Estimating canola phenology using synthetic aperture radar Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-16 Heather McNairn, Xianfeng Jiao, Anna Pacheco, Abhijit Sinha, Weikai Tan, Yifeng Li
Prolonged periods of wet soil conditions, when present during critical crop development stages, can significantly elevate the risk of some crop diseases. Wet soils in fields of flowering canola are a concern with respect to the development of sclerotinia as this pathogen feeds on the petals of the canola flower. As such, determining if canola is in bloom during periods of high moisture is important in deciding whether to take action to mitigate this disease. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization Synthetic Aperture Radar (SAR) data were used with a novel dynamic filtering framework to estimate canola growth stages. In this process, a new crop growth stage indicator was developed and SAR polarimetric parameters sensitive to changes in phenology were identified. Model development used multi-year SAR satellite and field data for one site in Manitoba, Canada. The crop growth estimator was then tested on unseen data from three sites, one in each of Canada's Prairie provinces. This independent validation established that the growth estimator was able to accurately determine canola growth stage and date of flowering with high accuracy. Correlation coefficients (r-values) between observed and estimated phenology ranged from 0.91 to 0.96. Given that this method performed well on test data from other sites and years, this approach could be widely adopted for monitoring the development of canola over extended regions.
Retrieval of land surface properties from an annual time series of Landsat TOA radiances during a drought episode using coupled radiative transfer models Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-17 Bagher Bayat, Christiaan van der Tol, Wouter Verhoef
The accurate retrieval of land surface vegetation properties under varying environmental conditions from time series of moderately high spatial resolution satellite observations is challenging. By coupling various Radiative Transfer (RT) models one can describe the soil, vegetation and atmosphere contributions in a “bottom-up” approach and, thereby, simulate top-of-atmosphere (TOA) spectral radiance data comparable to satellite-observed TOA radiances. This makes it possible to retrieve vegetation properties directly from TOA radiances rather than from atmospherically corrected top-of-canopy (TOC) reflectance data. The advantages of this approach are that a separate atmospheric correction of the satellite images is not necessary, and that the anisotropic surface reflection can also be taken into account effectively. In this study, we coupled various RT models, including the brightness – shape – moisture (BSM) reflectance model of the soil, the optical radiative transfer (RTMo) model of vegetation and the ‘MODerate resolution atmospheric TRANsmission’ (MODTRAN) model of the atmosphere, to simulate an annual time series of Landsat satellite TOA radiances observed during a drought episode in California Mediterranean grasslands in 2004. The inversion of this coupled system through an optimization technique against Landsat TOA radiances resulted in direct retrieval of vegetation properties. We accommodated the surface anisotropic reflection in our coupled modeling and also defined a novel anisotropy index to quantitatively express the importance of this phenomenon in satellite image analysis for the first time. Our study showed that the coupled use of RT models was able to accurately reproduce the time series of observed TOA radiances collected under varying soil moisture contents during an extended drought episode. The proposed coupling approach is useful for successful retrieval of vegetation properties from time series of satellite TOA radiance data to produce maps of land surface properties and to monitor vegetation properties variations in an operational straightforward way. The approach can also be easily adapted for conducting multi-sensor time series studies, creating a much denser temporal sampling than would be possible for separate single sensors.
Large area cropland extent mapping with Landsat data and a generalized classifier Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-16 Aparna R. Phalke, Mutlu Özdoğan
Accurate and up-to-date cropland maps play an important role in the study of food security. Traditional mapping of croplands using medium resolution (10–100 m) remote sensing imagery involving a “one-time, one-place” approach requires significant computing and labor resources. Although high mapping accuracies can be achieved using this approach, it is tedious and expensive to collect reference information to train the classifiers at each location and to apply over large areas, such as a continent. Moreover, large area cropland mapping presents additional challenges including a wide range of agricultural management practices, climatic conditions, and crop types. To overcome these challenges, here we report on a generalized image classifier to map cropland extent, which builds a classification model using training data from one location and time period, applied to other times and locations without the need for additional training data. The study was demonstrated across eight agro-ecological zones (AEZs) in Europe, the Middle East and North Africa using Landsat data acquired between 2009 and 2011. To reduce between-scene variability associated with image availability and cloud cover, input data were reduced to salient temporal statistics derived from enhanced vegetation index (EVI) combined with topographic variables. The generalized classifier was then tested across three levels of generalization: 1. individual - where training data were extracted from and applied to the same Landsat footprint; 2. AEZ where training data were extracted from a set of Landsat footprints within an AEZ and applied to any other Landsat footprint in the same AEZ; and 3. regional where training data were extracted from a set of Landsat footprints in the whole study area and applied to any other Landsat footprint inside the study area. Results showed that the generalized classifier is successful in identifying and mapping croplands with comparable success across all three levels of generalization with minimal cost: average loss in accuracy (as measured by overall accuracy) from the individual level (average overall accuracy of 80 ± 5%) to regional level (average overall accuracy of 74 ± 10%) is between 2 and 10% depending on the location. Results also show that generalization is not sensitive to the choice of the classification algorithm – the Linear Discriminant Analysis (LDA) model performs equally well compared to many popular machine learning algorithms found in the literature. This work suggests the generalization/signature extension framework has a great potential for rapid identification and mapping of croplands with reasonable accuracies over large areas using only easily computed vegetation indices with very little user input and ground information requirement.
Integrating cloud-based workflows in continental-scale cropland extent classification Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-15 Richard Massey, Temuulen T. Sankey, Kamini Yadav, Russell G. Congalton, James C. Tilton
Accurate information on cropland spatial distribution is required for global-scale assessments and agricultural land use policies. Cloud computing platforms such as Google Earth Engine (GEE) provide unprecedented opportunities for large-scale classifications of Landsat data. We developed a novel method to fuse pixel-based random forest classification of continental-scale Landsat data on GEE and an object-based segmentation approach known as recursive hierarchical segmentation (RHSeg). Using our fusion method, we produced a continental-scale cropland extent map for North America at 30 m spatial resolution for the nominal year 2010. The total cropland area for North America was estimated at 275.18 million hectares (Mha). The overall accuracies of the map are >90% across the continent. This map also compares well with the United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-food Canada (AAFC) annual crop inventory (ACI), and the Mexican government agency Servicio de Información Agroalimentaria y Pesquera (SIAP)'s agricultural boundaries. Furthermore, our map compared well with sub-country statistics including state-wise and county-wise cropland statistics in regression models resulting in R2 > 0.84. This key contribution paves the way for more detailed products such as crop intensity, crop type, and crop irrigation, and provides a method for creating high-resolution cropland extent maps for other countries where spatial information about croplands are not as prevalent.
The Harmonized Landsat and Sentinel-2 surface reflectance data set Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-14 Martin Claverie, Junchang Ju, Jeffrey G. Masek, Jennifer L. Dungan, Eric F. Vermote, Jean-Claude Roger, Sergii V. Skakun, Christopher Justice
The Harmonized Landsat and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a Virtual Constellation (VC) of surface reflectance (SR) data acquired by the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat 8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, bidirectional reflectance distribution function normalization and spectral bandpass adjustment. Three products are derived from the HLS processing chain: (i) S10: full resolution MSI SR at 10 m, 20 m and 60 m spatial resolutions; (ii) S30: a 30 m MSI Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR); (iii) L30: a 30 m OLI NBAR. All three products are processed for every Level-1 input products from Landsat 8/OLI (L1T) and Sentinel-2/MSI (L1C). As of version 1.3, the HLS data set covers 10.35 million km2 and spans from first Landsat 8 data (2013); Sentinel-2 data spans from October 2015.The L30 and S30 show a good consistency with coarse spatial resolution products, in particular MODIS Collection 6 MCD09CMG products (overall deviations do not exceed 11%) that are used as a reference for quality assurance. The spatial co-registration of the HLS is improved compared to original Landsat 8 L1T and Sentinel-2A L1C products, for which misregistration issues between multi-temporal data are known. In particular, the resulting computed circular errors at 90% for the HLS product are 6.2 m and 18.8 m, for S10 and L30 products, respectively. The main known issue of the current data set remains the Sentinel-2 cloud mask with many cloud detection omissions. The cross-comparison with MODIS was used to flag products with most evident non-detected clouds. A time series outlier filtering approach is suggested to detect remaining clouds. Finally, several time series are presented to highlight the high potential of the HLS data set for crop monitoring.
Discrimination of liana and tree leaves from a Neotropical Dry Forest using visible-near infrared and longwave infrared reflectance spectra Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-13 J. Antonio Guzmán Q., Benoit Rivard, G. Arturo Sánchez-Azofeifa
Increases in liana abundance in tropical forests are pervasive threats to the current and future forest carbon stocks. Never before has the need been more evident for new approaches to detect the presence of liana in ecosystems, given their significance as fingerprints of global environmental change. In this study, we explore the use of longwave infrared reflectance (LWIR, 8–11 μm) as a wavelength region for the classification of liana and tree leaves and compare classification results with those obtained using visible-near infrared reflectance data (VIS-NIR, 0.45–0.95 μm). Twenty sun leaves were collected from each of 14 liana species and 21 tree species located at the canopy or forest edge (n = 700) in Santa Rosa National Park, Costa Rica. LWIR and VIS-NIR reflectance measurements were performed on these leaves using a portable calibrated Fourier Transform Infrared Spectroscopy (FTIR) Agilent ExoScan 4100 and a UniSpec spectral analysis system, respectively. The VIS-NIR and LWIR data were first resampled. Then these two spectral libraries were pre-processed for noise reduction and spectral feature enhancement resulting in three datasets for each spectral region as follows: filtered only, filtered followed by extraction of the first derivative, and continuous wavelet transformation (CWT). Data reduction was then applied to these data sets using principal components analysis (PCA). The outputs obtained from the PCA were used to conduct the supervised classification of liana and tree leaves. In total, 21 classifiers were applied to datasets of training and testing to extract the classification accuracy and agreement for liana and tree leaves. The results suggest that the classification of leaves based on LWIR data can reach accuracy values between 66 and 96% and agreement values between 32 and 92%, regardless of the type of classifier. In contrast, the classification based on VIS-NIR data shows accuracy values between 50 and 70% and agreement values between 0.01 and 40%. The highest classification rates of liana and tree leaves were obtained from datasets pre-processed using the CWT or from the extraction of the first derivative and classified using either random forest, k-nearest neighbor, or support vector machine with radial kernel. The results using the LWIR reflectance highlight the potential of this spectral region for the accurate detection of liana extent in tropical ecosystems. Future studies should consider this potential and test the regional monitoring of lianas.
Fine-scale three-dimensional modeling of boreal forest plots to improve forest characterization with remote sensing Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-12 Jean-François Côté, Richard A. Fournier, Joan E. Luther, Olivier R. van Lier
Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-12 Qi Huang, Di Long, Mingda Du, Chao Zeng, Gang Qiao, Xingdong Li, Aizhong Hou, Yang Hong
River discharge is an important variable in the water cycle that is related to water supply, irrigation, and flood forecasting. However, gauging stations are extremely limited across most high-mountain regions such as the Tibetan Plateau (TP), known as the Asia's water towers. Remote sensing, in combination with partial in situ discharge measurements, bridges the gap in monitoring river discharge over ungauged and poorly gauged basins. Of great importance for the successful retrieval of river discharge using remote sensing are river width (water surface area) and water level (water surface elevation), but it is challenging to retrieve accurate discharge values for high-mountain regions because of narrow river channels, complex terrain, and limited observations from a single satellite platform. Here, we used 1237 high-spatial-resolution images (Landsat series and Sentinel-1/2) to derive water surface areas with the Google Earth Engine (GEE), and satellite altimetry (Jason-2/3 and Satellite with Argos and AltiKa (SARAL/Altika)) to derive water levels for the Upper Brahmaputra River (UBR, the Yarlung Zangbo River in China) in the TP where the river width is typically less than 400 m. Using three power function equations, discharge was estimated for cross-sections around the four gauging stations in the UBR with triangular cross-sections outperforming their trapezoidal counterparts. It was also found that the equation combining both river width and water level produced the best discharge estimates whereas the other two equations (requiring either river width or water level as the input data) were complementary and could be used to extend the time series of discharge estimates. The Nash–Sutcliffe efficiency coefficient values for the discharge estimates range from 0.68 to 0.98 during the study period 2000–2017. The proposed method is feasible to estimate discharge in the UBR and potentially other high-mountain rivers globally.
Heritable variation in needle spectral reflectance of Scots pine (Pinus sylvestris L.) peaks in red edge Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-12 Jaroslav Čepl, Jan Stejskal, Zuzana Lhotáková, Dana Holá, Jiří Korecký, Milan Lstibůrek, Ivana Tomášková, Marie Kočová, Olga Rothová, Markéta Palovská, Jakub Hejtmánek, Anna Krejzková, Salvador Gezan, Ross Whetten, Jana Albrechtová
Foliar reflectance is readily used in evaluating physiological status of agricultural crops and forest stands. However, in the case of forest trees, underlying genetics of foliar spectral reflectance and pigment content have rarely been investigated. We studied a structured population of Scots pine, replicated on two sites, with the selected trees´ pedigree reconstructed via DNA markers. This allowed us to decompose phenotypic variance of pigment and reflectance traits into its causal genetic components, and to estimate narrow-sense heritability (h2).We found statistically significant h2 ranging from 0.07 to 0.22 for most of the established reflectance indices. Additionally, we investigated the profile of heritable variation along the reflectance curve in 1 nm wavelength (WL) bands. We show that the maximum h2 value (0.39; SE 0.13) across the 400 to 2500 nm spectral range corresponds to the red edge inflection point, in this case to 722 nm WL band. Resultant h2 distribution indicates that additive gene effects fluctuate along the reflectance curve.Furthermore, h2 of the most widely used formats of reflectance indices, i.e. the simple ratio and the normalized difference, was estimated for all WL bands combined along the observed reflectance spectrum. The highest h2 estimates for both formats were obtained by combining WL bands of the red edge spectrum.These new genetically driven pigment- and spectral reflectance- based markers (proxies of adaptive traits) may facilitate selection of stress resistant plant genotypes. Recent development of high-resolution spectral sensors carried by airborne and spaceborn devices make foliage spectral traits a viable technology for mass phenotyping in forest trees.
Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-11 Lisheng Song, Shaomin Liu, William P. Kustas, Hector Nieto, Liang Sun, Ziwei Xu, Todd H. Skaggs, Yang Yang, Minguo Ma, Tongren Xu, Xuguang Tang, Qiuping Li
Operational estimation of spatio-temporal continuously daily evapotranspiration (ET), and the components evaporation (E) and transpiration (T), at river basin scale is very useful for developing sustainable water resource strategies, particularly in regions of limited water supplies. In this study, multi-year all-weather daily ET, E and T were estimated using MODIS-based (Dual Temperature Difference) DTD model under different land covers in the Heihe river basin in China, with a total area of approximately 143 × 103 km2. The remotely sensed ET was validated using ground measurements from large aperture scintillometer systems, with a source area of several kilometers, over grassland, cropland and riparian shrub-forest land cover. The results showed that the remotely sensed ET produced mean absolute percent differences (MAPD) of around 20% with the ground measurements during the growing season under clear sky conditions, but the model performance deteriorated for cloudy days. However, the daily ET product gave reasonable estimates for croplands with an MAPD value of about 20% and the estimates of T/ET and E/ET in good agreement with ground measurements. The DTD model also significantly outperformed other remote sensing-based models being applied globally. Based on these results the DTD model is considered reliable for monitoring crop water use and stress and to develop efficient irrigation strategies.
New neural network cloud mask algorithm based on radiative transfer simulations Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-10 Nan Chen, Wei Li, Charles Gatebe, Tomonori Tanikawa, Masahiro Hori, Rigen Shimada, Teruo Aoki, Knut Stamnes
Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Compared to threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.
Spatio-temporal variability of Antarctic sea-ice thickness and volume obtained from ICESat data using an innovative algorithm Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-10 Huan Li, Hongjie Xie, Stefan Kern, Wei Wan, Burcu Ozsoy, Stephan Ackley, Yang Hong
We use total (sea ice plus snow) freeboard as estimated from Ice, Cloud and land Elevation Satellite (ICESat) Geophysical Laser Altimeter System (GLAS) observations to compute Antarctic sea-ice thickness and volume. In order to overcome assumptions made about the relationship between snow depth and total freeboard or biases in snow depth products from satellite microwave radiometry, we implement a new algorithm. We treat the sea ice-snow system as one layer with reduced density, which we approximate by means of a priori information about the snow depth to sea-ice thickness ratio. We derive this a priori information directly from ICESat total freeboard data using empirical equations relating in-situ measurements of total freeboard to snow depth or sea-ice thickness. We apply our new algorithm (one-layer method or OLM), which uses the buoyancy equation approach without the need for auxiliary snow depth data, to compute sea-ice thickness for every ICESat GLAS footprint from a valid total freeboard. An improved method for sea-ice volume retrieval is also used to derive ice volume at 6.25 km scale. Spatio-temporal variations of sea-ice thickness and volume are then analyzed in the circumpolar Antarctic as well as its six sea sectors: Pacific Ocean, Indian Ocean, Weddell East, Weddell West, Bell-Amund Sea, and Ross Sea, under both interannual and seasonal scales. Because the OLM algorithm relies on only one parameter, the total freeboard, and is independent of auxiliary snow depth information, it is believed to become a viable alternative sea-ice thickness retrieval method for satellite altimetry.
Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-09 Francisco Zambrano, Anton Vrieling, Andy Nelson, Michele Meroni, Tsegaye Tadesse
Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. These increases are particularly hazardous for developing countries. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000–2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. For each of the 758 census units considered, the time series of the response and the predictor variables were averaged for agricultural areas resulting in a 17-season time series per unit for each variable. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean Rcv2 values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe.
Species-related single dead tree detection using multi-temporal ALS data and CIR imagery Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-09 Agnieszka Kamińska, Maciej Lisiewicz, Krzysztof Stereńczak, Bartłomiej Kraszewski, Rafał Sadkowski
The assessment of the health conditions of trees in forests is extremely important for biodiversity, forest management, global environment monitoring, and carbon dynamics. There is a vast amount of research using remote sensing (RS) techniques for the assessment of the current condition of a forest, but only a small number of these are concerned with detection and classification of dead trees. Among the available RS techniques, only the airborne laser scanner (ALS) enables dead tree detection at the single tree level with high accuracy.The main objective of the study was to identify spruce, pine and deciduous trees by alive or dead classifications. Three RS data sets including ALS (leaf-on and leaf-off) and color-infrared (CIR) imagery (leaf-on) were used for the study. We used intensity and structural variables from the ALS data and spectral information derived from aerial imagery for the classification procedure. Additionally, we tested the differences in the classification accuracy of all variants contained in the data integration. In the study, the random forest (RF) classifier was used. The study was carried out in the Polish part of the Białowieża Forest (BF).In general, we can state that all classifications, with different combinations of ALS features and CIR, resulted in high overall accuracy (OA ≥ 90%) and Kappa (κ > 0.86). For the best variant (CIR_ALSWSn-FH), the mean values of overall accuracy and Kappa were equal to 94.3% and 0.93, respectively. The leaf-on point cloud features alone produced the lowest accuracies (OA = 75–81% and κ = 0.68–0.76). Improvements of 0-0.04 in the Kappa coefficient and 0–3.1% in the overall classification accuracy were found after the point cloud normalization for all variants. Full-height point cloud features (F) produced lower accuracies than the results based on features calculated for half of the tree height point clouds (H) and combined FH.The importance of each of the predictors for different data sets for tree species classification provided by the RF algorithm was investigated. The lists of top features were the same, independent of intensity normalization. For the classification based on both of the point clouds (leaf–on and leaf-off), three structural features (a proportion of first returns for both half-height and full-height variants and the canopy relief ratio of points) and two intensity features from first returns and half-height variant (the coefficient of variation and skewness) were rated as the most important. In the classification based on the point cloud with CIR features, two image features were among the most important (the NDVI and mean value of reflectance in the green band).
Improving the precision of sea level data from satellite altimetry with high-frequency and regional sea state bias corrections Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-09 Marcello Passaro, Zulfikar Adlan Nadzir, Graham D. Quartly
The sea state bias (SSB) is a large source of uncertainty in the estimation of sea level from satellite altimetry. It is still unclear to what extent it depends on errors in parameter estimations (numerical source) or to the wave physics (physical source).By improving the application of this correction we compute 20-Hz sea level anomalies that are about 30% more precise (i.e. less noisy) than the current standards. The improvement is two-fold: first we prove that the SSB correction should be applied directly to the 20-Hz data (12 to 19% noise decrease); secondly, we show that by recomputing a regional SSB model (based on the 20-Hz estimations) even a simple parametric relation is sufficient to further improve the correction (further 15 to 19% noise decrease).We test our methodology using range, wave height and wind speed estimated with two retrackers applied to Jason-1 waveform data: the MLE4 retracked-data available in the Sensor Geophysical Data Records of the mission and the ALES retracked-data available in the OpenADB repository (https://openadb.dgfi.tum.de/). The regional SSB models are computed parametrically by means of a crossover analysis in the Mediterranean Sea and North Sea.Correcting the high-rate data for the SSB reduces the correlation between retracked parameters. Regional variations in the proposed models might be due to differences in wave climate and remaining sea-state dependent residual errors. The variations in the empirical model with respect to the retracker used recall the need for a specific SSB correction for any retracker.This study, while providing a significantly more precise solution to exploit high-rate sea level data, calls for a re-thinking of the SSB correction in both its physical and numerical component, gives robustness to previous theories and provides an immediate improvement for the application of satellite altimetry in the regions of study.
SMAP soil moisture improves global evapotranspiration Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-09 Adam J. Purdy, Joshua B. Fisher, Michael L. Goulden, Andreas Colliander, Gregory Halverson, Kevin Tu, James S. Famiglietti
Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPLSM model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle.
Deformation of Linfen-Yuncheng Basin (China) and its mechanisms revealed by Π-RATE InSAR technique Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-08 Chaoying Zhao, Chuanjin Liu, Qin Zhang, Zhong Lu, Chengsheng Yang
The Linfen-Yuncheng Basin (LYB) in China is a region possessing severe geo-hazards, including active tectonic fault movement, land subsidence and ground fissures among others. Interferometric Synthetic Aperture Radar (InSAR) technique is applied to map surface deformation associated with various geo-hazards in this basin. The poly-interferogram rate and time-series estimator algorithm (Π-RATE) is used over forty-nine scenes of SAR data to generate the deformation maps over the entire LYB. The precision of InSAR results is around 3 mm/yr. Some active faults and ground fissures are successfully detected. The spatiotemporal characteristics of tableland uplift, faults displacement and basin subsidence are quantitatively monitored with InSAR technique ranging from 2 mm/yr to 142 mm/yr. Finally, the mechanisms of surface deformation regarding large scale Zhongtiaoshan fault, middle scale basin land subsidence and small scale ground fissures are discussed in terms of interseismic movement, underground water level changes and hydrostratigraphic heterogeneity.
LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-09 Gourav Misra, Allan Buras, Marco Heurich, Sarah Asam, Annette Menzel
In the past, studies have successfully identified climatic controls on the temporal variability of the land surface phenology (LSP). Yet we lack a deeper understanding of the spatial variability observed in LSP within a land cover type and the factors that control it. Here we make use of a high resolution LiDAR based dataset to study the effect of subpixel forest stand characteristics on the spatial variability of LSP metrics based on MODIS NDVI. Multiple linear regression techniques (MLR) were applied on forest stand information and topography derived from LiDAR as well as land cover information (i.e. CORINE and proprietary habitat maps for the year 2012) to predict average LSP metrics of the mountainous Bavarian Forest National Park, Germany. Six different LSP metrics, i.e. start of season (SOS), end of season (EOS), length of season (LOS), NDVI integrated over the growing season (NDVIsum), maximum NDVI value (NDVImax) and day of maximum NDVI (maxDOY) were modelled in this study. It was found that irrespective of the land cover, the mean SOS, LOS and NDVIsum were largely driven by elevation. However, inclusion of detailed forest stand information improved the models considerably. The EOS however was more complex to model, and the subpixel percentage of broadleaf forests and the slope of the terrain were found to be more strongly linked to EOS. The explained variance of the NDVImax improved from 0.45 to 0.71 when additionally considering land cover information, which further improved to 0.84 when including LiDAR based subpixelforest stand characteristics. Since completely homogenous pixels are rare in nature, our results suggest that incorporation of subpixel forest stand information along with land cover type leads to an improved performance of topography based LSP models. The novelty of this study lies in the use of topography, land cover and subpixel vegetation characteristics derived from LiDAR in a stepwise manner with increasing level of complexity, which demonstrates the importance of forest stand information on LSP at the pixel level.
Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-06 S.M. Punalekar, A. Verhoef, T.L. Quaife, D. Humphries, L. Bermingham, C.K. Reynolds
A novel technique using LiDAR to identify native-dominated and tame-dominated grasslands in Canada Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-06 Ryan J. Fisher, Ben Sawa, Beatriz Prieto
Native grassland in North America is considered one of the most imperiled and altered ecosystems. Unfortunately, an assessment of how much native grassland remains in North America is difficult because all nation-wide landcover mapping products do not reliably distinguish native grasslands from grasslands that have been deliberately planted with tame grasses and forbs (i.e., tame grasslands). We established a 218.5 km2 study area in southwestern Saskatchewan, Canada to evaluate the use of high-resolution Light Detection and Ranging (LiDAR) for classification of native (i.e., fields with native-dominant species mixes or fields that were formerly native species dominated but have been invaded by exotic species) and tame grasslands (i.e., grasslands dominated by exotic grass and forb species), and compared these classifications to the best-available landcover mapping product that is currently available for this area. We used the presence of tractor furrows, identified from the LiDAR digital terrain hillshade product, in tame-dominated grasslands to distinguish them from native-dominated grasslands that had an absence of tractor furrows. The LiDAR method achieved substantially better classification success (Cohen's Kappa = 0.57) at distinguishing native-dominated (N = 82) from tame-dominated grasslands (N = 45), than the currently available landcover product (Cohen's Kappa = 0.13) over our 218.5 km2 study area. Misclassification by LiDAR of fields that had been planted with tame grasses and forbs, but were starting to be re-established by native plants appeared to be one weakness of the method in the study area. Our research highlights a novel and time-efficient method for classifying LiDAR imagery using easily available image analysis features in ArcGIS.
Spatio-temporal variations of CDOM in shallow inland waters from a semi-analytical inversion of Landsat-8 Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-05 Jiwei Li, Qian Yu, Yong Q. Tian, Brian L. Becker, Paul Siqueira, Nathan Torbick
Bottom reflectance is often the main cause of high uncertainty in Colored Dissolved Organic Matter (CDOM) estimation for optically shallow waters. This study presents a Landsat-8 based Shallow Water Bio-optical Properties (SBOP) algorithm to overcome bottom effects so as to successfully observe spatial and temporal CDOM dynamics in inland waters. We evaluated the algorithm via 58 images and a large set of field measurements collected across seasons of multiple years in the Saginaw Bay, Lake Huron. Results showed that the SBOP algorithm reduced estimation errors by as much as 4 times (RMSE = 0.17 and R2 = 0.87 in the Saginaw Bay) when compared to the QAA-CDOM algorithm that did not take into account bottom reflectance. These improvements in CDOM estimation are consistent and robust across broad range CDOM absorption. Our analysis revealed: 1) the proposed remote sensing algorithm resulted in significant improvements in tracing spatial-temporal CDOM inputs from terrestrial environments to lakes, 2) CDOM distribution captured with high resolution land-viewing satellite is useful in revealing the impacts of terrestrial ecosystems on the aquatic environment, and 3) Landsat-8 OLI, with its 16 days revisit time, provides valuable time series data for studying CDOM seasonal variations at land-water interface and has the potential to reveal its relationship to adjacent terrestrial biogeography and hydrology. The study presents a shallow water algorithm for studying freshwater or coastal ecology, as well as carbon cycling science.
Developing 5 m resolution canopy height and digital terrain models from WorldView and ArcticDEM data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-04 Arjan J.H. Meddens, Lee A. Vierling, Jan U.H. Eitel, Jyoti S. Jennewein, Joanne C. White, Michael A. Wulder
Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China Remote Sens. Environ. (IF 6.457) Pub Date : 2018-10-02 Jing Ge, Baoping Meng, Tiangang Liang, Qisheng Feng, Jinlong Gao, Shuxia Yang, Xiaodong Huang, Hongjie Xie
Monitoring changes in grassland cover is essential in assessment of grassland health as well as the effects of anthropogenic interventions and global climate change on grassland ecosystems. Remote sensing is an effective approach for providing rapid and dynamic monitoring of vegetation cover over large grassland areas. In this study, four types of remote sensing retrieval models (i.e., pixel dichotomy models, univariate vegetation index (VI) regression models, multivariate regression models, and a support vector machine (SVM) model) are built to derive grassland cover based on moderate resolution imaging spectroradiometer (MODIS) data and the measured grassland cover data collected (by unmanned aerial vehicle) during the grassland peak growing season from 2014 to 2016. The optimal model is then used to map the spatial distribution of grassland cover and its dynamic change in the headwater region of the Huanghe River (Yellow River) (HRHR) of the northeastern Tibetan Plateau over the 16 years period (2001 to 2016). The results show that (1) the pixel dichotomy models based on MODIS VI data are inappropriate for estimating grassland cover in the HRHR when their endmembers (VIoil and VIveg) are determined based only on the MODIS data; (2) the multivariate regression models present better performance than the univariate VI (normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI)) models; (3) MODIS NDVI outperforms MODIS EVI for modeling grassland cover in the study area; (4) the SVM model based on nine factors is the optimal model (R2: 0. 75 and RMSE: 6.85%) for monitoring alpine grassland cover in the study area; and (5) majority of the grassland area (59.9%) of the HRHR showed increase in yearly maximum grassland cover from 2001 to 2016, while the average yearly maximum grassland cover for the 16 years exhibited a generally increasing trend from west to east and from north to south. This study provides a more suitable remote sensing inversion model to greatly improve the accuracy of modeling alpine grassland cover in the HRHR, and to better assess grassland health status and the impacts of warming climate to grasslands in regions of remote and harsh environments.
Potential and limits of non-local means InSAR filtering for TanDEM-X high-resolution DEM generation Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-28 Xiao Xiang Zhu, Gerald Baier, Marie Lachaise, Yilei Shi, Fathalrahman Adam, Richard Bamler
The primary objective of the German TanDEM-X mission is the generation of a globally available, highly accurate and detailed digital elevation model (DEM), with the final product having 12 m posting, 2 m relative and 10 m absolute vertical accuracy. The first version of this global DEM has been finalized by the German Aerospace Center (DLR), in September 2016. Our experience with the experimental application of non-local means filters to TanDEM-X data suggests that TanDEM-X has the potential of producing DEMs of even higher resolution and accuracy. The goal of this investigation is to explore the possibility of employing non-local InSAR filters to achieve an effective resolution of 6 m, with an equivalent posting, and a relative height error below 0.8 m, i.e. an increase of quality by a factor of 2 × 2 in resolution and a factor of 2 m/0.8 m = 2.5 in height accuracy — all in all one order of magnitude.
Invasive tree species detection in the Eastern Arc Mountains biodiversity hotspot using one class classification Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-28 Rami Piiroinen, Fabian Ewald Fassnacht, Janne Heiskanen, Eduardo Maeda, Benjamin Mack, Petri Pellikka
Eucalyptus spp. and Acacia mearnsii are common exotic tree species in eastern Africa that have shown (strong) invasive behavior in some regions. Acacia mearnsii is considered a highly invasive species that is replacing native species and Eucalyptus spp. are known to consume high amounts of groundwater with suspected effects on native flora. Mapping the occurrence of these species in the Taita Hills, Kenya (part of the Eastern Arc Mountains Biodiversity Hotspot) is important as there is lack of knowledge on their occurrence and ecological impact in the area. Mapping methods that require a lot of fieldwork are impractical in areas like the Taita Hills, where the terrain is rugged and the infrastructure is poor. Our aim was hence to map the occurrence of these tree species in a 100 km2 area using airborne imaging spectroscopy and laser scanning. We used a one class biased support vector machine (BSVM) classifier as it needs labeled training data only for the positive classes (A. mearnsii and Eucalyptus spp.), which potentially reduces the amount of required fieldwork. We also introduce a new approach for parameterizing and setting the threshold level simultaneously for the BSVM classifier. The second aim was to link the occurrence of these species to selected environmental variables. The results showed that the BSVM classifier is suitable for mapping Acacia mearnsii and Eucalyptus spp., holding the potential to improve the efficiency of field data collection. The introduced parametrization/threshold selection method performed better than other commonly used approaches. The crown level F1-score was 0.76 for Eucalyptus spp. and 0.78 for A. mearnsii. We show that Eucalyptus spp. and A. mearnsii trees cover 0.8% and 1.6% of the study area, respectively. Both species are particularly located on steeper slopes and higher altitudes. Both species have significant occurrences in areas close to the biggest remaining native forest patch (Ngangao) in the study area. Nonetheless, follow-up studies are needed to evaluate their impact on the native flora and fauna, as well as their impact on the water resources. The maps created in this study in combination with such follow-up studies could serve as base data to generate guidelines that authorities can use to take action in handling the problems these species are causing.
Multiple Optimal Depth Predictors Analysis (MODPA) for river bathymetry: Findings from spectroradiometry, simulations, and satellite imagery Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-28 Milad Niroumand-Jadidi, Alfonso Vitti, David R. Lyzenga
Remote mapping of bathymetry can play a key role in gaining spatial and temporal insight into fluvial processes, ranging from hydraulics and morphodynamics to habitat conditions. This research introduces Multiple Optimal Depth Predictors Analysis (MODPA), which combines previously developed depth predictors along with additional predictors derived from the intensity component of the HSI color space transformation. MODPA empirically selects a set of optimal predictors among all candidates utilizing partial least squares (PLS), stepwise, or principal component (PC) regression models. The primary focus of this study was on shallow (<1 m deep) and clearly flowing streams where substrate variability could have a pronounced effect on depth retrieval. Spectroscopic experiments were performed under controlled conditions in a hydraulic laboratory to examine the robustness of bathymetry models with respect to changes in bottom type. Further, simulations from radiative transfer modeling were used to extend the analysis by isolating the effect of inherent optical properties (IOPs) and by investigating the performance of bathymetry models in optically complex and deeper streams. The bathymetry of the Sarca River, a shallow river in the Italian Alps, was mapped using a WorldView-2 (WV-2) image, for which we evaluated the atmospheric compensation (AComp) product. Results indicated the greater robustness of multiple-predictor models particularly MODPA rather than single-predictor models, such as Optimal Band Ratio Analysis (OBRA), with respect to heterogeneity of bottom types, IOPs, and atmospheric effects. The HSI intensity component enhanced the accuracy of depth retrieval, particularly in optically-complex waters and also for low spectral resolution imagery (e.g., GeoEye). Further, the enhanced spectral resolution of WV-2 imagery improved bathymetry retrieval compared to 4-band GeoEye data.
New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-27 Xiaoping Wang, Fei Zhang, Hsiang-te Kung, Verner Carl Johnson
This study aimed to improve the potential of Analytical Spectral Devices (ASD) hyperspectral and Landsat Operational Land Imager (OLI) data in predicting soil organic matter content (SOMC) in the bare topsoil of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China. The results indicated that the correlation of coefficients (R) between SOMCs and hyperspectral data processed by fractional derivative were significant at the 0.01 level; the number of wave bands increased initially and then decreased when the order increased. The correlation of coefficient peak appeared at the 1.2 order with a value of 0.52. The correlation of coefficients (R) between SOMCs and the optimal remote sensing indexes (the ratio index, RI; difference index, DI; and the normalized difference index, NDI) of peaked at the 1.2 order, with correlation of coefficients (R) values of 0.81, 0.86 and 0.82, respectively. Six SOMC estimation models were created by means of a single band and optimal remote sensing indexes using Gray Relational Analysis-BP Neural Network (GRA-BPNN). This study found that the optimal model was a 1.2 order derivative model, where the lowest root mean square error (RMSE) was 3.26 g/kg, the highest was 0.92, and the residual prediction deviation (RPD) was 2.26. To complete the high accuracy retrieval of SOMCs, based on Landsat OLI operational land images data, more ‘hidden’ information from the Landsat OLI images were obtained by employing the subsection of spectral band method and the fractional derivative algorithm. Accuracy of the SOMC map was attained by the optimal model of the ground hyperspectral data and the Landsat OLI data, which had low RMSE values of 4.21 g/kg and 4.16 g/kg, respectively. Therefore, we conclude that the SOMC can be estimated and retrieved using a fractional derivative algorithm, the subsection of spectral band method, and the optimal remote sensing index.
Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-27 Asa Gholizadeh, Daniel Žižala, Mohammadmehdi Saberioon, Luboš Borůvka
Soil Organic Carbon (SOC) is a useful representative of soil fertility and an essential parameter in controlling the dynamics of various agrochemicals in soil. Soil texture is also used to calculate soil's ability to retain water for plant growth. SOC and soil texture are thus important parameters of agricultural soils and need to be regularly monitored. Optical satellite remote sensing offers the potential for frequent surveys over large areas. In addition, the recently-operated Sentinel-2 missions provide free imagery. This study compared the capabilities of Sentinel-2 for monitoring and mapping of SOC and soil texture (clay, silt and sand content) with those obtained from airborne hyperspectral (CASI/SASI sensors) and lab ASD FieldSpec spectroradiometer measurements at four agricultural sites in the Czech Republic. Combination of 10 extracted bands of the Sentinel-2 and 18 spectral indices, as independent variables, were used to train prediction models and then produce spatial distribution maps of the selected attributes. Results showed that the prediction accuracy based on lab spectroscopy, airborne and Sentinel-2 in the majority of the sites was adequate for SOC and fair for clay; however, Sentinel-2 imagery could not be used to detect and map variations in silt and sand. The SOC and clay maps derived from the airborne and spaceborne datasets showed similar trend, with both performing better where SOC levels were relatively high, though at the highest levels Sentinel-2 was able to create the SOC map more precisely than the airborne sensors. Taken across all SOC levels measured in the reference data, Sentinel-2 results were marginally lower than lab spectroscopy and airborne imagery, but this reduction in precision may be offset by the extensive geographical coverage and more frequent revisit characteristic of satellite observation. The increased temporal revisit and area are expected to be positive enhancements to the acquisition of high-quality information on variations in SOC and clay content of bare soils.
A methodology to derive global maps of leaf traits using remote sensing and climate data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-26 Álvaro Moreno-Martínez, Gustau Camps-Valls, Jens Kattge, Nathaniel Robinson, Markus Reichstein, Peter van Bodegom, Koen Kramer, J. Hans C. Cornelissen, Peter Reich, Michael Bahn, Ülo Niinemets, Josep Peñuelas, Joseph M. Craine, Bruno E.L. Cerabolini, Vanessa Minden, Daniel C. Laughlin, Lawren Sack, Brady Allred, Steve W. Running
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.
Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-26 Vitor S. Martins, João V. Soares, Evlyn M.L.M. Novo, Claudio C.F. Barbosa, Cibele T. Pinto, Jeferson S. Arcanjo, Amy Kaleita
A practical atmospheric correction algorithm, called Coupled Moderate Products for Atmospheric Correction (CMPAC), was developed and implemented for the Multispectral Camera (MUX) on-board the China-Brazil Earth Resources Satellite (CBERS-4). This algorithm uses a scene-based processing and sliding window technique to derive MUX surface reflectance (SR) at continental scale. Unlike other optical sensors, MUX instrument imposes constraints for atmospheric correction due to the absence of spectral bands for aerosol estimation from imagery itself. To overcome this limitation, the proposed algorithm performs a further processing of atmospheric products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as input parameters for radiative transfer calculations. The success of CMPAC algorithm was fully assessed and confirmed by comparison of MUX SR data with the Landsat-8 OLI Level-2 and Aerosol Robotic Network (AERONET)-derived SR products. The spectral adjustment was performed to compensate for the differences of relative spectral response between MUX and OLI sensors. The results show that MUX SR values are fairly similar to operational Landsat-8 SR products (mean difference < 0.0062, expressed in reflectance). There is a slight underestimation of MUX SR compared to OLI product (except the NIR band), but the error metrics are typically low and scattered points are around the line 1:1. These results suggest the potential of combining these datasets (MUX and OLI) for quantitative studies. Further, the robust agreement of MUX and AERONET-derived SR values emphasizes the quality of moderate atmospheric products as input parameters in this application, with root-mean-square deviation lower than 0.0047. These findings confirm that (i) CMPAC is a suitable tool for estimating surface reflectance of CBERS MUX data, and (ii) ancillary products support the application of atmospheric correction by filling the gap of atmospheric information. The uncertainties of atmospheric products, negligence of the bidirectional effects, and two aerosol models were also identified as a limitation. Finally, this study presents a framework basis for atmospheric correction of CBERS-4 MUX images. The utility of CBERS data comes from its use, and this new product enables the quantitative remote sensing for land monitoring and environmental assessment at 20 m spatial resolution.
Retrieving river baseflow from SWOT spaceborne mission Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-21 Fulvia Baratelli, Nicolas Flipo, Agnès Rivière, Sylvain Biancamaria
The quantification of aquifer contribution to river discharge is of primary importance to evaluate the impact of climatic and anthropogenic stresses on the availability of water resources. Several baseflow estimation methods require river discharge measurements, which can be difficult to obtain at high spatio-temporal resolution for large basins. The future Surface Water and Ocean Topography (SWOT) satellite mission will provide discharge estimations for large rivers (>50–100 m wide) even in ungauged basins. The frequency of these estimations depends mainly on latitude and ranges from zero to more than ten values in the 21-day satellite cycle. This work aims at answering the following question: can baseflow be estimated from SWOT observations during the mission lifetime? An algorithm based on hydrograph separation by Chapman's filter was developed to automatically estimate the baseflow in a river network at regional scale (>10 000 km2). The algorithm was applied to the Seine river basin (75 000 km2, France) using the discharge time series simulated at daily time step by a coupled hydrological-hydrogeological model to obtain the reference baseflow estimations. The same algorithm is then forced with discharge time series sampled at SWOT observation frequency. The average baseflow is estimated with good accuracy for all the reaches which are observed at least once per cycle (relative bias less than 8%). The time evolution of baseflow is also rather well retrieved, with a Nash-Sutcliffe coefficient above 0.7 for 96% of the network length. An analysis of the effect of SWOT discharge uncertainties on baseflow estimation shows that bias is the component of discharge error that most contributes to the error on baseflow. Anyway, when the combined effect of SWOT discharge sampling and SWOT discharge uncertainties is considered, the error on baseflow estimates is slightly smaller than that on discharge. This work provides new potential for the SWOT mission in terms of global hydrological analysis and water cycle closure.
A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-19 Sarah J. Graves, T. Trevor Caughlin, Gregory P. Asner, Stephanie A. Bohlman
Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-19 Sylvain Jay, Frédéric Baret, Dan Dutartre, Ghislain Malatesta, Stéphanie Héno, Alexis Comar, Marie Weiss, Fabienne Maupas
The recent emergence of unmanned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for retrieving canopy variables from UAV multispectral observations, and (ii) to investigate to what extent the use of such centimeter-scale imagery makes it possible to improve the estimation of leaf and canopy variables in sugar beet crops (Beta vulgaris L.). Five important structural and biochemical plant traits are considered: green fraction (GF), green area index (GAI), leaf chlorophyll content (Cab), as well as canopy chlorophyll (CCC) and nitrogen (CNC) contents.Based on a comprehensive data set encompassing a large variability in canopy structure and biochemistry, the results obtained for every targeted trait demonstrate the superiority of centimeter-resolution methods over two standard remote-sensing approaches (i.e., vegetation indices and PROSAIL inversion) applied to average canopy reflectances. Two variables (denoted GFGREENPIX and VICAB) extracted from the images are shown to play a major role in these performances. GFGREENPIX is the GF estimate obtained by thresholding the Visible Atmospherically Resistant Index (VARI) image, and is shown to be an accurate and robust (e.g., against variable illumination conditions) proxy of the structure of sugar beet canopies, i.e., GF and GAI. VICAB is the mNDblue index value averaged over the darkest green pixels, and provides critical information on Cab. When exploited within uni- or multivariate empirical models, these two variables improve the GF, GAI, Cab, CCC and CNC estimates obtained with standard approaches, with gains in estimation accuracy of 24, 8, 26, 37 and 8%, respectively. For example, the best CCC estimates (R2 = 0.90) are obtained by multiplying Cab and GAI estimates respectively derived from VICAB and a log-transformed version of GFGREENPIX, log(1-GFGREENPIX).The GFGREENPIX and VICAB variables, which are only accessible from centimeter-scale imagery, contributes to a better identification of the effects of canopy structure and leaf biochemistry, whose influences may be confounded when considering coarser resolution observations. Such results emphasize the strong benefits of centimeter-scale UAV imagery over satellite or airborne remote sensing, and demonstrate the relevance of low-cost multispectral cameras to retrieve a number of plant traits, e.g., for agricultural applications.
Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-18 Yidi Xu, Le Yu, Feng R. Zhao, Xueliang Cai, Jiyao Zhao, Hui Lu, Peng Gong
Ensuring food security has been the top priority of many regions, particularly in developing countries in Africa. In recent decades, increasing population, together with growing food demands, have put great pressure on the world's food production. Long-term, up-to-date, annual cropland mapping at high resolution (i.e., at tens-of-metre levels) is in urgent demand for tracking spatial and temporal patterns of cropland change. However, because of the difficulty of capturing seasonality and flexible cropping systems, few studies have focused on understanding the dynamics of cropland using Landsat data in Africa. Here, we propose a new method of updating annual cropland mapping using a change-detection approach and post-classification to improve on traditional bi-temporal change vector analysis. Three Landsat footprints in Africa were selected (Egypt, Ethiopia and South Africa) as our study areas based on their different cropping systems and field sizes. The potential annual change areas were detected by employing multiple indices and thresholds in reference and long-term annual composite Landsat images. Next, map updates were conducted in the potential change pixels using random forest-based classification. Different training sample metrics were used (seasonal and annual samples) and compared in the classification step. The long-term cropland mapping accuracies for these three sites ranged from 88.04% to 94.30% (Egypt), 76.28% to 82.88% (Ethiopia) and 56.52% to 67.53% (South Africa). The results showed improvements in the accuracy and consistency of updating the annual cropland information using change-detection approaches, accounting for accuracy increases of 2.40%, 10.62% and 0.55% compared with a yearly cropland mapping approach in our previous research. The best results using annual samples extracted from the same season with the classified images supported the use of annual and growing samples in long-term annual mapping. Overall, a common trend of cropland expansion in all three sites was revealed, with an increase rate of 10.06, 3.73 and 1.35 kha/year in Egypt, Ethiopia and South Africa, respectively. The results indicated a rapid increasing pattern from bare land (desert) to irrigated systems (Egyptian site) but smaller and stable cropland changes in smallholder and farming-pastoral ecotones (Ethiopian and South African site), where limited land was still available for an expansion of agricultural area. This study highlights the potential application of time-series Landsat data in documenting and contributing missing cropland distribution information required for assessing and solving food security in Africa.
Induced earthquake and liquefaction hazards in Oklahoma, USA: Constraints from InSAR Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-14 William D. Barnhart, William L. Yeck, Daniel E. McNamara
Oklahoma experienced three earthquakes of Mw5.0 or greater in 2016: the 13-Feb. Fairview earthquake (Mw5.1), the 03-Sep. Pawnee earthquake (Mw5.8), and the 07-Nov. Cushing earthquake (Mw5.0). These events are the first earthquakes in the state exceeding Mw5.0 since the 2011 Mw5.7 Prague earthquake and likely result from wide-scale deep fluid-injection. We use interferometric synthetic aperture radar (InSAR) observations to quantify the magnitude and location of surface deformation associated with these three events, determine the depth ranges of fault slip, and assess the spatial relationship between fault slip and well-calibrated mainshock and aftershock locations. We also include newly reported, calibrated event locations for the Cushing earthquake. We find that the Pawnee earthquake ruptured within the crystalline basement with the shallowest slip occurring at depths of 3.1–4.3 km. We find a similar, though shallower, crystalline basement source for the Cushing earthquake with a minimum depth to slip of 1.6–2.3 km. Despite the smaller magnitude of the Cushing earthquake, it generated anomalously high ground motions and damage compared to the larger Pawnee and Fairview earthquakes. We postulate that the shallow source of the Cushing earthquakes provides one explanation for the higher than expected ground motions. The Fairview earthquake generated no detectable co-seismic displacements, which is consistent with a relatively deep earthquake source (~8.5 km). We do, however, identify a 16 km stretch of floodplain where widespread liquefaction occurred in response to the Fairview earthquake, and where 30 gas production wells were exposed to surface displacements exceeding 5 cm. Consequently, the depth to crystalline basement, which limits the depth of injection-induced earthquakes in Oklahoma, and the potential for liquefaction are important factors in assessing shaking risk in the central United States.
Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-12 Lianfa Li, Jiehao Zhang, Xia Meng, Ying Fang, Yong Ge, Jinfeng Wang, Chengyi Wang, Jun Wu, Haidong Kan
Vegetation and soil moisture inversion from SAR closure phases: First experiments and results Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-11 Francesco De Zan, Giorgio Gomba
The inversion of soil moisture from Synthetic Aperture Radar (SAR) closure phases is intrinsically plagued by ambiguities that affect the moisture order. This work shows a characterization of the ambiguities and a way to solve for them with the help of interferometric coherence. This allows to properly constrain the inversion and to retrieve the moisture signal. A data set of ALOS-2/PALSAR-2 L-band images is used as an example of successful inversion at the scene level, with sub-kilometer resolution. The results are validated with soil moisture products based on ASCAT and show a high degree of correlation. The raw moisture derived by the algorithm could be immediately used to correct SAR interferometric phases; however, for applications that need absolute moisture levels, a calibration step is likely necessary. Unexpectedly, a good performance was observed over forested areas, which suggests a sensitivity of closure phases to tree moisture; at the same time, over pastures and agricultural fields the closure phase signal was found relatively weak. Additional research is needed to evaluate the applicability of the same measurements principle to shorter wavelengths and exploitation of potential synergies with backscatter and polarimetric information.
Satellite detection of an unusual intrusion of salty slope water into a marginal sea: Using SMAP to monitor Gulf of Maine inflows Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-11 Semyon A. Grodsky, Douglas Vandemark, Hui Feng, Julia Levin
Satellite salinity from the Soil Moisture Active Passive (SMAP) mission and in situ observations are used to diagnose the source of a significant increase in warm and salty surface water entering the Gulf of Maine (GoM) in the winter of 2017–2018. SMAP salinity anomaly data indicate that this event was related to a salty feature that moved along the northwestern Atlantic shelf break from near the Grand Banks southwest towards the GoM over eight months before entering the Gulf in December 2017 to January 2018. Satellite altimetry, sea surface salinity, and sea surface temperature data suggest that, before entering the GoM, the salty feature interacted with Gulf Stream meanders and eddies several times, helping to sustain the water mass. It is likely that feature interactions with a warm Gulf Stream meander took place in the fall of 2017, helping to advect high salinity water onto the shelf and then into the GoM as a surface trapped feature in late fall 2017. According to satellite salinity data, this episode led to significant salinification (about 1 psu) in the northeastern GoM. Interior GoM buoy salinity data agree in showing four months of increased GoM salinity of the upper 50 m starting in November 2017. Buoy T/S analyses characterize this surface inflow as modified warm Atlantic slope water, typically seen only below 100 m and previously unobserved at the surface in the 15-year buoy record. This new salty water circulated cyclonically along the GoM coastline and mixed into the deeper Gulf through February 2018. Its intrusion may have also enhanced the cyclonic winter circulation in the Gulf. GoM surface salinity anomalies ended abruptly in early March 2018 coincident with the occurrence of a bomb cyclone and its associated strong upper ocean mixing.
Significance of dual polarimetric synthetic aperture radar in biomass retrieval: An attempt on Sentinel-1 Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-08 Shoba Periasamy
The study investigates the suitability of Sentinel-1 data product of C-band frequency (5.36 GHz) in the estimation of terrestrial biomass. The experiment was carried out in the Perambular District, Tamil Nadu, India. The model DPSVI (Dual Polarization SAR Vegetation Index) was proposed based on the pattern of scatter plot constructed between the backscattering coefficient of VV (σvvo) and VH (σvho) imageries in which the pixels representing the surface features such as vegetation, soil, and water bodies were distributed according to the theory ‘Degree of Depolarization (DOP)’. The model was developed by proposing and integrating three crucial parameters (i) Inverse Dual-Pol Diagonal Distance (IDPDD) (ii) Vertical Dual De-polarization Index (VDDPI) and (iii) σvho which are highly influential towards biomass extraction. The model was executed for two different seasons (wet and dry) from 2015 to 2017. The resultant output was tested with Normalized Differential Vegetation Index (NDVI) derived from Sentinel 2, and the field observed Above Ground Biomass (AGB) from 50 sampling locations to demonstrate the theoretical and in-situ potential of the proposed model. The resultant product of the DPSVI has shown the acceptable R2 value of 0.75 for the dry season and 0.73 for the wet season with NDVI and got R2 value of 0.73 for the dry and R2 value of 0.70 for the wet season with AGB which manifested that the proposed model was an effective indicator of terrestrial vegetation irrespective of seasons.
Robust quantification of riverine land cover dynamics by high-resolution remote sensing Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-07 Gillian Milani, Michele Volpi, Diego Tonolla, Michael Doering, Christopher Robinson, Mathias Kneubühler, Michael Schaepman
Floodplain areas belong to the most diverse, dynamic and complex ecological habitats of the terrestrial portion of the Earth. Spatial and temporal quantification of floodplain dynamics is needed for assessing the impacts of hydromorphological controls on river ecosystems. However, estimation of land cover dynamics in a post-classification setting is hindered by a high contribution of classification errors. A possible solution relies on the selection of specific information of the change map, instead of increasing the overall classification accuracy. In this study, we analyze the capabilities of Unmanned Aerial Systems (UAS), the associated classification processes and their respective accuracies to extract a robust estimate of floodplain dynamics. We show that an estimation of dynamics should be built on specific land cover interfaces to be robust against classification errors and should include specific features depending on the season-sensor coupling. We use five different sets of features and determine the optimal combination to use information largely based on blue and infrared bands with the support of texture and point cloud metrics at leaf-off conditions. In this post-classification setting, the best observation of dynamics can be achieved by focusing on the gravel-water interface. The semi-supervised approach generated error of 10% of observed changes along highly dynamic reaches using these two land cover classes. The results show that a robust quantification of floodplain land cover dynamics can be achieved by high-resolution remote sensing.
Enhancing digital elevation models for hydraulic modelling using flood frequency detection Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-07 Georgina Ettritch, Andy Hardy, Landing Bojang, Dónall Cross, Peter Bunting, Paul Brewer
Medium-resolution DEMs have limited applicability to flood mapping in large river systems within data sparse regions such as Sub-Saharan Africa. We present a novel approach for the enhancement of the SRTM (30 m) Digital Elevation Model (DEM) in The Gambia, West Africa: A time-series analysis of flood frequency and land cover was used to delineate differences in the vertical limits between morphological units within an alluvial floodplain. Combined with supplementary river stage data and vegetation removal techniques, these methods were used to improve the estimation of bare-earth terrain in flood modelling applications for a region with no access to high-resolution alternatives. The results demonstrate an improvement in floodplain topography for the River Gambia. The technique allows the reestablishment of small-scale complex morphology, instrumental in the routing of floodwater within a noise-filled DEM. The technique will be beneficial to flood-risk modelling applications within data sparse regions.
The Chlorophyll Fluorescence Imaging Spectrometer (CFIS), mapping far red fluorescence from aircraft Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-08 Christian Frankenberg, Philipp Köhler, Troy S. Magney, Sven Geier, Peter Lawson, Mark Schwochert, James McDuffie, Darren T. Drewry, Ryan Pavlick, Andreas Kuhnert
The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO2 but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2’s O2 A-band, covering 757–775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-σ precision errors of 0.5–0.7 W/m2/sr/μm for typical SIF retrievals, which can be reduced to <0.2 W/m2/sr/μm when individual data is gridded at 30 m spatial resolution. The 30 m resolution also enabled direct comparison with the Crop Data Layer from the National Agricultural Statistics Service as well as Landsat imagery (NDVI, EVI, Tskin), taken just a day prior to the CFIS overflights. Results show consistently higher vegetation indices and SIF values over soy fields compared to corn, likely due to the respective phenological stage, which might already have affected chlorophyll content and canopy structure (August 15, 2016). While this work is intended to highlight the technical capabilities and performance of CFIS, the comparisons against Landsat and crop types provide insights into how CFIS can be used to study mechanisms related to photosynthesis at fine spatial scales, with the fidelity needed to obtain un-biased SIF retrievals void of atmospheric correction.
Exploitation of error correlation in a large analysis validation: GlobCurrent case study Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-06 Richard E. Danielson, Johnny A. Johannessen, Graham D. Quartly, Marie-Hélène Rio, Bertrand Chapron, Fabrice Collard, Craig Donlon
An assessment of variance in ocean current signal and noise shared by in situ observations (drifters) and a large gridded analysis (GlobCurrent) is sought as a function of day of the year for 1993–2015 and across a broad spectrum of current speed. Regardless of the division of collocations, it is difficult to claim that any synoptic assessment can be based on independent observations. Instead, a measurement model that departs from ordinary linear regression by accommodating error correlation is proposed. The interpretation of independence is explored by applying Fuller's (1987) concept of equation and measurement error to a division of error into shared (correlated) and unshared (uncorrelated) components, respectively. The resulting division of variance in the new model favours noise. Ocean current shared (equation) error is of comparable magnitude to unshared (measurement) error and the latter is, for GlobCurrent and drifters respectively, comparable to ordinary and reverse linear regression. Although signal variance appears to be small, its utility as a measure of agreement between two variates is highlighted.Sparse collocations that sample a dense (high resolution) grid permit a first order autoregressive form of measurement model to be considered, including parameterizations of analysis-in situ error cross-correlation and analysis temporal error autocorrelation. The former (cross-correlation) is an equation error term that accommodates error shared by both GlobCurrent and drifters. The latter (autocorrelation) facilitates an identification and retrieval of all model parameters. Solutions are sought using a prescribed calibration between GlobCurrent and drifters (by variance matching). Because the true current variance of GlobCurrent and drifters is small, signal to noise ratio is near zero at best. This is particularly evident for moderate current speed and for the meridional current component.
A spatial and temporal analysis of forest dynamics using Landsat time-series Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-05 Trung H. Nguyen, Simon D. Jones, Mariela Soto-Berelov, Andrew Haywood, Samuel Hislop
Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-05 Shenglei Wang, Junsheng Li, Bing Zhang, Evangelos Spyrakos, Andrew N. Tyler, Qian Shen, Fangfang Zhang, Tiit Kuster, Moritz K. Lehmann, Yanhong Wu, Dailiang Peng
Eutrophication of inland waters is considered a serious global environmental problem. Satellite remote sensing (RS) has been established as an important source of information to determine the trophic state of inland waters through the retrieval of optically active water quality parameters such as chlorophyll-a (Chl-a). However, the use of RS techniques for assessment of the trophic state of inland waters on a global scale is hindered by the performance of retrieval algorithms over highly dynamic and complex optical properties that characterize many of these systems. In this study, we developed a new RS approach to assess the trophic state of global inland water bodies based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and the Forel-Ule index (FUI). First, the FUI was calculated from MODIS data by dividing natural water colour into 21 indices from dark blue to yellowish-brown. Then the relationship between FUI and the trophic state index (TSI) was established based on in-situ measurements and MODIS products. The water-leaving reflectance at 645 nm band was employed to distinguish coloured dissolved organic matter (CDOM)-dominated systems in the FUI-based trophic state assessment. Based on the analysis, the FUI-based trophic state assessment method was developed and applied to assess the trophic states of 2058 large inland water bodies (surface area >25 km2) distributed around the world using MODIS data from the austral and boreal summers of 2012. Our results showed that FUI can be retrieved from MODIS with a considerable accuracy (92.5%, R2 = 0.92) by comparing with concurrent in situ measurements over a wide range of lakes, and the overall accuracy of the FUI-based trophic state assessment method is 80.0% (R2 = 0.75) validated by an independent dataset. Of the global large water bodies considered, oligotrophic large lakes were found to be concentrated in plateau regions in central Asia and southern South America, while eutrophic large lakes were concentrated in central Africa, eastern Asia, and mid-northern and southeast North America.
Optimising NDWI supraglacial pond classification on Himalayan debris-covered glaciers Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-04 C. Scott Watson, Owen King, Evan S. Miles, Duncan J. Quincey
A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-05 Rosa Coluzzi, Vito Imbrenda, Maria Lanfredi, Tiziana Simoniello
Landslide state of activity maps by combining multi-temporal A-DInSAR (LAMBDA) Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-17 Roberta Bonì, Massimiliano Bordoni, Alessio Colombo, Luca Lanteri, Claudia Meisina
In this paper, a new methodology was developed to automatically update Landslide state of Activity Maps by combining multi-temporal A-DInSAR data (LAMBDA). LAMBDA procedure was tested using ERS-1/2 (1992–2000), Radarsat-1/2 (2003–2009) and COSMO-SkyMed data (2011–2014) over an area of 2199 km2 located in Alps context of Piedmont region (north-western Italy). For the first time, a multidimensional landslide activity matrix was implemented to update the landslide state of activity during the monitored time span. For the definition of the state of activity, the representative velocity of each landslide was divided by the standard deviation of the velocities along the slope of the whole dataset. Thus, a common stability threshold of ±1 was introduced for multi-sensors A-DInSAR data, allowing to distinguish a phenomenon with stable targets (PS-DS) or unstable PS-DS. By combining activity classes estimated during different time spans allows to determine if a phenomenon is active, reactivated, or dormant. Furthermore, an innovative confidence degree assessment was carried out to verify the reliability of the procedure, by considering the measuring points distribution and the variability of the movements for each landslide. The results were validated using the landslide inventory of the study area and in-situ monitoring systems for representative case studies. Thanks to this approach an updated state of activity until 2014 was assigned to 507 landslides out the 1657 which were previously mapped in the study area.
Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data Remote Sens. Environ. (IF 6.457) Pub Date : 2018-09-01 Pablo Crespo-Peremarch, Piotr Tompalski, Nicholas C. Coops, Luis Ángel Ruiz
The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine full-waveform metrics to characterize understory vegetation attributes at two different scales (3.75 m square subplots and circular plots with a radius of 15 m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2 = 0.957, maximum height: R2 = 0.771, cover: R2 = 0.871, and volume: R2 = 0.951). The proportion of explained variance was slightly lower at 3.75 m side cells (mean height: R2 = 0.633, maximum height: R2 = 0.470, cover: R2 = 0.581, and volume R2 = 0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.
Deriving three dimensional reservoir bathymetry from multi-satellite datasets Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-31 Augusto Getirana, Hahn Chul Jung, Kuo-Hsin Tseng
We evaluate different techniques that rebuild reservoir bathymetry by combining multi-satellite imagery of surface water elevation and extent. Digital elevation models (DEMs) are processed in two distinct ways in order to determine 3-D reservoir bathymetry. They are defined as (a) linear extrapolation and (b) linear interpolation. The first one linearly extrapolates the land slope, defining the bottom as the intersection of all extrapolated lines. The second linearly interpolates the uppermost and lowermost pixels of the reservoir's main river, repeating the process for all other tributaries. A visible bathymetry, resulting from the combination of radar altimetry and water extent masks, can be coupled with the DEM, improving the accuracy of techniques (a) and (b). Envisat- and Altika-based altimetric time series is combined to a Landsat-based water extent database over the 2002–2016 period in order to generate the visible bathymetry, and topography is derived from the 3-arcsec HydroSHEDS DEM. Fourteen 3-D bathymetries derived from the combination of these techniques and datasets, plus the inclusion of upstream and downstream riverbed elevations, are evaluated over Lake Mead. Accuracy is measured using ground observations, and show that metrics improve as a function of added data requirement and processing. Best bathymetry estimates are obtained when the visible bathymetry, linear extrapolation technique and riverbed elevation are combined. Water storage variability is also evaluated and shows that best results are derived from the aforementioned combination. This study contributes to our understanding and representation of reservoir water impoundment impacts on the hydrological cycle.
Preliminary assessment of 20-m surface albedo retrievals from sentinel-2A surface reflectance and MODIS/VIIRS surface anisotropy measures Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-30 Zhan Li, Angela Erb, Qingsong Sun, Yan Liu, Yanmin Shuai, Zhuosen Wang, Peter Boucher, Crystal Schaaf
Satellite-based retrievals of land surface albedo at 20-m resolution are generated by coupling the surface reflectances from the recently launched Sentinel-2A satellite with surface anisotropy information (as described by Bidirectional Reflectance Distribution Function, BRDF) from either the MODerate-resolution Imaging Spectroradiometer (MODIS) or the Visible Infrared Imaging Radiometer Suite (VIIRS). The intrinsic black-sky albedo (BSA) and white-sky albedo (WSA) values of the surface are derived at the six shortwave spectral bands of Sentinel-2A's Multi Spectral Instrument (MSI). A specific set of narrow-to-broadband conversion coefficients is derived from radiative transfer simulations and presented for the generation of broadband albedos. Initial evaluation uses well-calibrated ground-based albedo measurements by pyranometers mounted on the towers at seven sites of the Surface Radiation Network (SURFRAD). Over those sites where pyranometer measurement footprints are not spatially representative of the landscape covered by the satellite pixels (i.e., spatially nonrepresentative) at the grid scales (500 m to 1 km) of the MODIS and VIIRS products, the finer-resolution Sentinel-2A albedos manifested a pronounced decrease in root mean squared error (RMSE) and mean bias in the evaluation against the ground-based data as compared to the coarser resolution albedo products of MODIS and VIIRS. This decrease occurs because the 20-m Sentinel-2A albedo values are better able to resolve the spatial details of surface albedo within the ground-based instrument footprints than the coarser resolution sensors. This preliminary evaluation also demonstrates the consistency of the Sentinel-2A albedo results whether using the MODIS BRDF or the VIIRS BRDF products for the surface anisotropy information. The RMSEs and mean biases of the Sentinel-2A albedos over all the seven validation sites are both within the accuracy requirement of ±0.05 absolute albedo units for satellite derived albedo products. This study, to generate Sentinel-2A MSI albedo with either MODIS or VIIRS BRDFs, extends previous efforts of Landsat TM, ETM+ and OLI albedo and enhances the continuity of finer-resolution albedo data. Such long-term and higher resolution records of surface albedo improve the investigations into the changes and drivers of local/regional surface energy balance over heterogeneous regions and increasingly fragmented landscapes across the globe due to natural and human-induced land cover changes.
Signature of the Agulhas Current in high resolution satellite derived wind fields Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-29 M. Krug, D. Schilperoort, F. Collard, M.W. Hansen, M. Rouault
5 years of Systematic Envisat Advanced Synthetic Aperture Radar (ASAR) acquisitions and 8 years of observations from the Jason 2 altimeter are used to investigate the signature of the Agulhas Current on high resolution (between 1 and 5 km) satellite-derived winds. The satellite wind observations are analysed together with co-located ocean current, Sea Surface Temperature (SST) and significant wave height information. Satellite-derived winds cannot be considered current relative over the Agulhas Current. SAR and altimeter winds increase in magnitude over the Agulhas Current in all up-, down- and cross-current wind conditions, with contributions from both the ocean surface current and SST. When winds blow against the current, strong accelerations in satellite winds are observed at the Agulhas Current's inshore front, with the strongest winds observed 10 km offshore from the location of maximum SST gradient. The strong SST gradient appears to drive a thermal wind with a magnitude of about 1 m/s and which intensifies/abates the predominant alongfront winds at the Agulhas Current's northern wall. The SAR dataset shows abnormally high increases in wind speeds during up-current conditions in comparisons to the Jason-2 derived winds. We argue that these differences are caused by the inability of the CMOD_type algorithms to taken into account the wave field and provide accurate wind speed estimates in conditions where wave height exceed the expected value for a given wind speed. Our analysis suggests that wave-current interactions in regions of strong current shear produce enhanced sea surface roughness signatures and lead to artificially high estimates of SAR-derived wind speeds. There is future potential in using high resolution SAR imagery to map strong ocean current fronts and thus improve our ability to monitor ocean surface currents from space.
Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-29 Ning Zhang, Xiaoli Zhang, Guijun Yang, Chenghao Zhu, Langning Huo, Haikuan Feng
The increased frequency and intensity of insect-induced forest disturbances necessitates effective methods to precisely monitor and map the degree of disaster. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technology for surveying and monitoring forest health. In this article, a novel framework that utilizes a UAV-based hyperspectral image is proposed to identify the degree of damage caused by Dendrolimus tabulaeformis Tsai et Liu (D.tabulaeformis) in Jianping county of Liaoning province, China. First, data reduction of the hyperspectral image is achieved by comparing three waveband selection algorithms: principal components analysis (PCA), the successive projection algorithm (SPA), and the instability index between classes (ISIC). On this basis, a joint algorithm, ISIC-SPA, which demonstrates the best waveband selection efficiency and good cross-validation accuracy, is proposed. ISIC-SPA is used to select only three sensitive wavebands from 125 original wavebands with a root mean square error of 0.1535. Then, according to analysis of the three sensitive wavebands' reflectance and the corresponding defoliation rate, the piecewise index (PI, B(710 + 738 - 522)) was constructed and the threshold of PI was found to divide the defoliation level. Finally, a piecewise partial least-squares regression model was established to quantitatively estimate the defoliation using the optimal wavebands to identify and demarcate the damage level to individual trees. The assessment accuracy of damage caused by D.tabulaeformis at the tree level reached 95.23% using the ISIC-SPA-P-PLSR framework.
Anthropogenic marine debris over beaches: Spectral characterization for remote sensing applications Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-27 Tomás Acuña-Ruz, Diego Uribe, Richard Taylor, Lucas Amézquita, María Cristina Guzmán, Javier Merrill, Paula Martínez, Leandro Voisin, Cristian Mattar B.
Anthropogenic Marine Debris (AMD) is one of the most important pollutants in the oceans. Millions of tons of debris across oceans have created a critical environmental problem. This study presents a novel method aimed to improve the identification of macroplastics through remote sensing over beaches, combining AMD hyperspectral laboratory characterization and digital supervised classification in high spatial resolution imagery. Several samples were collected from the Chiloé Island beaches, Chile. Spectral signature samples and physical properties were assessed through laboratory work. HyLogger3® (CSIRO), PS-300 Apogee and ASD Field Spec hyperspectral systems were used to characterize each sample. Using those measurements, a spectral library was generated by processing, filtering and sorting the spectral data gathered, determining distinctive spectral bands for digital classification. By using this spectral library, a digital classification method was implemented over World-View 3 imagery, covering the three beaches selected as test sites. Distinct classification methods and geospatial analyses were applied to determine land cover composition, aimed for the detection of Styrofoam and the rest of anthropogenic marine debris. Four field campaigns were carried out to validate the AMD classification and mass retrievals, performed on >300 ground based points. The AMD hyperspectral library was successfully applied for an AMD digital classification in satellite imagery. Support Vector Machine method showed the best performance, resulting in an overall accuracy equivalent to 88% and over 50 tons of debris estimated on the pilot beaches. These results prove the feasibility of quantifying macro-AMD through the integration of hyperspectral laboratory measurements and remote sensing imagery, allowing to estimate anthropogenic influence on natural ecosystems and providing valuable information for further development of the methodology and sustainable AMD management.
Subpixel variability and quality assessment of satellite sea surface temperature data using a novel High Resolution Multistage Spectral Interpolation (HRMSI) technique Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-24 Sandra L. Castro, Lucas A. Monzon, Gary A. Wick, Ryan D. Lewis, Greg Beylkin
A novel interpolation technique is applied to assessment of the quality of sea surface temperature (SST) observations and quantitative analysis of the subpixel variability within satellite footprints of different size. Using retrieved satellite data as input, the new, global, multistage interpolation technique generates a trigonometric polynomial, providing a representation of the underlying physical SST field in functional form. The resulting interpolating function can be efficiently and accurately evaluated anywhere within the domain over which it was derived and its moments calculated to estimate the mean and variance of the field over desired sub-regions. Application of the technique is demonstrated for SST retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), Spinning Enhanced Visible and Infrared Imager (SEVIRI), and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensors. Comparison of the functional form with the data from which it was derived demonstrates how the technique can potentially help to identify small observational artifacts such as MODIS scan striping and residual cloud contamination. Integrals of the interpolating functions over successively larger spatial scales successfully emulate the retrieved SST at the different effective spatial resolutions and the second moments are consistent with the direct sample variances, and hence representative of the spatial SST variability of the available finer-resolution observations over the coarser scales. Using the approach, the variability of 1-km-resolution SST observations on open ocean grids of both 5- and 25-km resolution is found to be ~0.07 K. In regions of sharper gradients such as associated with strong localized diurnal warming, the variability within 25-km-resolution grids increases to as much as 0.4 K for sampling at 1-km resolution. The variability of 1-km observations on a 25-km-resolution grid is about 2.4 times greater than that on a 5-km-resolution grid. Broader application of the technique globally could help better quantify regional variations in the spatial variability, which would subsequently improve uncertainty estimates for existing satellite-based SST products.
Extending RAPID model to simulate forest microwave backscattering Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-24 Huaguo Huang, Zhiyu Zhang, Wenjian Ni, Linna Chai, Wenhan Qin, Guang Liu, Donghui Xie, Lingmei Jiang, Qinhuo Liu
Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-24 Michael Marshall, Kevin Tu, Jesslyn Brown
Earth observation data are increasingly used to provide consistent eco-physiological information over large areas through time. Production efficiency models (PEMs) estimate Gross Primary Production (GPP) as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from Earth observation. GPP can be summed over the growing season and adjusted by a crop-specific harvest index to estimate yield. Although PEMs have many advantages over other crop yield models, they are not widely used, because performance is relatively poor. Here, a new PEM is presented that addresses deficiencies for macro-scale application: Production Efficiency Model Optimized for Crops (PEMOC). It was developed by optimizing functions from the literature with GPP estimated by eddy covariance flux towers in the United States. The model was evaluated using newly developed Earth observation products and county-level yield statistics for major crops. PEMOC generally performed better at the field and county level than another commonly used PEM, the Moderate Resolution Imaging Spectroradiometer GPP (MOD17). PEMOC and MOD17 estimates of GPP had an R2 and root mean squared error (RMSE) over the growing season of 0.71–0.89 (9.87–17.47 g CO2 d−1) and 0.59–0.83 (6.86–22.20 g CO2 d−1) with flux tower GPP. PEMOC produced R2s and RMSE of 0.70 (0.52), 0.60 (0.61), and 0.62 (0.59), while MOD17 produced R2s and RMSE of 0.65 (0.57), 0.53 (0.66), and 0.65 (0.57) with corn, soybean, and winter wheat crop yield anomalies. The sample size of rice was small, so yields were compared directly. PEMOC and MOD17 produced R2s and RMSE of 0.53 (3.42 t ha−1) and 0.40 (4.89 t ha−1). The most sizeable model improvements were seen for C3 and C4 crops during emergence/senescence and peak season, respectively. These improvements were attributed to C3 and C4 partitioning, optimized temperature and moisture constraints, and an evapotranspiration-based soil moisture index.
A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-24 Ruyin Cao, Yang Chen, Miaogen Shen, Jin Chen, Ji Zhou, Cong Wang, Wei Yang
High-quality Normalized Difference Vegetation Index (NDVI) time-series data are important for many regional and global ecological and environmental applications. Unfortunately, residual noise in current NDVI time-series products greatly hinders their further applications. Several noise-reduction methods have been proposed during the past two decades, but two important issues remain to be resolved. First, the methods usually perform poorly for cases of continuous missing data in the NDVI time series. Second, they generally assume negatively biased noise in the NDVI time series and thus erroneously raise some local low NDVI values in certain cases (e.g., the harvest period for multi-season crops).We therefore developed a new noise-reduction algorithm called the Spatial-Temporal Savitzky-Golay (STSG) method. The new method assumes discontinuous clouds in space and employs neighboring pixels to assist in the noise reduction of the target pixel in a particular year. The relationship between the NDVI of neighboring pixels and that of the target pixel was obtained from multi-year NDVI time series thanks to the accumulation of NDVI data over many years, which would have been impossible a decade ago. We tested STSG on 16-day composite MODIS NDVI time-series data from 2001 to 2016 in regions of mainland China and 11 phenology camera sites in North American. The results showed that STSG performed significantly better compared with four previous widely used methods (i.e., the Asymmetric Gaussian, Double Logistic, Fourier-based, and Savitzky-Golay filter methods). One obvious advantage was that STSG was able to address the problem of temporally continuous NDVI gaps. STSG effectively increased local low NDVI values and simultaneously avoided overcorrecting low NDVI values during the crop harvest period. In addition, implementing STSG required only raw MODIS NDVI time-series products without any additional burden of data requirements. All of these advantages make STSG a promising noise-reduction method for generating high-quality NDVI time-series data.
Disentangling the causes of canopy height increase in managed and unmanaged temperate deciduous forests using multi-temporal airborne laser scanning Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-23 Jean-Francois Senécal, Frédérik Doyon, Christian Messier
Airborne laser scanning (ALS) is a tool that can be used to monitor canopy height changes when data acquisitions are done at successive times. However, ALS is subject to some limitations in forested environments. One of these is that height growth measured between two times can be due to vertical crown growth of trees or to lateral growth of branches. The latter process gives canopy height change values unrelated to actual stem elongation. Lateral growth is a source of uncertainties in height change analyses because canopy height models do not contain information on the source of the growth.Lateral infilling of open space in the canopy and vertical crown growth are important processes in the gap dynamics of temperate deciduous forests. Small gaps are expected to close more often by lateral growth of branches while larger gaps can sometimes close by the vertical crown growth of seedling and saplings. Yet, few studies have examined growth patterns in and out of gaps in temperate deciduous forests.We studied both unmanaged and managed temperate deciduous forests in southern Quebec, Canada using a multi-temporal ALS dataset. A Bayesian stochastic model of three-dimensional dynamics was developed to classify whether measured height growth was due to lateral growth or vertical crown growth. A generalized additive model was also constructed to relate vertical crown growth to the height of trees.Between 10.6% and 13.7% of the canopy height model's pixels changed value due to lateral growth. There was no clear temporal trend for the frequencies of lateral growth in the managed forests chronosequence. Frequencies of lateral growth were also comparable between unmanaged and managed sites. Treetops of dominant trees grew slowly while crown edges of those trees grew both vertically and laterally. Our results show that canopy structure recovery in managed forests occurs through faster vertical crown growth of trees <10 m tall and lower tree mortality compared to unmanaged forests.
Fuel load mapping in the Brazilian Cerrado in support of integrated fire management Remote Sens. Environ. (IF 6.457) Pub Date : 2018-08-22 Jonas Franke, Ana Carolina Sena Barradas, Marco Assis Borges, Máximo Menezes Costa, Paulo Adriano Dias, Anja A. Hoffmann, Juan Carlos Orozco Filho, Arturo Emiliano Melchiori, Florian Siegert
The Brazilian Cerrado is considered to be the most species-rich savannah region in the world, covering ~2 million km2. Uncontrolled late season fires promote deforestation, produce greenhouse gases (~25% of Brazil's land-use related CO2 emissions between 2003 and 2005) and are a major threat to the conservation of biodiversity in protected areas. Governmental institutions therefore implemented early dry season (EDS) prescribed burnings as part of integrated fire management (IFM) in protected areas of the Cerrado, with the aim to reduce the area and severity of late dry season (LDS) fires. The planning and implementation of EDS prescribed burning is supported by satellite-based geo-information on fuel conditions, derived from Landsat 8 and Sentinel-2 data. The Mixture Tuned Matched Filtering algorithm was used to analyse the data, and the relationship between the resulting matched fractions (dry vegetation, green vegetation and soil) and in situ surface fuel samples was assessed. The linear regression of in situ data versus matched filter scores (MF scores) of dry vegetation showed an r2 of 0.81 (RMSE = 0.15) and in situ data versus MF scores of soil showed an r2 of 0.65 (RMSE = 0.38). To predict quantitative fuel load, a multiple linear regression analysis was carried out with MF scores of NPV and soil as predictors (adjusted r2 = 0.86; p < 0.001; standard error = 0.075). The fuel load maps were additionally evaluated by fire managers while planning EDS prescribed burning campaigns. The fuel load mapping approach has proven to be an effective tool for integrated fire management by improving the planning and implementation of prescribed burning, promoting pyrodiversity, prioritising fire suppression and evaluating fire management efforts to meet overall conservations goals. National and state level authorities have successfully institutionalized the approach and it was incorporated into IFM policies in Brazil.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.
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