice cloud property retrievals are particularly sensitive to assumptions about the ice particle shape. The comparison between satellite derived COTs using both the MODIS C6 product and XBAER-ice algorithm agree well with Atmospheric Radiation Measurement (ARM) in-situ measurements (with correlation coefficient above 0.9) whereas CERs show poorer agreement (~0.6). The global spatial distribution patterns and values of COT from ice clouds retrieved by using the MODIS C6 and XBAER-ice algorithms show a high degree of consistency. The XBAER-ice derived ice cloud CER values are systematically smaller than the MODIS C6 product, but both products have similar spatial distribution patterns. The XBAER-ice offers a potential alternative algorithm for the synergistic operational retrieval of ice cloud properties from the observations of Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3.
Spatial downscaling of TRMM precipitation data considering the impacts of macro-geographical factors and local elevation in the Three-River Headwaters Region Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 Tao Zhang, Baolin Li, Yecheng Yuan, Xizhang Gao, Qingling Sun, Lili Xu, Yuhao Jiang
Precipitation products with high spatial resolution are important for basin-scale hydrological and meteorological applications. Downscaling techniques commonly used with satellite-derived rainfall data build statistical regression relationships between the precipitation and land surface characteristics to obtain rainfall estimates with improved spatial resolution. However, these relationships tend to be extended mistakenly from the regional scale to the hill slope scale. This paper introduces a quadratic parabolic profile (QPP) model for downscaling precipitation. The proposed technique uses a quadratic parabolic equation to express the rule for changes of precipitation with elevation. It is assumed that precipitation is the primary factor restricting vegetation growth during the growing season. Therefore, an ordinary least square regression method is used to fit an “elevation–normalized difference vegetation index (NDVI)” function to determine the parameters of the QPP model. This method was implemented in the Three-River Headwaters Region (TRHR) during the growing seasons of 2009–2013 for both monthly and total precipitation. The results indicated that the precipitation estimates downscaled using the QPP method had higher accuracies than those of commonly used exponential regression, multiple linear regression, and geographically weighted regression models. The average root mean square errors (RMSEs) and mean absolute percent errors (MAPEs) of total precipitation during the growing season of the commonly used models were 17%–69% and 17%–92% higher, respectively, than those of the QPP model. Meanwhile, the precipitation downscaled using the QPP technique also had lower MAPEs and RMSEs than the PERSIANN-CCS, PERSIANN-CDR, GSMaP-RNL, and GSMaP-RNLG products. Downscaled precipitation estimates from the QPP model exhibited patterns with elevation that were more detailed and more reliable than from the commonly used downscaling methods and another four satellite products. In addition, the QPP model is insensitive to errors in the NDVI or elevation. These findings suggest the proposed approach could be implemented successfully to downscale both monthly and total precipitation of the Tropical Rainfall Measuring Mission (TRMM) 3B43 product throughout the growing season in the TRHR.
Exploring the physiological information of Sun-induced chlorophyll fluorescence through radiative transfer model inversion Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-15 Marco Celesti, Christiaan van der Tol, Sergio Cogliati, Cinzia Panigada, Peiqi Yang, Francisco Pinto, Uwe Rascher, Franco Miglietta, Roberto Colombo, Micol Rossini
A novel approach to characterize the physiological conditions of plants from hyperspectral remote sensing data through the numerical inversion of a light version of the SCOPE model is proposed. The combined retrieval of vegetation biochemical and biophysical parameters and Sun-induced chlorophyll fluorescence (F) was investigated exploiting high resolution spectral measurements in the visible and near-infrared spectral regions. First, the retrieval scheme was evaluated against a synthetic dataset. Then, it was applied to very high resolution (sub-nanometer) canopy level spectral measurements collected over a lawn treated with different doses of a herbicide (Chlorotoluron) known to instantaneously inhibit both Photochemical and Non-Photochemical Quenching (PQ and NPQ, respectively). For the first time the full spectrum of canopy F, the fluorescence quantum yield (ΦF), as well as the main vegetation parameters that control light absorption and reabsorption, were retrieved concurrently using canopy-level high resolution apparent reflectance (ρ*) spectra. The effects of pigment content, leaf/canopy structural properties and physiology were effectively discriminated. Their combined observation over time led to the recognition of dynamic patterns of stress adaptation and stress recovery. As a reference, F values obtained with the model inversion were compared to those retrieved with state of the art Spectral Fitting Methods (SFM) and SpecFit retrieval algorithms applied on field data. ΦF retrieved from ρ* was eventually compared with an independent biophysical model of photosynthesis and fluorescence. These results foster the use of repeated hyperspectral remote sensing observations together with radiative transfer and biochemical models for plant status monitoring.
Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-07 Brian L. Collister, Richard C. Zimmerman, Charles I. Sukenik, Victoria J. Hill, William M. Balch
Passive ocean color remote sensing has revolutionized our ability to quantify the horizontal distribution of phytoplankton across the ocean surface. Lidar technology can provide remotely sensed estimates of the vertical distribution of optical properties and suspended particles in natural waters, significantly improving our ability to model upper ocean biogeochemical processes. In this study, we constructed and deployed a ship-based lidar system to measure laser backscattering and linear depolarization profiles in the coastal Mid-Atlantic ranging from estuarine to oceanic conditions, and across the Gulf of Maine (GoM). The instrument identified layers with different backscattering intensity in stratified waters of the coastal Mid-Atlantic and produced system attenuation coefficients (Ksys) approximating the absorption coefficient (apg) of particulate + dissolved matter. The linear depolarization ratio was strongly related to in situ measurements of the particulate backscattering ratio (bbp/bp). Measurements of Ksys and linear depolarization made across the GoM corresponded well with simultaneous in situ observations performed aboard the M/V Nova Star and by an autonomous glider deployed along the transect. The relationship between Ksys and apg differed between sampling schemes, likely due to differences in the deployment geometries (e.g., height, nadir angle). These results support the proposition that ship-based lidar systems can provide a powerful tool for remotely measuring the vertical distributions of optical properties and geochemical constituents (e.g., particles) in the upper ocean. Continued development of compact lidar systems for deployment on ships, moorings, and autonomous platforms has the potential to greatly improve the quality and scope of a variety of oceanographic investigations.
Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-07 Xiaoma Li, Yuyu Zhou, Ghassem R. Asrar, Zhengyuan Zhu
High spatiotemporal resolution air temperature (Ta) datasets are increasingly needed for assessing the impact of temperature change on people, ecosystems, and energy system, especially in the urban domains. However, such datasets are not widely available because of the large spatiotemporal heterogeneity of Ta caused by complex biophysical and socioeconomic factors such as built infrastructure and human activities. In this study, we developed a 1 km gridded dataset of daily minimum Ta (Tmin) and maximum Ta (Tmax), and the associated uncertainties, in urban and surrounding areas in the conterminous U.S. for the 2003–2016 period. Daily geographically weighted regression (GWR) models were developed and used to interpolate Ta using 1 km daily land surface temperature and elevation as explanatory variables. The leave-one-out cross-validation approach indicates that our method performs reasonably well, with root mean square errors of 2.1 °C and 1.9 °C, mean absolute errors of 1.5 °C and 1.3 °C, and R2 of 0.95 and 0.97, for Tmin and Tmax, respectively. The resulting dataset captures reasonably the spatial heterogeneity of Ta in the urban areas, and also captures effectively the urban heat island (UHI) phenomenon that Ta rises with the increase of urban development (i.e., impervious surface area). The new dataset is valuable for studying environmental impacts of urbanization such as UHI and other related effects (e.g., on building energy consumption and human health). The proposed methodology also shows a potential to build a long-term record of Ta worldwide, to fill the data gap that currently exists for studies of urban systems.
Surface albedo and toc-r 300 m products from PROBA-V instrument in the framework of Copernicus Global Land Service Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-06 Jean-Louis Roujean, Jonathan Leon-Tavares, Bruno Smets, Patrick Claes, Fernando Camacho De Coca, Jorge Sanchez-Zapero
Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-06 Zeshi Zheng, Noah P. Molotch, Carlos A. Oroza, Martha H. Conklin, Roger C. Bales
We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions.
Surface rock effects on soil moisture retrieval from L-band passive microwave observations Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-06 N. Ye, J.P. Walker, C. Rüdiger, D. Ryu, R.J. Gurney
The L-band (1.41 GHz) passive microwave remote sensing technique is the approach used by the first satellites dedicated to soil moisture measurement, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) mission developed by the National Aeronautics and Space Administration (NASA). These satellites aim to provide global soil moisture maps for the top ~5 cm layer of soil with an accuracy better than 0.04 m3/m3. However, with a passive microwave observing resolution of ~40 km, non-soil targets such as surface rock may possibly confound the brightness temperature observations and degrade the accuracy of retrievals for many SMOS and SMAP pixels across the world. Since the microwave contribution of rock is not well accounted for in current soil moisture retrieval algorithms, simply ignoring its existence may be detrimental to the performance of resultant soil moisture products. Using a combination of model simulations and airborne field campaign data from central Australia, this study has determined that a rock cover fraction threshold of up to 0.4 can be tolerated before the 0.04 m3/m3 soil moisture target accuracy is potentially exceeded under extreme dry or wet conditions. However, this threshold reduces to 0.2 when assessed in terms of a brightness temperature impact >4 K. These rock fraction thresholds have subsequently been applied to the Ecoclimap rock cover map, identifying the SMOS and SMAP pixels globally that are likely to be adversely affected if rock is unaccounted for. The results show that approximately ~3.3% of all SMOS and SMAP pixels may have brightness temperature impacts exceeding 4 K from surface rock, with Asia being the most affected, having ~6.0% affected pixels. These values reduce to ~1.5% of SMOS and SMAP pixels globally, and ~3.1% for Asia, when assessed in terms of soil moisture errors expected to possibly exceed 0.04 m3/m3 when not accounting for surface rock.
An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-05 Jianwei Wei, Zhongping Lee, Rodrigo Garcia, Laura Zoffoli, Roy A. Armstrong, Zhehai Shang, Patrick Sheldon, Robert F. Chen
The Operational Land Imager (OLI) onboard Landsat-8 satellite can provide remote sensing reflectance (Rrs) of aquatic environments with high spatial resolution (30 m), allowing for benthic habitat mapping and monitoring of bathymetry and water column optical properties. To facilitate these applications, accurate sensor-derived Rrs is required. In this study, we assess atmospheric correction schemes, including NASA's NIR-SWIR approach, Acolite's NIR and SWIR approaches and the cloud-shadow approach. We provide the first comprehensive evaluation for Landsat-8 Rrs retrievals in optically shallow coral reefs, along with an investigation of Landsat-8 Rrs products in a temperate turbid embayment. The obtained Landsat-8 Rrs data products are evaluated with concurrent in situ hyperspectral Rrs measurements. Our analyses show that the NASA and the cloud-shadow approaches generated reliable Rrs products across shallow coral reefs and optically deep waters. This evaluation suggests that high quality Rrs products are achievable from the Landsat-8 satellite in optically shallow environments, which supports further application of Landsat-8 type measurements for coral reef studies.
Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions Remote Sens. Environ. (IF 6.457) Pub Date : 2018-06-02 Gotzon Basterretxea, Joan Salvador Font-Muñoz, Paula María Salgado-Hernanz, Jorge Arrieta, Ismael Hernández-Carrasco
The Mediterranean Sea exhibits a strong basin and regional scale phytoplankton variability correlated to its semi-enclosed nature, complex orography and the variety of physical and chemical processes that regulate its productivity. Herein, using 17 years of ocean-color composites, we investigate differences in the regional patterns of interannual variability in satellite-derived chlorophyll (Chl), a proxy for phytoplankton biomass. A neural network classification, based on the Self-Organizing Maps (SOM) analysis in the time domain, is used to reveal regions of similar temporal variability of Chl in the Mediterranean Sea. Characteristic temporal patterns extracted by the SOM analysis show different scales of variation that can be related to already identified oceanographic features and biogeochemical variability in the Mediterranean Sea. Clear differences are noticed between regions located in the Western basin and Adriatic Sea, where rivers, winter mixing and winds are known to drive variations in primary production at regional scale and regions located in the Eastern basin, represented by a large and rather homogeneous region. Using the SOM-defined characteristic temporal patterns of Chl, we analyzed the regional influence of the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) in the long-term (>1 year) Chl variability. Our results indicate that NAO has more influence in the Chl variations occurring in regions located in the Western basin whereas ENSO exhibits higher impact on the central Mediterranean and Eastern basin during its positive phase. Both NAO and ENSO show non-stationary coherence with Mediterranean Chl. The analysis also reveals a sharp regime shift occurring in 2004–2007, when NAO changed from positive to negative values. This shift particularly affected the winter phytoplankton biomass and it is indicative of climate driven ecosystem-level changes in the Mediterranean Sea. Our results stablish a regional connection between interannual phytoplankton variability exhibited in different regions of the Mediterranean Sea and climate variations.
Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory Remote Sens. Environ. (IF 6.457) Pub Date : 2018-05-30 Jan Pisek, Henning Buddenbaum, Fernando Camacho, Joachim Hill, Jennifer L.R. Jensen, Holger Lange, Zhili Liu, Arndt Piayda, Yonghua Qu, Olivier Roupsard, Shawn P. Serbin, Svein Solberg, Oliver Sonnentag, Anne Thimonier, Francesco Vuolo
Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.
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