Heterogeneous decadal glacier downwasting at the Mt. Everest (Qomolangma) from 2000 to ~ 2012 based on multi-baseline bistatic SAR interferometry Remote Sens. Environ. (IF 6.265) Pub Date : 2018-01-05 Gang Li, Hui Lin, Qinghua Ye
Remote sensing based geodetic observations can be used as alternatives data to map glacier height changes because the harsh environment complicates in situ observations. In this study, we analysed five pairs of X-band bistatic TerraSAR-X/TanDEM-X images from 2011 and 2014 for Mt. Everest (Qomolangma). Glacier height changes were derived using the D-InSAR method respect to SRTM DEM of 2000. An iterative D-InSAR method using multi-baseline bistatic SAR interferograms was employed, which increased reliability and accuracy of glacier height changing observations. From 2000 to ~ 2012, the geodetic glacier mass balance for the Mt. Everest and the surrounding region was − 0.38 ± 0.04 m w.e. a− 1. The spatial pattern of the glacier mass loss was heterogeneous. The regional heterogeneity may possibly reflect debris-covering rates, terminating type, temperature rising rates and glacier flow rates. Comparison to long-period geodetic glacier mass balance data provided by previous studies since 1970 revealed that our results showed more rapid increases in the glacier mass loss rates after 2000 in the area around Khumbu Glacier in the southern slope of the Mt. Everest, whereas glacier mass loss rates kept stable in the Rongbuk Catchment at its northern slope.
Selection of HyspIRI optimal band positions for the earth compositional mapping using HyTES data Remote Sens. Environ. (IF 6.265) Pub Date : 2018-01-05 Arshad Iqbal, Saleem Ullah, Noora Khalid, Waqar Ahmad, Ijaz Ahmad, Muhammad Shafique, Glynn C. Hulley, Dar A. Roberts, Andrew K. Skidmore
The National Aeronautics and Space Administration (NASA) has proposed the launch of a new space-borne sensor called HyspIRI (Hyperspectral and Infrared Imager) which will cover the spectral range from 0.4–12 μm. Two instruments will be mounted on HyspIRI platform: 1) a hyperspectral instrument which can sense earth surface between 0.4 and 2.5 μm at 10 nm intervals and 2) a multispectral infrared sensor will acquire images between 3 and 12 μm in eight spectral bands (one in Mid infrared (MIR) and seven in Thermal Infrared (TIR)). The TIR spectral wavebands will be positioned based on their importance in various applications. This study aimed to identify HyspIRI optimal TIR wavebands position for earth compositional mapping. A Genetic Algorithm coupled with the Spectral Angle Mapper (GA-SAM) was used as a spectral bands selector. High dimensional HyTES (Hyperspectral Thermal Emission Spectrometer) emissivity spectra comprised of 202 spectral bands of Cuprite and Death Valley regions were used to select meaningful subsets of bands for earth compositional mapping. The GA-SAM was trained for fifteen mineral classes and the algorithms were run iteratively 50 times. High calibration (> 95%) and validation (> 90%) accuracies were achieved with a limited number (seven) of spectral bands selected by GA-SAM. The knowledge of important band positions will help the scientists of the HyspIRI group to place spectral bands in regions where accuracies of earth compositional mapping can be enhanced.
Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery Remote Sens. Environ. (IF 6.265) Pub Date : 2018-01-03 Masoud Mahdianpari, Bahram Salehi, Fariba Mohammadimanesh, Brian Brisco, Sahel Mahdavi, Meisam Amani, Jean Elizabeth Granger
Wetlands provide a wide variety of environmental services globally and detailed wetland inventory maps are always necessary to determine the conservation strategies and effectively monitor these productive ecosystems. During the last two decades, satellite remote sensing data have been extensively used for wetland mapping and monitoring worldwide. Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex and multi-dimensional data, which has high potential to discriminate different land cover types. However, despite significant improvements to both information content in PolSAR imagery and advanced classification approaches, wetland classification using PolSAR data may not provide acceptable classification accuracy. This is because classification accuracy using PolSAR imagery strongly depends on the polarimetric features that are incorporated into the classification scheme. In this paper, a novel feature weighting method for PolSAR imagery is proposed to increase the classification accuracy of complex land cover. Specifically, a new coefficient is determined for each element of the coherency matrix by integration of Fisher Linear Discriminant Analysis (FLDA) and physical interpretation of the PolSAR data. The proposed methodology was applied to multi-temporal polarimetric C-band RADARSAT-2 data in the Avalon Peninsula, Deer Lake, and Gros Morne pilot sites in Newfoundland and Labrador, Canada. Different combinations of input features, including original PolSAR features, polarimetric decomposition features, and modified coherency matrix were used to evaluate the capacity of the proposed method for improving the classification accuracy using the Random Forest (RF) algorithm. The results demonstrated that the modified coherency matrix obtained by the proposed method, Van Zyl, and Freeman-Durden decomposition features were the most important features for wetland classification. The fine spatial resolution maps obtained in this study illustrate the distribution of terrestrial and aquatic habitats for the three wetland pilot sites in Newfoundland using the modified coherency matrix and other polarimetric features. The classified maps provide valuable baseline data for effectively monitoring climate and land cover changes, and support further scientific research in this area.
VIIRS-derived ocean color product using the imaging bands Remote Sens. Environ. (IF 6.265) Pub Date : 2018-01-03 Menghua Wang, Lide Jiang
A technique and implementation for deriving ocean color product using the imaging band (I-band) measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) have been developed. The three VIIRS-SNPP image bands (638, 862, and 1600 nm) with spatial resolution of 375 m are much better than the VIIRS moderate (M) resolution bands of 750 m, which have been used for routinely producing VIIRS global ocean color products. In particular, the high spatial resolution of VIIRS-derived normalized water-leaving radiance at the wavelength of 638 nm (I1 band) nLw(638) can provide useful information about water optical, biological, and biogeochemical properties over turbid coastal and inland waters. In addition, VIIRS-derived nLw(638) data provide an important spectral point and coverage along with nLw(λ) from the seven VIIRS M-bands at wavelengths of 410, 443, 486, 551, 671, 745, and 862 nm, particularly over turbid coastal and inland waters. In this paper, we describe our effort to develop a technique for deriving VIIRS nLw(λ) spectra for both M-bands and I-bands using the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Some examples of VIIRS-derived nLw(638) are provided and discussed, showing the usefulness of this added capability for providing water properties over global turbid coastal and inland waters. Therefore, MSL12 is now capable of routinely and efficiently producing VIIRS nLw(638) for both spatial resolutions of 375 and 750 m, along with nLw(λ) spectra at VIIRS M1–M7 with the spatial resolution of 750 m.
A comparison and validation of satellite-derived fire severity mapping techniques in fire prone north Australian savannas: Extreme fires and tree stem mortality Remote Sens. Environ. (IF 6.265) Pub Date : 2018-01-03 Andrew C. Edwards, Jeremy Russell-Smith, Stefan W. Maier
Severe fires in tropical savanna systems are recognised as incurring significant impacts on a variety of ecological attributes, including woody vegetation structure and greenhouse gas emissions. However, knowledge of the frequency and extent of severe fires is restricted given challenges associated with the development of reliable remotely sensed mapping procedures. This study takes advantage of three wildfires, 900–5300 km2 in extent, containing very severely affected areas, occurring in semi-evergreen, eucalypt-dominated, tropical Australian savanna, which resulted in significant areas of complete canopy scorch, very significant tree stem mortality (24–55%), and associated loss of living above ground biomass (47–69%) at respective sites. Although increased map scale is generally considered to improve the reliability of fire severity mapping, our analysis found > 90% agreement between Landsat and MODIS-derived burnt area mapping, and > 80% for binary (severe vs. non-severe) fire severity mapping. Mapping of internal fire (unburnt) patchiness was enhanced with finer resolution Landsat imagery, but the much longer orbital return cycle precluded its use at two of the three sites given extended cloudy conditions. Application of an automated MODIS-derived fire severity mapping algorithm (overall reliability in 2015 = 75%) calibrated for generalised north Australian savanna conditions, suggests that 15% and 12% of Australia's 1.3 M km2 tropical savannas region were burnt by severe fires in 2015 and 2016, respectively. The study illustrates the potential for MODIS-derived fire severity mapping, the impacts of very severe fires on stand structure, and ongoing challenges associated with deriving reliable fire severity mapping products in Australian savanna systems.
Bottom characterization by using airborne lidar bathymetry (ALB) waveform features obtained from bottom return residual analysis Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-30 Firat Eren, Shachak Pe'eri, Yuri Rzhanov, Larry Ward
Airborne Lidar Bathymetry (ALB) surveys are traditionally used for measuring depths in shallow nearshore and back-bay areas. In this paper, we present a novel ALB waveform processing procedure, namely bottom return residual analysis, for bottom characterization. Waveform features obtained from the bottom return residual analysis are used in a supervised classification approach, i.e. Support Vector Machine, to differentiate between: 1) sand and rock bottoms and subsequently, 2) fine and coarse sand bottoms. The classification procedure was tested on ALB survey data collected with an Optech SHOALS-1000T ALB system that covers a ~ 7 km2 area within 1 km from shore in the western Gulf of Maine, USA. The bottom classification results, when compared to ground-truth measurements, indicate a 96% overall accuracy for sand and rock classification and 86% overall accuracy for fine and coarse sand classification. Results of ALB-based bottom classification are compared with interpretations of a multibeam echosounder acoustic backscatter mosaic collected from the survey area.
The first validation of the precipitable water vapor of multisensor satellites over the typical regions in China Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-27 Fanglin Shi, Jinyuan Xin, Leiku Yang, Zhiyuan Cong, Ruixia Liu, Yining Ma, Yuesi Wang, Xiaofeng Lu, Lei Zhao
Ground-based observations (2014–2015) indicated that the precipitable water vapor (PWV) values in China exhibited large spatial and temporal variations. The annual mean PWV values ranged from 4.0 ± 2.9 mm to 42.3 ± 10.6 mm from the Qinghai-Tibet Plateau to the South China Sea, and the seasonal variation ranged from 1.9 ± 1.2 mm to 50.5 ± 5.4 mm. The PWV values were retrieved from MODIS, VIRR and MERSI data, and the accuracies varied widely with relative errors from 10% to 899%. VIRR performed the best in the humid southern coastal regions of China (R = 93%) where the annual PWV was as high as 35 mm. MERSI was more suitable in the dry western region of China (R = 84%) where the average PWV value was lower than 15 mm. MODIS PWV products could be used to accurately observe the PWV in the city and urbanized areas of the eastern China Plain (R = 89%) where the annual mean PWV was 20–30 mm. All of the products yielded larger errors in the summer than in the winter. The MOD05 and MOD07 data performed better than the other satellite data in most regions.
Quantifying the relative contributions of vegetation and soil moisture conditions to polarimetric C-Band SAR response in a temperate peatland Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-27 Koreen Millard, Murray Richardson
Effective modeling of many hydrological and climatological processes requires accurate spatial characterization of soil moisture, often over large regions and across different spatial scales. Synthetic Aperture Radar (SAR) has been shown to be sensitive to surface soil moisture, and is therefore a promising alternative to field data campaigns. However, the presence of spatially-variable vegetation and surface roughness also affect SAR backscatter. In this research, empirical models were developed to both predict soil moisture from SAR and assess the relationship between LiDAR-derived vegetation and surface conditions, and polarimetric SAR parameters in a vegetated peatland environment. Importantly, the low predictive strength of soil moisture models was only evident through a process of model cross-validation (bivariate regression R2 ranged from 0.14 to 0.66 for fitted models and 0.05 to 0.41 for independently cross-validated models). The LiDAR-derived vegetation density was found to explain a large amount of variance in the SAR data, and models to predict soil moisture from SAR from only the least vegetated sites within the peatland demonstrated much higher predictive strength (R2 = 0.11 to 0.71). Soil moisture within the vegetated and least-vegetated sites was not significantly different. Therefore, non-vegetated areas may be useful as representative imaging locations for remotely monitoring surface moisture conditions in large peatland complexes with heterogeneous vegetation.
Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-27 Ashish Kumar, Narendra Singh, Anshumali, Raman Solanki
The study elucidate upon the evaluation of satellite retrievals with ground based aerosol optical depth (AOD) measurements, their utilization in LiDAR ratio (LR) estimation, boundary layer (BL) height determination and the case studies on aerosol transport over Himalayan region. The AOD retrievals from the latest level-2 data collections (C5.1 and C6.0) of MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites and Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) versions (4.10 and 3) are subjected for quantitative analysis to assess the level of agreement with the quality assured level-2 ground based AErosol RObotic NETwork (AERONET) measurements over Manora peak (29.36° N, 79.46°E), a high altitude site in the Himalayas. Analysis revealed that the AOD from the latest MODIS Terra C6.0 deep blue (DB) 30 km × 30 km and CALIPSO ver. 4.10 (overpass within ~ 100 km distance) are in a very good agreement (R ≥ 0.9) with that from coincident AERONET measurements averaged over the span of ± 30 min. About 77% of the AOD retrieved using MODIS and ~ 87% from CALIPSO were found to be within the expected error (EE) limits. The AOD comparison between MODIS Terra C6.0 DB and CALIPSO ver. 4.10, suggested their synergic use for aerosol characterization over Himalayas. In comparison to the ver. 3, CALIPSO ver. 4.10 is found to have undergone substantial changes, and their long term inter-comparison in the grid 28.86°-29.86° N and 78.96°–79.96° E revealed that their vertical feature and aerosol sub-types are in agreement of ~ 94.6% and ~ 68.6%, respectively. Utilizing the AOD retrievals from AERONET and MODIS collections, the iteratively computed LR for three LiDAR systems was found to be lower (< 16) during winter and higher (> 43) during summer. Study on the BL height estimations suggested that the wavelet covariance transform (WCT) method for CALIPSO could be the best choice as compared to the threshold method, and complements well with the specific humidity gradient method used with the radiosonde observation. Case studies on the continental transport of smoke plumes emanating from crop-residue burning in post-monsoon, and long range transport of aerosols and dust over the region in summer are also discussed using the collocated measurements from ground-based AERONET and LiDAR, in conjunction with MODIS, CALIPSO, reanalysis data and trajectory modeling.
An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-27 Alexandre Bouvet, Stéphane Mermoz, Thuy Le Toan, Ludovic Villard, Renaud Mathieu, Laven Naidoo, Gregory P. Asner
Savannahs and woodlands are among the most important biomes in Africa: they cover half of sub-Saharan Africa, provide vital ecosystem services to the rural communities, and play a major part in the carbon budget. Despite their importance and their fragility, they are much less studied than other ecosystems like rainforests. In particular, the distribution and amount of the above-ground woody biomass (AGB) is largely unknown. In this paper, we produce the first continental map of the AGB of African savannahs and woodlands at a resolution of 25 m. The map is built from the 2010 L-band PALSAR mosaic produced by JAXA, along the following steps: a) stratification into wet/dry season areas in order to account for seasonal effects, b) development of a direct model relating the PALSAR backscatter to AGB, with the help of in situ and ancillary data, c) Bayesian inversion of the direct model. A value of AGB and its uncertainty has been assigned to each pixel. This approach allows estimating AGB until 85 Mg·ha− 1 approximately, while dense forests and non-vegetated areas are masked out using the ESA CCI Land Cover dataset. The resulting map is visually compared with existing AGB maps and is validated using a cross-validation approach and a comparison with AGB estimates obtained from LiDAR datasets, leading to an RMSD of 8 to 17 Mg·ha− 1. Finally, carbon stocks for savannahs in Africa and in 50 countries are estimated and compared with estimates by FAO and from AGB maps available over Africa.
Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-27 Yibo Liu, Jingfeng Xiao, Weimin Ju, Gaolong Zhu, Xiaocui Wu, Weiliang Fan, Dengqiu Li, Yanlian Zhou
Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in satellite-derived LAI products and their effects on the simulation of carbon and water fluxes at regional scales remain unclear. We evaluated three satellite-derived LAI products - MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003–2012 using fine-resolution (30 m) LAI data and field LAI measurements. GLASS had higher accuracy than FSGOM and MCD15 for forests, while FSGOM had higher accuracy than MCD15 and GLASS for grasslands. The three LAI products differed in magnitude, spatial patterns, and trends in LAI. We then examined the resulting discrepancies in simulated annual GPP and ET over China using a process-based, diagnostic terrestrial biosphere model. Mean annual total GPP for China's terrestrial ecosystems based on GLASS (6.32 Pg C yr− 1) and FSGOM (6.15 Pg C yr− 1) was 22.5% and 19.2% higher than that based on MCD15 (5.16 Pg C yr− 1), respectively. Annual GPP based on GLASS and MCD15 increased over larger fractions of China's vegetated area (15.9% and 17.3%, respectively) than that based on FSGOM (12.6%). National annual ET based on GLASS (379.9 mm yr− 1) and FSGOM (374.4 mm yr− 1) was 7.9% and 6.3% higher than that based on MCD15 (352.1 mm yr− 1), respectively. Simulated ET increased in larger fractions of the vegetated area for GLASS (5.7%) and MCD15 (5.8%) than for FSGOM (3.9%). Our study shows that there were large discrepancies in LAI among satellite-derived LAI products and the biases of the LAI products could lead to substantial uncertainties in simulated carbon and water fluxes.
Multi-temporal fine-scale modelling of Larix decidua forest plots using terrestrial LiDAR and hemispherical photographs Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-27 M. Bremer, V. Wichmann, M. Rutzinger
Fine-scale architectural tree models serve as an effective representation of three-dimensional plant material distributions. They can help to quantify wood volume and biomass, to estimate leaf area distributions on a detailed scale, and can be exploited for physically based modelling approaches. If architectural tree models can be derived for multiple acquisition dates, they permit the detailed investigation of phenological effects. Although promising approaches for the generation of architectural tree/forest models from terrestrial LiDAR data are available, they are often non-trivial and their application to forest plots is often difficult. This is restricting the flexibility of these reconstruction approaches especially for multi-temporal analyses. In this paper, forest models of two Larix decidua forest plots are reconstructed by making use of terrestrial LiDAR data and digital hemispherical photographs (DHP). Recent modelling strategies are enhanced and developed further in order to improve the robustness and usability of the architectural tree model reconstruction process. Raw point cloud data are directly used as input to solve both tree delineation and tree reconstruction in a single processing pipeline. This includes terrain filtering, intensity filtering, and trunk extraction. These steps are followed by a hierarchical and iterative multi-tree branch and twig reconstruction. Based on multi-temporal DHPs, various foliage states are documented. These DHPs and the reconstructed branching architectures are used to flexibly generate and update multi-temporal 3D models of foliage. In order to quantify the modelling performance with respect to various forest characteristics, a test setup based on simulated forest and acquisition geometries is build up. It can be shown, that typical sources of error in the tree reconstruction process are minimized by the proposed approach. It is possible to estimate wood volume distributions, trunk tapering and leaf area distributions with an error of only 10–14%. Except for strongly overlapping tree crowns, the overall accuracy of the single tree delineation in interlinked tree crowns is higher than 80%. Considering these error margins, we apply the modelling strategy to two forest plots and derive architectural models for three dates during the growing season. Using DHPs as reference data, it can be shown, that the estimated gap fraction values derived from the generated models show an error of only 10–15%.
Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-27 Víctor Fernández-García, Mónica Santamarta, Alfonso Fernández-Manso, Carmen Quintano, Elena Marcos, Leonor Calvo
Multispectral imagery is a widely used source of information to address post-fire ecosystem management. The aim of this study is to evaluate the ability of remotely sensed indices derived from Landsat 8 OLI/TIRS to assess initial burn severity (overall, on vegetation and on soil) in fire-prone pine forests along the Mediterranean-Transition-Oceanic climatic gradient in the Mediterranean Basin. We selected four large wildfires which affected pine forests in a climatic gradient within the Iberian Peninsula. In each wildfire we established CBI plots to obtain field values of three burn severity metrics: site, vegetation and soil burn severity. The ability of 13 spectral indices to match these three field burn severity metrics was compared and their transferability along the climatic gradient assessed using linear regression models. Specifically, we analysed the performance of 12 indices previously used for burn severity assessments (8 reflective, 2 thermal, 2 mixed) and a new reflective index (dNBR-EVI). The results showed that Landsat spectral indices have a greater ability to determine site and vegetation burn severity than soil burn severity. We found large differences in indices performances among the three different climatic regions, since most indices performed better in the Mediterranean and Transition regions than in the Oceanic one. In general, the dNBR-EVI showed the best fit to site, vegetation and soil burn severity in the three regions, demonstrating broad transferability along the entire climatic gradient.
Accurate coastal DEM generation by merging ASTER GDEM and ICESat/GLAS data over Mertz Glacier, Antarctica Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-29 Xianwei Wang, David M. Holland, G. Hilmar Gudmundsson
Mertz Glacier (MG) calved in February 2010 and a 70-years' calving cycle of MG was reported recently because of the shallow Mertz Bank. To better investigate the calving process, a high accurate surface Digital Elevation Model (DEM) with accurate date marker over MG is urgently required for numerical modeling, especially over the ice tongue. However it is quite challenging to generate an accurate DEM over high relief and steep slope regions. To solve this problem, a new method for accurate coastal DEM production by merging ASTER GDEM and ICESat/GLAS data is designed, which can effectively discriminate accurate elevation data from ASTER GDEM. Then, a DEM corresponding to the end of 2002 over MG is generated, which has an accuracy of 0.99 ± 17.50 m. This DEM is several times better in accuracy than the original ASTER GDEM (accuracy: − 8.17 ± 54.31 m) and two new characteristics of ASTER GDEM have been found through analysis. First, ASTER GDEM has elevation bias (varying from − 23 m to 28 m), spatially correlated to ASTER ground tracks, which was most probably caused by uncertainty of attitude measurements of ASTER. Second, ASTER GDEM grids with stacking number ≥ 4 can be effectively adjusted by ground control points so as to improve elevation accuracy. Additionally, this DEM has the best accuracy by comparing with other DEMs (RAMP DEM: 47.71 ± 91.61 m, ICESat/GLAS DEM: − 1.01 ± 30.33, Bamber DEM: − 1.07 ± 33.04 m, Bedmap-2 DEM: 9.19 ± 48.34 m, and Cryosat-2 DEM: − 5.42 ± 32.02 m). The high performance of our DEM in accuracy indicates that our method is effective and has a potential to be widely used to improve existing ASTER GDEM along Antarctic coast.
CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-29 Scott A. Kulp, Benjamin H. Strauss
Positive vertical bias in elevation data derived from NASA's Shuttle Radar Topography Mission (SRTM) is known to cause substantial underestimation of coastal flood risks and exposure. Previous attempts to correct SRTM elevations have used regression to predict vertical error from a small number of auxiliary data products, but these efforts have been focused on reducing error introduced solely by vegetative land cover. Here, we employ a multilayer perceptron artificial neural network to perform a 23-dimensional vertical error regression analyses, where in addition to vegetation cover indices, we use variables including neighborhood elevation values, population density, land slope, and local SRTM deviations from ICESat altitude observations. Using lidar data as ground truth, we train the neural network on samples of US data from 1–20 m of elevation according to SRTM, and assess outputs with extensive testing sets in the US and Australia. Our adjustment system reduces mean vertical bias in the coastal US from 3.67 m to less than 0.01 m, and in Australia from 2.49 m to 0.11 m. RMSE is cut by roughly one-half at both locations, from 5.36 m to 2.39 m in the US, and from 4.15 m to 2.46 in Australia. Using ICESat data as a reference, we estimate that global bias falls from 1.88 m to −0.29 m, and RMSE from 4.28 m and 3.08 m. The methods presented here are flexible and effective, and can be effectively applied to land cover of all types, including dense urban development. The resulting enhanced global coastal DEM (CoastalDEM) promises to greatly improve the accuracy of sea level rise and coastal flood analyses worldwide.
Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-29 Hamed Gholizadeh, John A. Gamon, Arthur I. Zygielbaum, Ran Wang, Anna K. Schweiger, Jeannine Cavender-Bares
Hyperspectral data, with their detailed spectral information at different wavelengths, offer multiple ways to assess biodiversity. One approach, known as the “spectral variation hypothesis” (SVH), proposes that biodiversity is linked to spectral diversity. However, SVH-based approaches, which we refer to as “spectral diversity metrics”, can be confounded by soil exposure and are sensitive to the spatial resolution of the data. To address these issues, we 1) investigated the impact of soil exposure on spectral diversity, 2) identified optimal bands for mapping biodiversity using a spectral diversity metric based on dimension reduction, and 3) assessed the impact of spatial resolution on spectral diversity metrics. In this study, α-diversity (species richness) was used as a measure of plant biodiversity. The study was based on two imaging spectrometry data sets from the Cedar Creek Ecosystem Science Reserve in Central Minnesota, USA, at two levels: proximal and airborne. The data sets included varying degrees of soil background sampled at two different spatial resolutions (1 mm and 0.75 m). We explored five spectral diversity metrics, including the coefficient of variation, convex hull volume, spectral angle mapper, spectral information divergence, and a newly proposed dimension reduction-based metric called “convex hull area.” For the proximal data set (pixel size of 1 mm), filtering soil pixels by applying a normalized difference vegetation index (NDVI) threshold improved the performance of all spectral diversity metrics significantly, with the coefficient of variation showing the highest correlation with species richness. In the airborne data set (pixel size of 0.75 m), the convex hull area outperformed other metrics. These findings demonstrate promising approaches for remote sensing of biodiversity, illustrate a confounding effect of soil background on remote diversity measurement, and indicate that the most informative regions of the electromagnetic spectrum for estimating species richness can vary with spatial scale.
The shelf-life of airborne laser scanning data for enhancing forest inventory inferences Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-30 Ronald E. McRoberts, Qi Chen, Dale D. Gormanson, Brian F. Walters
The term shelf-life is used to characterize the elapsed time beyond which a commodity loses its usefulness. The term is most often used with reference to foods and medicines, but herein it is used to characterize the elapsed time beyond which airborne laser scanning (ALS) data are no longer useful for enhancing inferences for forest inventory population parameters. National forest inventories (NFI) have a long history of using remotely sensed auxiliary information to enhance inferences. Although the combination of model-assisted estimators and ALS auxiliary data has been demonstrated to be particularly useful for this purpose, the expense associated with the acquisition of the ALS data has been an argument against their operational use. However, the longer the shelf-life of ALS data, the less the continuing acquisition costs and the greater the utility of the data. The objective of the study was to assess the shelf-life of ALS data for enhancing inferences in the form of confidence intervals for mean aboveground, live tree, stem biomass per unit area. Confidence intervals were constructed using both model-assisted estimators and post-stratified estimators, four measurements of mostly the same forest inventory plots at 5-year intervals over a 17-year period, and a single set of ALS data acquired near the end of the 17-year period. The study area in north central Minnesota in the USA was characterized by naturally regenerated, uneven-aged, mixed species stands on both lowland and upland sites. The primary conclusions were twofold. First, the shelf-life of ALS data when used with model-assisted estimators exceeded 10 years, and second, even for 12 years elapsed time between plot measurement and ALS data acquisition, the variance of the model-assisted estimator of the mean was smaller by a factor of at least 1.75 than the variance of the stratified estimator used by the national forest inventory.
Analysis of thickness changes and the associated driving factors on a debris-covered glacier in the Tienshan Mountain Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-21 Lei Huang, Zhen Li, Haidong Han, Bangsen Tian, Jianmin Zhou
Supraglacial debris complicates the melting state of debris-covered glaciers, and whether debris increases or decreases the rate of glacier melt is ambiguous according to different observations. In this paper, we aim to determine the potential factors that influence changes in thickness of debris-covered glaciers. First, we present the thickness changes of a large debris-covered glacier in the Tienshan Mountain using high-resolution Digital Elevation Model (DEM) data from three periods in 2000, 2009 and 2013. It is shown that the thickness changes differ greatly over the debris-covered portions of the glacier. These debris-covered regions can be divided into three parts along the glacier axis according to the rate of thickness change. Specifically, these parts include the zone of minimal change, the zone of heavy thinning, and the zone of slight thinning. Detailed information on the closely related factors, including the debris thickness, which was measured across the whole glacier during our field work, and the presence of ice cliffs and supraglacial lakes detected on high-resolution satellite images, are combined to determine the reasons for the differences in melting state among the three zones. The results show that the thickness changes of the debris-covered glacier are jointly influenced by debris thickness and the presence of ice cliffs and supraglacial lakes; moreover, the dominant factor differs among the different zones. The critical debris thickness, which mostly appears in the minimal change zone and accelerates glacier melting, as confirmed through field observations, is not associated with glacier thinning because its location is close to the accumulation zone. The regions where the rates of thinning are greatest coincide with the regions where the ice cliffs are densest. Where the debris is thicker than 1 m on average, the glacier is still thinning slightly due to the presence of ice cliffs and lakes. It is proven that the quantity and area of the ice cliffs and supraglacial lakes is the key to understanding the melting rate of debris covered glacier.
Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-21 Qingqing He, Bo Huang
The use of satellite-retrieved aerosol optical depth (AOD) data and statistical modeling provides a promising approach to estimating PM2.5 concentrations for a large region. However, few studies have conducted high spatial resolution assessments of ground-level PM2.5 for China at the national scale, due to the limitations of high-resolution AOD products. To generate high-resolution PM2.5 for the entirety of mainland China, a combined 3 km AOD dataset was produced by blending the newly released 3 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOD data with the 10 km-resolution MODIS Deep Blue AOD data. Using this dataset, surface PM2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations. Comparisons with the popular spatiotemporal models (daily geographically weighted regression and two-stage model) indicated that the proposed GTWR model, with an R2 of 0.80 in cross-validation (CV), performs notably better than the two other models, which have an R2 in CV of 0.71 and 0.72, respectively. The use of the combined 3-km high resolution AOD data was found not only to present detailed particle gradients, but also to enhance model performance (CV R2 is only 0.32 for the non-combined AOD data). Furthermore, the GTWR's ability to predict PM2.5 for days without PM2.5-AOD paired samples and to generate historical PM2.5 estimates was demonstrated. As a result, fine-scale PM2.5 maps over China were generated, and several PM2.5 hotspots were identified. Therefore, it becomes possible to generate daily high-resolution PM2.5 estimates over a large area using GTWR, due to its synergy of spatial and temporal dimensions in the data and its ability to extend the temporal coverage of PM2.5 observations.
Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-22 Xiaoma Li, Yuyu Zhou, Ghassem R. Asrar, Zhengyuan Zhu
High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST is one of such high spatiotemporal datasets that are widely used. But, it has a large amount of missing values primarily because of clouds, shadows, and other atmospheric conditions. Gapfilling the missing values is an important approach to create seamless high spatiotemporal LST datasets. However, current gapfilling methods have limitations in terms of accuracy and efficiency to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybrid method by integrating daily merging (gapfilling missing values at one overpass using values at the other three overpasses each day), spatiotemporal gapfilling (estimating missing values based on values of their spatial and temporal neighbors), and temporal interpolation (gapfilling missing values based on values of their neighboring days), to create a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas in the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates its root mean squared error (RMSE) to be 3.3 K for mid-daytime (1:30 pm) and 2.7 K for mid-nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products for large areas. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying urban climate (e.g., quantifying surface urban heat island intensity, creating seamless high spatiotemporal air temperature dataset) and the related impacts on people (e.g., health and mortality), ecosystems (e.g., phenology), and energy systems (e.g., building energy use).
Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the CALIPSO satellite Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-22 Jiayun Yao, Sean M. Raffuse, Michael Brauer, Grant J. Williamson, David M.J.S. Bowman, Fay H. Johnston, Sarah B. Henderson
Forest fire smoke is a growing public health concern as more intense and frequent fires are expected under climate change. Remote sensing is a promising tool for exposure assessment, but its utility for health studies is limited because most products measure pollutants in the total column of the atmosphere, and not the surface concentrations most relevant to population health. Information about the vertical distribution of smoke is vital for addressing this limitation. The CALIPSO satellite can provide such information but it cannot cover all smoke events due to its narrow ground track. In this study, we developed a random forests model to predict the minimum height of the smoke layer observed by CALIPSO at high temporal and spatial resolution, using information about fire activity in the vicinity, geographic location, and meteorological conditions. These pieces of information are typically available in near-real-time, ensuring that the resulting model can be easily operationalized. A total of 15,617 CALIPSO data blocks were identified as impacted by smoke within the province of British Columbia, Canada from 2006 to 2015, and 52.1% had smoke within the boundary layer, where the population might be exposed. The final model explained 82.1% of the variance in the observations with a root mean squared error of 560 m. The most important variables in the model were wind patterns, the month of smoke observation, and fire intensity within 500 km. Predictions from this model can be 1) directly applied to smoke detection from the existing remote sensing products to provide another dimension of information; 2) incorporated into statistical smoke models with inputs from remote sensing products; or 3) used to inform estimates of vertical dispersion in deterministic smoke models. These potential applications are expected to improve the assessment of ground-level population exposure to forest fire smoke.
Improved crop residue cover estimates obtained by coupling spectral indices for residue and moisture Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-16 M. Quemada, W.D. Hively, C.S.T. Daughtry, B.T. Lamb, J. Shermeyer
Remote sensing assessment of crop residue cover (fR) and tillage intensity can improve predictions of the environmental impact of agricultural practices and promote sustainable management. Spectral indices for estimating fR are sensitive to soil and crop residue water contents, therefore the uncertainty of fR estimates increases when moisture conditions vary. Our goals were to evaluate the robustness of spectral residue indices based on the shortwave infrared region (SWIR) for estimating fR and to mitigate the uncertainty caused by variable moisture conditions on fR estimates. Ten fields with center pivot irrigation systems (eight partially irrigated and two uniformly dry fields) were identified in Worldview-3 satellite imagery acquired for a study site in Maryland (USA). The fields were mid-irrigation at the time of imagery acquisition, allowing comparison of residue cover under dry and wet conditions. Fields were subdivided into approximately equal-size wedges within the dry and wet portions of each field, and the SWIR bands were extracted for each pixel. Two crop residue indices (Normalized Difference Tillage Index (NDTI); Shortwave Infrared Normalized Difference Residue Index (SINDRI) and a water index (WI) were calculated. Reflectance in each band was moisture-adjusted based on the WI difference between wet and dry wedges, and updated NDTI and SINDRI were calculated. Finally, the probability density distributions of fR estimated from the residue indices were calculated for each field. SINDRI was more robust than NDTI for estimating fR. Moisture corrections of spectral bands reduced the root mean square error of NDTI fR estimates from 22.7% to 4.7%, and SINDRI fR estimates from 6.0% to 2.2%. The mean and variance of the probability density distribution of fR estimated from residue indices, before and after moisture correction, were greatly reduced in the partially irrigated fields, but only slightly in fields with uniform water distribution. The estimation of fR should be based on SINDRI if appropriate bands are available, but fR can be reliably estimated by combining NDTI with a water content index to mitigate the uncertainty caused by variable moisture conditions.
A new top-down approach for directly estimating biomass burning emissions and fuel consumption rates and totals from geostationary satellite fire radiative power (FRP) Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-16 Bernardo Mota, Martin J. Wooster
Regional to global-scale biomass burning emissions inventories are primarily based on satellite-derived burned area or fire radiative power (FRP), and most rely on conversions to fuel consumption prior to the emissions estimation stage. This is generally considered the step introducing greatest uncertainty, and some apparently discrete inventories are not fully independent, as they have been cross-calibrated to aid this stage. We present a novel emissions inventory approach that bypasses the fuel consumption step, directly linking geostationary FRP measures to emission rates of total particulate matter (TPM), via coefficients derived from observations of smoke plume aerosol optical depth (AOD). The approach is fully ‘top-down’, being based on spaceborne observations alone, is performed at or close to the FRP data's original pixel resolution, and avoids the need to assume or model fuel consumption per unit area prior to the emissions calculation. Rates and totals of trace gas and carbon emission can be inferred from the TPM fluxes, and in combination with satellite burned area (BA) products the approach provides an innovative top down approach to mapping fuel consumption per unit area (kg·m− 2) as a last step in the calculation. Using this innovative methodology, which we term ‘FREemissions’ (FREM), we generate a 2004–2012 fire emissions inventory for southern Africa, based on Meteosat FRP-PIXEL data. We find basic annual average TPM emissions 45% higher than the widely used GFASv1.2 inventory, with our higher totals in line with independent assessments that necessitate a significant upscaling of GFAS TPM emissions to match observed AODs. Our estimates are also 12% higher than GFEDv4.1s, which already includes a substantial upward adjustment for fires too small to be detected by the MODIS MCD64A1 BA product. If we adjust the FREM-derived emissions for SEVIRI's inability to detect the lower FRP component of the regions fire regime then the differences between FREM and GFAS/GFED grow further, to a mean of 64% with respect to GFED4.1s TPM emissions for example. These upwardly adjusted FREM estimates agree very well with FEER, an FRP- and AOD-based inventory driven by polar-orbiting MODIS FRP ‘snapshots’ rather than geostationary observations. Similarly higher totals are seen for FREM's fire-emitted trace gases, derived using the emission factor ratios of gases to particulates. Our exploitation of geostationary FRP requires fewer assumptions than use of polar orbiter FRP measures, avoids biases coming from incomplete sampling of the fire diurnal cycle, and enables the FREM approach to provide fire emissions and fuel consumption estimates at a higher spatio-temporal resolution than any inventory currently available (e.g. 0.05°, and hourly averages or better), including per km2 of area burned. The approach offers great potential to generate very high resolution fire emissions datasets for the tropics, sub-tropics and potentially temperate zones, with updates available in near real-time from the global suite of geostationary meteorological satellites operated by organisations such as EUMETSAT (Meteosat), NOAA (GOES) and JMA (Himawari).
Infrared dust aerosol optical depth retrieved daily from IASI and comparison with AERONET over the period 2007–2016 Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-14 V. Capelle, A. Chédin, M. Pondrom, C. Crevoisier, R. Armante, L. Crepeau, N.A. Scott
Aerosols represent one of the dominant uncertainties in radiative forcing, partly because of their very high spatiotemporal variability, a still insufficient knowledge of their microphysical and optical properties, or of their vertical distribution. A better understanding and forecasting of their impact on climate therefore requires precise observations of dust emission and transport. Observations from space offer a good opportunity to follow, day by day and at high spatial resolution, dust evolution at global scale and over long time series. Infrared observations allow retrieving dust aerosol optical depth (AOD) as well as the mean dust layer altitude, daytime and nighttime, over oceans and over continents, in particular over desert. Moreover, coarse mode particles are preferentially observed in the infrared, when, in the visible, both larger and finer particles are observed making the distinction between the two modes difficult. Therefore, they appear complementary to observations in the visible. In this study, a decade of the Infrared Atmospheric Sounder Interferometer (IASI) on board European Satellite Metop-A observations, from July 2007 to December 2016, has been processed pixel by pixel, using a “Look-Up-Table” (LUT) physical approach. Important improvements have been brought to our former approach in order to extend it to: 1) daytime retrieval, 2) mid-latitude retrieval, 3) retrieval at the IASI pixel resolution, 4) near real time retrieval (day-1). Moreover, over continents, surface characteristics (pressure, temperature, as well as emissivity spectrum) are now better accounted for. Here, a particular attention is given to the validation of the IASI-retrieved AOD through comparisons with the Spectral Deconvolution Algorithm (SDA) 500 nm coarse mode AOD observed at 70 ground-based Aerosol RObotic NETwork (AERONET) sites during the 114 months processed. Even if such a comparison requires converting AOD from infrared to visible, inherently leading to significant uncertainties, the two AOD datasets compare well, with an overall correlation of 0.8. For a large majority of sites, correlation ranges from 0.7 to 0.9. Sites with highest correlation are well distributed within the “dust belt” (Sahara, Arabian Peninsula, Mediterranean basin, India and also the Caribbean). Correlations obtained for East-Asia are in general smaller, which might be due to a more complex dust structure (i.e., impact of pollution) and partly due to an increase of the AERONET coarse-mode AOD uncertainty. More generally, the good overall agreement between our restitutions and AERONET AOD demonstrates the ability of infrared sounders to infer dust properties, which opens interesting perspective for a synergy with visible observations.
PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-14 Dong Li, Tao Cheng, Min Jia, Kai Zhou, Ning Lu, Xia Yao, Yongchao Tian, Yan Zhu, Weixing Cao
The leaf optical properties model PROSPECT has been widely used to retrieve foliar chemistry in reverse mode from directional-hemispherical reflectance factor (DHRF) spectra measured with integrating sphere equipped spectrometers. With bidirectional reflectance factor (BRF) spectra, some researchers attempted to invert PROSPECT after a modification to the latest version of the model. However, the retrieval accuracy varies greatly with chemical constituents and can be low for some of them, such as dry matter content. This paper proposes a new approach called PROCWT by coupling PROSPECT with continuous wavelet transform (CWT) to suppress the surface reflectance effect and enhance the absorption features of chemical constituents. Instead of the reflectance spectra, the wavelet coefficient spectra generated after CWT were used to construct the merit function for model inversion. Given that the multi-scale decomposition of CWT enables enhancement of chemical-specific absorption features, the use of PROCWT at different scales of wavelet decomposition could lead to improved retrievals of biochemical parameters. The performance of PROCWT was evaluated for estimating foliar chemicals of wheat and rice crops from BRF spectra measured with a leaf clip equipped spectrometer over a two-year field experiment. PROCWT was also compared with the standard PROSPECT inversion (STANDARD), the PROSPECT inversion with the subtraction of surface reflectance (PROREF), and the simplified PROCOSINE (sPROCOSINE). Our results demonstrated that the contribution of surface reflectance component was significant for BRF spectra and the effect of surface reflectance could be suppressed by PROCWT as well as PROREF and sPROCOSINE. Compared with STANDARD, PROCWT and the two traditional methods significantly improved the retrieval accuracies for pigments and leaf water content, but only PROCWT produced significant improvement for dry matter content with a decrease of 14.79 g/m2 in the root mean squared error (RMSE) (30% of the mean) over the entire experimental dataset by enhancing dry matter absorption features. High scales of wavelet decomposition were favorable for the estimation of carotenoid and water contents and low scales for the estimation of chlorophyll and dry matter contents. The difference in optimal scale revealed the separation of overlapping absorption features attributed to various chemical constituents. In addition, the newest PROSPECT-D outperformed PROSPECT-5B in the retrieval of chlorophyll content but not for carotenoid. This new physically-based approach could be beneficial to analysts attempting to retrieve leaf chemicals from BRF spectra alone and close-range reflectance imagery of crops and even other vegetation types.
Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014) Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-23 Derek Rogge, Agnes Bauer, Julian Zeidler, Andreas Mueller, Thomas Esch, Uta Heiden
Soil information with high spatial and temporal resolution is crucial to assess potential soil degradation and to achieve sustainable productivity and ultimately food security. The spatial resolution of existing soil maps can commonly be too coarse to account for local soil variations and owing to the cost and resource needs required to update information these maps lack temporal information. With improved computational processing capabilities, increased data storage and most recently, the increasing amount of freely available data (e.g. Landsat, Sentinel-2A/B), remote sensing imagery can be integrated into existing soil mapping approaches to increase temporal and spatial resolution of soil information. Satellite multi-temporal data allows for generating cloud-free, radiometrically and phenologically consistent pixel based image composites of regional scale. Such data sets are of particular use for extracting soil information in areas of intermediate climate where soils are rarely exposed. The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure. The objective of this paper is to demonstrate the automated processors ability to handle large image databases to build multispectral reflectance composite base data layers that can support large scale top soil analyses. The functionality of the SCMaP is demonstrated using Landsat imagery over Germany from 1984 to 2014 applied over 5 year periods. Three primary product levels are generated that will allow for a long term assessment and distribution of soils that include the distribution of exposed soils, a statistical information related to soil use and intensity and the generation of exposed soil reflectance image composites. The resulting composite maps provide useful value-added information on soils with the exposed soil reflectance composites showing high spatial coverage that correlate well with existing soil maps and the underlying geological structural regions.
Rigorous 3D change determination in Antarctic Peninsula glaciers from stereo WorldView-2 and archival aerial imagery Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-23 Karolina D. Fieber, Jon P. Mills, Pauline E. Miller, Lucy Clarke, Louise Ireland, Adrian J. Fox
This paper presents detailed elevation and volume analysis of 16 individual glaciers, grouped at four locations, spread across the Antarctic Peninsula (AP). The study makes use of newly available WorldView-2 satellite stereo imagery to exploit the previously untapped value of archival stereo aerial photography. High resolution photogrammetric digital elevation models (DEMs) are derived to determine three-dimensional glacier change over an unprecedented time span of six decades with an unparalleled mean areal coverage of 82% per glacier. The use of an in-house robust surface matching algorithm ensured rigorous alignment of the DEMs to overcome inherent problems associated with processing archival photography, most notably the identification and correction of scale error in some datasets. The analysis provides insight into one of the most challenging and data-scarce areas on the planet by expanding the spatial extent north of the AP to include previously un-studied glaciers located in the South Shetland Islands. 81% of glaciers studied showed considerable loss of volume over the period of record. The mean annual mass loss for all glaciers yielded 0.24 ± 0.08 m.w.e. per year, with a maximum mass loss of up to 62 m.w.e. and frontal retreat exceeding 2.2 km for Stadium Glacier, located furthest north on Elephant Island. Observed volumetric loss was broadly, though not always, correlated with frontal retreat. The combined mass balance of all 16 glaciers yielded − 1.862 ± 0.006 Gt, which corresponds to − 0.005 mm sea level equivalent (SLE) over the 57 year observation period.
Analyzing spatial and temporal variability in short-term rates of post-fire vegetation return from Landsat time series Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 Ryan J. Frazier, Nicholas C. Coops, Michael A. Wulder, Txomin Hermosilla, Joanne C. White
The disturbance and recovery cycles of Canadian boreal forests result in highly dynamic landscapes, requiring continued monitoring to observe and characterize environmental change over time. Well-established remote sensing methods capture change over forested ecosystems, however the return of forest vegetation in disturbed locations is infrequently documented and not well understood. Landsat time-series data allows for both the capture of the initial disturbance and the ability to monitor the subsequent vegetation regeneration with spectral vegetation indices. In this research, we used three spectral recovery metrics derived from an annual Landsat-based per-pixel Normalized Burn Ratio time series to determine trends in the short-term rates of spectral recovery for areas disturbed by wildfire (1986–2006), as assessed using a series of 5-year post-disturbance windows to observe forest recovery trends. Our results indicated that rates of spectral forest recovery vary over time and space in the Taiga and Boreal Shield ecozones. We found evidence that post-fire spectral forest recovery rates have accelerated over time in both the East and West Taiga Shield ecozones, with a consistent, positive, and significant trend measured using a Mann-Kendall test for monotonicity and Theil-Sen slope estimation. Over the analysis period (1986–2011), relative rates of spectral forest recovery increased by 18% in the Taiga Shield East and 9% in the Taiga Shield West. In contrast, spectral forest recovery rates in the Boreal Shield varied temporally, and were not consistently positive or negative. These results demonstrate that post-fire spectral recovery rates are not fixed over time and that spectral trends are dependent upon spatial location in the Canadian boreal. This retrospective baseline information on trends in spectral recovery rates highlights the value of, and continued need for detailed monitoring of vegetation regeneration in boreal forest ecosystems, particularly in the context of a changing climate.
Climate and nutrient effects on Arctic wetland plant phenology observed from phenocams Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 C.G. Andresen, C.E. Tweedie, V.L. Lougheed
This study explores how climate and nutrients influence productivity of arctic wetland plants. The Green-excess Index (GEI) derived from Red, Green and Blue digital image brightness values from digital repeat photography (a.k.a. phenocams) was used to track the inter-annual variability in seasonal greening and above ground biomass for two dominant aquatic emergent graminoids on the Arctic Coastal Plain of northern Alaska: Carex aquatilis and Arctophila fulva. Four years of seasonal and inter-annual greening trends show strong differences in timing and intensity of greenness among species. Thawing degree-days (TDD, days above 0 °C) was a good predictor of GEI in both A. fulva and C. aquatilis. Employing regression tree analyses, we found a greening threshold of 46 TDD for A. fulva, after which GEI increased markedly, while C. aquatilis greened more gradually with a greening mid-point of 31 TDD. Based on long-term climate records and TDD thresholds, greening date has begun 16 thawing degree-days earlier over the past 70 years. To understand the effects of latitude and nutrients on seasonal greening, we compared southern sites and nutrient enriched sites with reference sites. We found statistically higher greenness in southern sites and enriched sites compare to reference sites in both plant species, supporting the role of nutrients and warmer temperatures as key factors enhancing productivity in arctic wetlands. In addition, this study provides an inexpensive, alternative method to monitor climate and nutrient effects at high frequency in arctic aquatic systems through camera-derived GEI greenness and has the potential to bridge the gap between plot level and satellite based observations given its strong relationships with biomass and NDVI.
Understanding land subsidence in salt marshes of the Venice Lagoon from SAR Interferometry and ground-based investigations Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 Cristina Da Lio, Pietro Teatini, Tazio Strozzi, Luigi Tosi
The existence of salt marshes and tidal morphologies is strictly connected to their elevation with respect to the mean sea level. Quantifying land subsidence of these high-valued transitional environments is therefore crucial to investigate their long-term possible survival, also in view of the expected climate changes. However, monitoring with a certain accuracy their movements has been challenging until now due to the peculiar features of these morphological forms: they are difficult to access, made of largely unconsolidated deposits, without anthropogenic structures, relatively far from anthropogenic facilities, and become submerged by the sea water twice a day. For these reasons, they have never be linked to traditional levelling and GPS networks, and also standard Interferometric SAR applications returned very poor results in terms of spatial and temporal coverage. An advanced Persistent Scatterer Interferometry (PSI) technique on a 5-year long stack of X-bandwidth SAR acquisitions of the Venice Lagoon is here presented. The regularity of the acquisitions, the short satellite revisiting time (11 days), the high image resolution (~ 3 × 3 m), and the strategies used in the PSI application have allowed us to detect thousands of measurable persistent targets (PTs) in the Venice Lagoon salt marshes. The measured displacements range from small uplifts to subsidence rates of more than 20 mm/yr. The analyses of the observed displacements point out that land subsidence is much larger on man-made than natural salt marshes, with a significant negative correlation with the marsh age. In addition, land subsidence with the presence of halophytic vegetation species is generally smaller than on unvegetated marshes. Finally, at a few selected sites, the integration of the PSI outcome with local ground-based measurements, such as multi-depth benchmarks, feldspar marker horizons and surface elevation tables, has allowed quantifying the displacement variability versus depth and therefore developing a first conceptual model of the salt marsh consolidation and accretion processes.
Remote sensing of mangrove forest phenology and its environmental drivers Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 J. Pastor-Guzman, Jadunandan Dash, Peter M. Atkinson
Mangrove forest phenology at the regional scale have been poorly investigated and its driving factors remain unclear. Multi-temporal remote sensing represents a key tool to investigate vegetation phenology, particularly in environments with limited accessibility and lack of in situ measurements. This paper presents the first characterisation of mangrove forest phenology from the Yucatan Peninsula, south east Mexico. We used 15-year time-series of four vegetation indices (EVI, NDVI, gNDVI and NDWI) derived from MODIS surface reflectance to estimate phenological parameters which were then compared with in situ climatic variables, salinity and litterfall. The Discrete Fourier Transform (DFT) was used to smooth the raw data and four phenological parameters were estimated: start of season (SOS), time of maximum greenness (Max Green), end of season (EOS) and length of season (LOS). Litterfall showed a distinct seasonal pattern with higher rates during the end of the dry season and during the wet season. Litterfall was positively correlated with temperature (r = 0.88, p < 0.01) and salinity (r = 0.70, p < 0.01). The results revealed that although mangroves are evergreen species the mangrove forest has clear greenness seasonality which is negatively correlated with litterfall and generally lagged behind maximum rainfall. The dates of phenological metrics varied depending on the choice of vegetation indices reflecting the sensitivity of each index to a particular aspect of vegetation growth. NDWI, an index associated to canopy water content and soil moisture had advanced dates of SOS, Max Green and EOS while gNDVI, an index primarily related to canopy chlorophyll content had delayed dates. SOS ranged between day of the year (DOY) 144 (late dry season) and DOY 220 (rainy season) while the EOS occurred between DOY 104 (mid-dry season) to DOY 160 (early rainy season). The length of the growing season ranged between 228 and 264 days. Sites receiving a greater amount of rainfall between January and March showed an advanced SOS and Max Green. This phenological characterisation is useful to understand the mangrove forest dynamics at the landscape scale and to monitor the status of mangrove. In addition the results will serve as a baseline against which to compare future changes in mangrove phenology due to natural or anthropogenic causes.
Assessment of the impact of spatial heterogeneity on microwave satellite soil moisture periodic error Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 Fangni Lei, Wade T. Crow, Huanfeng Shen, Chun-Hsu Su, Thomas R.H. Holmes, Robert M. Parinussa, Guojie Wang
An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer – Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators – based on microwave brightness temperature, land cover types, and long-term averaged vegetation index – are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals.
Cloudy-sky land surface longwave downward radiation (LWDR) estimation by integrating MODIS and AIRS/AMSU measurements Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 Tianxing Wang, Jiancheng Shi, Yuechi Yu, Letu Husi, Bo Gao, Wang Zhou, Dabin Ji, Tianjie Zhao, Chuan Xiong, Ling Chen
Longwave downward radiation (LWDR) is another major energy source received by the earth's surface apart from solar radiation. Its importance in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a large number of efforts have been made to derive LWDR from space under only clear-sky conditions leading to difficulty in utilizing space-based LWDR in most models due to its spatio-temporal discontinuity. Currently, only a few studies are focused on LWDR estimation under cloudy skies, while their global application is still questionable. In this paper, an alternative strategy is proposed aiming to derive high-resolution (1 km) cloudy-sky LWDR by fusing collocated satellite multi-sensor measurements. The results show that the newly developed method works well and can derive LWDR at a better accuracy with RMSE < 27 W/m2 and bias < 10 W/m2 even under cloudy skies and at 1 km scales. By comparing the CCCM and SSF products of CERES, MERRA, ERA-interim and NCEP-CSFR over the Tibetan Plateau region, the new approach demonstrates its superiority in terms of accuracy, temporal variation and spatial distribution patterns of LWDR. Comprehensive comparison analysis also reveals that, except for the proposed product, the other four products (CERES, MERRA, ERA-interim and NCEP-CSFR) show a big difference from each other in the LWDR spatio-temporal distribution pattern and magnitude. The difference between these products can still be up to 60 W/m2 even at the monthly scale, implying that large uncertainties exist in current LWDR estimations. More importantly, besides the higher accuracy of the proposed method, it provides unprecedented possibilities for jointly generating high-resolution global LWDR datasets by connecting the NASA's Earth Observing System-(EOS) mission (MODIS-AIRS/AMSU) and the Suomi National Polar-orbiting Partnership-(NPP) mission (VIIRS-CrIS/ATMS). Meanwhile, the scheme proposed in this study also gives some clues towards multiple data fusing in the remote sensing community.
In-situ passive microwave emission model parameterization of sub-arctic frozen organic soils Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 B. Montpetit, A. Royer, A. Roy, A. Langlois
Many passive microwave remote sensing applications such as land surface temperature, snow water equivalent and soil moisture retrievals need to take into account a soil parameterization to the overall surface signal emission. Soil emission modeling presents large uncertainties when the soil is frozen. In this paper, an empirical retrieval method is presented, specifically for rough frozen soil permittivity estimates at 10.7, 19 and 37 GHz. The method was tested and validated using in-situ passive microwave measurements at incidence angles from 0 to 60° of sub-arctic frozen organic soils in Northeastern Canada. The retrieved permittivity values give an overall RMSE between the measured and simulated brightness temperatures of 4.6 K for all frequencies combined. A sensitivity analysis was conducted on the different soil parameters optimized in this study. This analysis suggests that the accuracy of the retrieved parameters, using the method given here, is of ± 1.00 for the permittivity and ± 0.12 cm for surface roughness. Also, a comparison was conducted between the parameterization used in this study and the one of Wegmüller and Mätzler (1999) to estimate the soil contribution to the emitted brightness temperature of snowpacks. An improvement of 66% of the RMSE between the modeled and measured snow brightness temperatures was observed when using the approach of this study compared to the previous work. The method shows great potential to improve the estimation of the frozen soil contribution to the measured passive microwave brightness temperature.
A new global method of satellite dataset merging and quality characterization constrained by the terrestrial water budget Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 S. Munier, F. Aires
During the last decades, satellite observations have increasingly been used to study the global water cycle over land. Although their value is now appreciated by the hydrological community, they are still limited by their uncertainties and their inability to close the water budget. In a previous study, we optimally integrated several datasets for each component (precipitation, evapotranspiration, storage change and discharge) to close this budget at a basin scale. Furthermore, an independent and simple calibration of each satellite dataset was designed to reduce the budget residual. In this paper, we extend the calibration procedure to the global scale. Pixels are first classified into surface types characterized by their NDVI and net precipitation values. We show that the global calibration transforms the original datasets towards a consensus that is hydrologically more coherent, with a budget residual reduced by 26%. The calibrated datasets are compared to ground-based observations, showing an improvement for more than 65% of the sites tested. This opens new perspectives to generate long-term datasets at global scale based purely on all available satellites observations, which describe all the terrestrial water components useful for climate purposes. Beyond the simple calibration presented here, inconsistencies among the various satellite datasets can be used as a proxy for satellite observation uncertainties. The quality of our calibration procedure is constrained by the availability of discharge measurements, and could therefore be improved in the future, as discharge measurement networks become more extensive.
A LandTrendr multispectral ensemble for forest disturbance detection Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 Warren B. Cohen, Zhiqiang Yang, Sean P. Healey, Robert E. Kennedy, Noel Gorelick
Monitoring and classifying forest disturbance using Landsat time series has improved greatly over the past decade, with many new algorithms taking advantage of the high-quality, cost free data in the archive. Much of the innovation has been focused on use of sophisticated workflows that consist of a logical sequence of processes and rules, multiple statistical functions, and parameter sets that must be calibrated to accurately classify disturbance. For many algorithms, calibration has been local to areas of interest and the algorithm's classification performance has been good under those circumstances. When applied elsewhere, however, algorithm performance has suffered. An alternative strategy for calibration may be to use the locally tested parameter values in conjunction with a statistical approach (e.g., Random Forests; RF) to align algorithm classification with a reference disturbance dataset, a process we call secondary classification. We tested that strategy here using RF with LandTrendr, an algorithm that runs on one spectral band or index. Disturbance detection using secondary classification was spectral band- or index-dependent, with each spectral dimension providing some unique detections and different error rates. Using secondary classification, we tested whether an integrated multispectral LandTrendr ensemble, with various combinations of the six basic Landsat reflectance bands and seven common spectral indices, improves algorithm performance. Results indicated a substantial reduction in errors relative to secondary classification based on single bands/indices, revealing the importance of a multispectral approach to forest disturbance detection. To explain the importance of specific bands and spectral indices in the multispectral ensemble, we developed a disturbance signal-to-noise metric that clearly highlighted the value of shortwave-infrared reflectance, especially when paired with near-infrared reflectance.
Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 Grigorijs Goldbergs, Shaun R. Levick, Michael Lawes, Andrew Edwards
Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2 = 0.754, RMSE = 90 kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4 ha plots: R2 = 0.86, RMSE = 3.4 Mg ha− 1; and 1 ha plots: R2 = 0.83, RMSE = 4.0 Mg ha− 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.
Remote sensing retrievals of colored dissolved organic matter and dissolved organic carbon dynamics in North American estuaries and their margins Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-24 Fang Cao, Maria Tzortziou, Chuanmin Hu, Antonio Mannino, Cédric G. Fichot, Rossana Del Vecchio, Raymond G. Najjar, Michael Novak
Dissolved organic carbon, DOC, and the colored component of dissolved organic matter, CDOM, are key indicators of coastal water quality and biogeochemical state. Yet applications of space-based remote sensing to monitoring of CDOM variability across estuarine ecosystems and assessment of DOC exchanges along highly dynamic terrestrial-aquatic interfaces have been scarce, in part due to the coarse spatial resolution of most existing ocean color sensors and the seasonal and regional dependence of most existing algorithms. Here, we used a rich dataset of field observations to develop and validate new CDOM and DOC algorithms that are broadly applicable to different estuarine and coastal regions, over different seasons and a wide range of in-water conditions. Algorithms were applied to satellite imagery from MERIS-Envisat at a spatial resolution (300 m) that can resolve much of the spatial variability that characterizes estuaries and their margins. Multi-spectral remote sensing reflectance (Rrs) was used to retrieve CDOM absorption at various wavelengths and CDOM absorption spectral slope in the 275–295 nm spectral range (S275–295). DOC concentrations were obtained from a tight relationship between the DOC-specific CDOM absorption and S275–295, two optical quantities that depend only on the quality of CDOM and strongly covary across spatial and temporal scales. Algorithm evaluation using MERIS satellite data across different estuarine and coastal environments (i.e., the northern Gulf of Mexico, the Delaware Bay, the Chesapeake Bay estuary, and the Middle Atlantic Bight coastal waters) and across different seasons over multiple years resulted in relative errors (mean absolute percent difference; MAPD) of 29% (N = 17), 9.5% (N = 14), and 18% (N = 32), for aCDOM(300), S275–295, and DOC, respectively. These relative errors are comparable to those previously reported for satellite retrievals of CDOM and DOC products in less optically complex offshore waters. Application of these algorithms to multi-year MERIS satellite imagery over the Chesapeake Bay estuary allowed, for the first time, to capture the impact of tidal exchanges on carbon dynamics along wetland-estuary interfaces, and resolved spatial gradients, seasonal variability, and year-to-year changes in estuarine carbon amount and quality associated with marsh carbon export, riverine inputs, and extreme precipitation events.
Characterizing the encroachment of juniper forests into sub-humid and semi-arid prairies from 1984 to 2010 using PALSAR and Landsat data Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-28 Jie Wang, Xiangming Xiao, Yuanwei Qin, Russell B. Doughty, Jinwei Dong, Zhenhua Zou
Over the past few decades, wide encroachment of eastern redcedar (Juniperus virginiana) and Ashe juniper (Juniperus ashei) into the prairies of the U.S. Great Plains has affected wildlife habitats, forage and livestock production, and biogeochemical cycles. This study investigates the spatio-temporal dynamics of juniper forest encroachment into tallgrass prairies by generating juniper forest encroachment maps from 1984 to 2010 at 30 m spatial resolution. A pixel and phenology-based mapping algorithm was used to produce the time series maps of juniper forest encroachment using a combination of Phased Array type L-band Synthetic Aperture Radar (PALSAR) mosaic data from 2010 and Landsat 5 and 7 data (10,871 images from 1984 to 2010). We analyzed the resultant maps to understand the dynamics of juniper forest encroachment at state and county spatial scales and examined juniper occurrence by geographic region and soil type. The juniper forest maps were generated over five multi-year periods: the late 1980s (1984–1989), early 1990s (1990–1994), late 1990s (1995–1999), early 2000s (2000–2004), and late 2000s (2005–2010). We also produced a map of time since stand detection of juniper forests in 2010. Our major findings include: (1) juniper forests have expanded linearly in time at an annual rate of ~ 40 km2/year since 1984; (2) juniper forests had notable spatial clusters in its expansion process; (3) ~ 65% of juniper forests in 2010 were < 15 years after stands have been detected; and (4) juniper forests in 2010 were mainly distributed in sandy and loamy soils with relatively low available water storage in the top soils. This study demonstrates the potential of combining a cloud computing platform (Google Earth Engine), time series optical images (Landsat), and microwave images to document the spatial-temporal dynamics of juniper forest encroachment into prairies since the 1980s at the regional scale. The results can be used to study the causes, consequences, and potential future distribution of juniper encroachment, which are relevant to the sustainable management of prairie ecosystems.
Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-28 Jie Dong, Lu Zhang, Minggao Tang, Mingsheng Liao, Qiang Xu, Jianya Gong, Meng Ao
InSAR technology provides a powerful tool to detect potentially unstable slopes across wide areas and to monitor surface displacements of a single landslide. However, conventional time series InSAR methods such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) can rarely identify sufficient measurement points (MPs) in mountainous areas due to decorrelations caused by steep terrain and vegetation coverage. In this study, we developed a new InSAR approach, coherent scatterer InSAR (CSI), to map landslide surface displacements in the radar line-of-sight (LOS) direction by combining persistent scatterers (PS) and distributed scatterers (DS). The key ideas of CSI include the employment of the generalized likelihood ratio (GLR) test for the identification of statistically homogeneous pixels (SHPs) and the use of the phase linking algorithm to estimate optimal phase for each DS pixel. The joint exploitation of PS and DS targets dramatically increases the spatial density of MPs, which makes the phase unwrapping more reliable. To demonstrate the effectiveness of the CSI approach, we applied it to retrieve the historical displacements of the Jiaju landslide in Danba County of southwest China using 19 L-band ALOS PALSAR images (2006–2011) and nine C-band ENVISAT ASAR images (2007–2008). Multiple comparisons clearly illustrated the big advantages of CSI over PSI and SBAS in mapping landslide displacements with more details owing to much higher (> 10 times) MP density. Furthermore, the superiority of L-band SAR data over C-band for landslide investigation in rural environments was confirmed. Quantitative validation of the CSI results for PALSAR data against in-situ GPS measurements suggested an accuracy of about 10.5 millimeters per year (mm/year) in terms of root mean square error (RMSE). Afterwards, the spatial-temporal characteristics of the Jiaju landslide surface displacements were summarized, with a new upper boundary for the active northern part delineated. Particularly, the northern part of the landslide moved faster than the southern part, exhibiting a maximum LOS displacement rate of around 120 mm/year. Subsequently, the fluvial erosion by the Dajinchuan River was identified as the predominant impact factor for the instability of the Jiaju landslide. Finally, the major problems and challenges for the application of CSI method were discussed, and the conclusions were given.
Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-29 Nikolai Knapp, Rico Fischer, Andreas Huth
Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-01 Lei Fan, J.-P. Wigneron, Qing Xiao, A. Al-Yaari, Jianguang Wen, Nicolas Martin-StPaul, J.-L. Dupuy, François Pimont, A. Al Bitar, R. Fernandez-Moran, Y.H. Kerr
Live fuel moisture content (LFMC) is an important factor in fire risk management in the Mediterranean region. Drawing upon a large network of stations (the Réseau Hydrique) measuring LFMC for operational fire danger assessment in the south-eastern region of France, this study assesses the ability of several long-term passive microwave remote sensing indices to capture the LFMC temporal dynamic of various Mediterranean shrub species. Microwave remote sensing has a high potential for monitoring LFMC independently of several constraints (e.g., atmospheric and cloud contamination effects) associated with optical-infrared and thermal remote sensing observations. The following four microwave-derived indices are considered: (1) the Essential Climate Variable near-surface soil moisture (ECV_SM); (2) the root-zone soil moisture (ECV_RZSM) derived from ECV_SM; (3) the microwave polarization difference index (MPDI) computed from five microwave frequencies (C, X, Ku, K and Ka-band corresponding to 6.9, 10.7, 18.7, 23.8 and 36.5 GHz respectively); and (4) the vegetation optical depth (VOD) at C- and X-band (from the Advanced Microwave Scanning Radiometer for the Earth observing system, AMSR-E). Firstly, an evaluation of the root-zone soil moisture ECV_RZSM against a network of soil moisture measurements (SMOSMANIA in southern France) gave satisfactory results. For most of the Réseau Hydrique sites, the present study found good agreement between LFMC and individual microwave indices, including root-zone soil moisture, VOD at X-band, and MPDI at X and Ku-bands, all averaged over the 15 days preceding the in-situ LFMC measurements. VOD at X-band showed the best agreement with the in situ LFMC data (median of correlation coefficients over all in situ sites = 0.43). Further comparisons between LFMC data and several optical indices computed from the Moderate Resolution Imaging Spectrometer (MODIS) data including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), visible atmospheric resistant index (VARI), normalized difference water index (NDWI), normalized difference infrared index 6 (NDII6), normalized difference infrared index 7 (NDII7) and global vegetation moisture index (GVMI) were made. The comparisons showed that VARI and SAVI, as optical greenness indices, outperform the microwave indices and other optical indices with median of correlation coefficients of 0.66 and 0.65, respectively. Overall, this study shows that passive microwave indices, particularly VOD, are efficient proxies for LFMC of Mediterranean shrub species and could be used along with optical indices to evaluate fire risks in the Mediterranean region.
An airborne remote sensing case study of synthetic hydrocarbon detection using short wave infrared absorption features identified from marine-harvested macro- and microplastics Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-06 Shungudzemwoyo P. Garaba, Heidi M. Dierssen
The abundance and distribution of plastic debris in natural waters is largely unknown due to limited comprehensive monitoring. Here, optical properties of dry and wet marine-harvested plastic debris were quantified to explore the feasibility of plastic debris optical remote sensing in the natural environment. We measured the spectral reflectance of microplastics (< 5 mm) from the North Atlantic Ocean, macroplastics (> 5 mm) washed ashore along the USA west coast and virgin plastic pellets over a wavelength range from 350 to 2500 nm. Compared to the spectral variability of multi-colored dry macroplastics, the measured dry marine-harvested microplastic reflectance spectra could be represented as a single bulk average spectrum with notable absorption features at ~ 931, 1215, 1417 and 1732 nm. The wet marine-harvested microplastics had similar spectral features to the dry microplastics but the magnitude was lower over the measured spectrum. When spectrally matched to the reference library of typical dry virgin pellets, the mean dry marine-harvested microplastics reflectance had moderate similarities to low-density polyethylene, polyethylene terephthalate, polypropylene and polymethyl methacrylate. This composition was consistent with the subset sampled with the Fourier Transform Infrared (FTIR) spectrometer and what has been reported globally. The absorption features at 1215 and 1732 nm were observable through an intervening atmosphere and used to map the distributions of synthetic hydrocarbons at a landfill and on man-made structures from airborne visible-infrared imaging spectrometer (AVIRIS) imagery, indicating the potential to remotely sense dry washed ashore and land-origin plastics. These same absorption features were identifiable on wet marine-harvested microplastics, but the ability to conduct remote sensing of microplastics at the ocean surface layer will require more detailed radiative transfer analysis and development of high signal-to-noise sensors. The spectral measurements presented here provide a foundation for such advances towards remote detection of plastics from various platforms.
Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-07 Semih Kuter, Zuhal Akyurek, Gerhard-Wilhelm Weber
In this paper, a novel approach to estimate fractional snow cover (FSC) from MODIS data in a complex and heterogeneous Alpine terrain is represented by using a state-of-the-art nonparametric spline regression method, namely, multivariate adaptive regression splines (MARS). For this purpose, twenty MODIS - Landsat 8 image pairs acquired between April 2013 and December 2016 over European Alps are used. Fifteen of the image pairs are employed during model training and five images are reserved as an independent test dataset. MARS models are trained by using MODIS top-of-atmosphere reflectance values of bands 1–7, normalized difference snow index, normalized difference vegetation index and land cover class as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat 8 binary snow cover maps. Multilayer feedforward artificial neural network (ANN) models are also trained by using the same input data. During the training and the testing, the effects of the training data size and the sampling type on the predictive performance of ANN and MARS models are investigated. An additional search is also conducted to reveal whether the choice of the transfer function used in the output layer of ANN has a significant contribution to the network's FSC mapping performance. The final ANN and MARS FSC products are at 500 m spatial resolution. The results on the independent test scenes indicate that the developed ANN models with linear and hyperbolic tangent transfer functions in the output layer and the MARS models are in good agreement with reference FSC data with the same average values of R = 0.93. In contrast, the standard MODIS snow fraction product, namely, MOD10 FSC, exhibits slightly poorer performance with average R = 0.88. The proposed MARS approach is statistically proven to have the same performance with ANN, yet it is computationally more efficient in model building.
Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-07 Ran Goldblatt, Michelle F. Stuhlmacher, Beth Tellman, Nicholas Clinton, Gordon Hanson, Matei Georgescu, Chuyuan Wang, Fidel Serrano-Candela, Amit K. Khandelwal, Wan-Hwa Cheng, Robert C. Balling Jr
Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.
On the relationship between sub-daily instantaneous and daily total gross primary production: Implications for interpreting satellite-based SIF retrievals Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-08 Yao Zhang, Xiangming Xiao, Yongguang Zhang, Sebastian Wolf, Sha Zhou, Joanna Joiner, Luis Guanter, Manish Verma, Ying Sun, Xi Yang, Eugénie Paul-Limoges, Christopher M. Gough, Georg Wohlfahrt, Beniamino Gioli, Christiaan van der Tol, Nouvellon Yann, Magnus Lund, Agnès de Grandcourt
Spatially and temporally continuous estimation of plant photosynthetic carbon fixation (or gross primary production, GPP) is crucial to our understanding of the global carbon cycle and the impact of climate change. Besides spatial, seasonal and interannual variations, GPP also exhibits strong diurnal variations. Satellite retrieved solar-induced chlorophyll fluorescence (SIF) provides a spatially continuous, but temporally discrete measurement of plant photosynthesis, and has the potential to be used to estimate GPP at global scale. However, it remains unclear whether the seasonal time series of SIF snapshots taken at a fixed time of the day can be used to infer daily total GPP variation at spatial and seasonal scales. In this study, we first used GPP estimates from 135 eddy covariance flux sites, covering a wide range of geographic locations and biome types, to investigate the relationship between the instantaneous GPP (GPPinst) and daily GPP (GPPdaily) on the seasonal course for different times of the day. Latitudinal and diurnal patterns were found to correspond to variations in photosynthetically active radiation (PAR) and light use efficiency (LUE), respectively. We then used the Soil-Canopy Observation Photosynthesis and Energy Balance (SCOPE) model and the FluxCom GPP product to investigate the instantaneous and daily SIF-GPP relationships at five flux tower sites along a latitudinal gradient and at a global scale for different biome types. The results showed that daily SIF had a stronger linear correlation with daily GPP than instantaneous SIF at the seasonal scale, with an instantaneous to daily SIF conversion factor following the latitudinal and seasonal pattern driven by PAR. Our study highlights the necessity to take the latitudinal and diurnal factors into consideration for SIF-GPP relationship analyses or for physiological phenology analyses based on SIF.
Eddy-induced cross-shelf export of high Chl-a coastal waters in the SE Bay of Biscay Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-08 Anna Rubio, Ainhoa Caballero, Alejandro Orfila, Ismael Hernández-Carrasco, Luis Ferrer, Manuel González, Lohitzune Solabarrieta, Julien Mader
Different remote sensing data were combined to characterise a winter anticyclonic eddy in the southeastern Bay of Biscay and to infer its effects on cross-shelf exchanges, in a period when typical along shelf-slope currents depict a cyclonic pattern. While the joint analysis of available satellite data (infrared, visible and altimetry) permitted the characterisation and tracking of the anticyclone properties and path, data from a coastal high-frequency radar system enabled a quantitative analysis of the surface cross-shelf transports associated with this anticyclone. The warm core anticyclone had a diameter of around 50 km, maximum azimuthal velocities near 50 cm s− 1 and a relative vorticity of up to −0.45f. The eddy generation occurred after the relaxation of a cyclonic wind-driven current regime over the shelf-slope; then, the eddy remained stationary for several weeks until it started to drift northwards along the shelf break. The surface signature of this eddy was observed by means of high-frequency radar data for 20 consecutive days, providing a unique opportunity to characterise and quantify, from a Lagrangian perspective, the associated transport and its effect on the Chl-a surface distribution. We observed the presence of mesoscale structures with similar characteristics in the area during different winters within the period 2011–2014. Our results suggest that the eddy-induced recurrent cross-shelf export is an effective mechanism for the expansion of coastal productive waters into the adjacent oligotrophic ocean basin.
A sub-pixel method for estimating planting fraction of paddy rice in Northeast China Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-09 Wei Liu, Jie Dong, Kunlun Xiang, Sen Wang, Wei Han, Wenping Yuan
Timely and accurate data regarding the distribution of paddy rice are valuable for various agricultural studies. In this study, we aimed to develop a sub-pixel method for estimating the planting fraction of paddy rice in Northeast China. This method assumes low seasonal variations in moisture in paddy rice fields compared with other upland crops due to the presence of flooding water throughout the growing season. We used the coefficient of variation (CV) of the land surface water index (LSWI) derived from the moderate resolution imaging spectroradiometer (MODIS) to indicate the water condition. High resolution images obtained by an unmanned aerial vehicle (UAV) were used to test this assumption and to develop the relationship between the CV of LSWI and the planting fraction of paddy rice. The results showed that the CV of LSWI could effectively indicate the planting fraction of paddy rice, where our method explained 84% of the variation in the planting fraction of paddy rice in the UAV survey sites. Validation based on the statistical data showed that this method explained 78% and 85% of the variations in the paddy rice area at the county and prefecture levels, respectively. Moreover, the performance of this method was good independent of the field survey data, and this alternative approach may facilitate mapping of the planting distribution of paddy rice over large areas.
On the imaging of exposed intertidal flats by single- and dual-co-polarization Synthetic Aperture Radar Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-11 Martin Gade, Wensheng Wang, Linnea Kemme
We used 26 Radarsat-2 and TerraSAR-X single- and dual-co-polarization Synthetic Aperture Radar (SAR) images of a test site on the German North Sea coast to investigate the influence of imaging geometry and environmental conditions (wind speed, water level, and vegetation period) on the radar return from exposed intertidal flats. Multi-temporal analyses of single- (VV-) polarization SAR imagery indicate that the surface roughness is more variable at shorter scales (responsible for the X-band backscatter) than at longer scales (C-band). Less variation at both radar bands was found in sea-grass meadows. TerraSAR-X dual-co-polarization data were used for polarimetric analyses based on a decomposition of the Kennaugh matrix, whose elements provide information on the total intensity at both co-polarizations and on the relative strength of even- and odd-bounce backscattering. At steep incidence angles (around 30°) the radar backscatter from bare sand flats is similarly strong at both co-polarizations, while the vertically polarized radar return dominates at higher incidence angles (above 40°). At low water levels, resulting in lower moisture of the sandy sediments, strong single-bounce radar backscattering was observed, while higher water levels (and moisture) caused weak mixed (single- and double-bounce) backscattering. Apart from the absolute water level its history, e.g. the time and level of the closest low tide, must be considered. During the vegetation period, sea-grass meadows cause a stronger increase in horizontally (HH-) polarized radar backscatter, along with an increased double-bounce backscattering. We conclude that the Kennaugh element framework has potential to be used for classification purposes in intertidal areas, but also that, for a full interpretation of the SAR imagery, the exact topography and the surface roughness have to be known.
Validation of the SMAP freeze/thaw product using categorical triple collocation Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-11 Haobo Lyu, Kaighin A. McColl, Xinlu Li, Chris Derksen, Aaron Berg, T. Andrew Black, Eugenie Euskirchen, Michael Loranty, Jouni Pulliainen, Kimmo Rautiainen, Tracy Rowlandson, Alexandre Roy, Alain Royer, Alexandre Langlois, Jilmarie Stephens, Hui Lu, Dara Entekhabi
The landscape freeze/thaw (FT) state plays an important role in local, regional and global weather and climate, but is difficult to monitor. The Soil Moisture Active Passive (SMAP) satellite mission provides hemispheric estimates of landscape FT state at a spatial resolution of approximately 362 km2. Previous validation studies of SMAP and other satellite FT products have compared satellite retrievals with point estimates obtained from in-situ measurements of air and/or soil temperature. Differences between the two are attributed to errors in the satellite retrieval. However, significant differences can occur between satellite and in-situ estimates solely due to differences in scale between the measurements; these differences can be viewed as ‘representativeness errors’ in the in-situ product, caused by using a point estimate to represent a large-scale spatial average. Most previous validation studies of landscape FT state have neglected representativeness errors entirely, resulting in conservative estimates of satellite retrieval skill. In this study, we use a variant of triple collocation called ‘categorical triple collocation’ – a technique that uses model, satellite and in-situ estimates to obtain relative performance rankings of all three products, without neglecting representativeness errors – to validate the SMAP landscape FT product. Performance rankings are obtained for nine sites at northern latitudes. We also investigate differences between using air or soil temperatures to estimate FT state, and between using morning (6 AM) or evening (6 PM) estimates. Overall, at most sites, the SMAP product or in-situ FT measurement is ranked first, and the model FT product is ranked last (although rankings vary across sites). These results suggest SMAP is adding value to model simulations, providing higher-accuracy estimates of landscape FT states compared to models and, in some cases, even in-situ estimates, when representativeness errors are properly accounted for in the validation analysis.
Supervised methods of image segmentation accuracy assessment in land cover mapping Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-11 Hugo Costa, Giles M. Foody, Doreen S. Boyd
Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the basic spatial unit of analysis. As it is typically desirable to know the accuracy with which the objects have been delimited prior to undertaking the classification, numerous methods have been used for accuracy assessment. This paper reviews the state-of-the-art of image segmentation accuracy assessment in land cover mapping applications. First the literature published in three major remote sensing journals during 2014–2015 is reviewed to provide an overview of the field. This revealed that qualitative assessment based on visual interpretation was a widely-used method, but a range of quantitative approaches is available. In particular, the empirical discrepancy or supervised methods that use reference data for assessment are thoroughly reviewed as they were the most frequently used approach in the literature surveyed. Supervised methods are grouped into two main categories, geometric and non-geometric, and are translated here to a common notation which enables them to be coherently and unambiguously described. Some key considerations on method selection for land cover mapping applications are provided, and some research needs are discussed.
The global forest/non-forest map from TanDEM-X interferometric SAR data Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-11 Michele Martone, Paola Rizzoli, Christopher Wecklich, Carolina González, José-Luis Bueso-Bello, Paolo Valdo, Daniel Schulze, Manfred Zink, Gerhard Krieger, Alberto Moreira
In this paper we present the activities performed at the Microwaves and Radar Institute of the German Aerospace Center (DLR) to derive global forest/non-forest classification mosaics from interferometric synthetic aperture radar (InSAR) data acquired by the TanDEM-X mission. The data have been collected between 2011 and 2016 in bistatic stripmap single polarization (HH) mode, with the main goal of generating a consistent, timely, and highly accurate 3D representation of the global terrain’s surface (digital elevation model, DEM). The global data set of quicklook images, which represent a spatially averaged version of the original full resolution data at a ground independent pixel spacing of 50 m × 50 m, was used as input, in order to limit the computational burden. For classification purposes, several observables, systematically provided by the TanDEM-X system, can be exploited, such as the calibrated amplitude, the digital elevation model (DEM), and the interferometric coherence. Among the several factors contributing to a coherence degradation in InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering phenomena, which typically occur in presence of vegetation. This quantity is directly derived from the interferometric coherence and used as main indicator for the identification of vegetated areas. For this purpose, a fuzzy multi-clustering classification approach, which takes into account the geometry and acquisition configuration, is applied to each acquired scene separately. A certain variability of the interferometric coherence at X band was observed among different forest types, mainly due to changes in forest structure, density, and tree height, which led to an adjustment of the algorithm settings depending on the considered type of forest. The identification of additional information layers, such as urban settlements or water areas, is also discussed, and the procedure for mosaicking of overlapping acquisitions (two at global scale, up to ten over mountainous terrain, forests, and desert regions) to improve the classification accuracy is detailed. The resulting global forest/non-forest map was validated using external reference information as well as with other existing classification maps and an overall agreement was observed that often exceeds 90%. Finally, examples for high-resolution (at 12 m × 12 m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEM-X bistatic system for a broad range of geoinformation services and scientific applications. The global TanDEM-X forest/non-forest map presented in this paper will be made available to the scientific community for free download and usage.
Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to application Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-11 Helge Aasen, Andreas Bolten
Development of a support vector machine based cloud detection method for MODIS with the adjustability to various conditions Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-11 Haruma Ishida, Yu Oishi, Keitaro Morita, Keigo Moriwaki, Takashi Y. Nakajima
Common requirements for cloud detection methods including the adjustability with respect to incorrect results are clarified, and a method is proposed that satisfies the requirements by applying the support vector machine (SVM). Because the conditions of clouds and Earth's surfaces vary widely, incorrect results in actual cloud detection operations are unavoidable. Cloud detection methods therefore should be adjustable to easily reduce the frequency of incorrect results under certain conditions, without causing new incorrect results under other conditions. Cloud detection methods are also required to resolve a characteristic issue: the boundary between clear-sky and cloudy-sky areas in nature is vague, because the density of the cloud particles continuously varies. This vagueness makes the cloud definition subjective. Furthermore, the training dataset preparation for machine learning should avoid circular arguments. The SVM learning is generally less likely to result in overfitting: this study suggests that only typical data are sufficient for the SVM training dataset. By incorporating the discriminant analysis (DA), it is possible to subjectively determine the definition of typical cloudy and clear sky and to obtain typical cloud data without direct cloud detection. In an approach to adjust the classifier, data typical of certain conditions that lead to incorrect results are added to the training dataset. In this study, an adjustment procedure is proposed, which quantitatively judges, whether an addition is actually effective for reduction of the frequency of incorrect results. Another approach for the adjustment is improving feature space used for cloud detection. Indices as quantitative guidance to estimate whether an addition or elimination of a feature actually reduces the frequency of incorrect results can be obtained from the analysis of the support vectors. The cloud detection method incorporating the SVM is therefore able to integrate practical adjustment procedures. Applications of this method to Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrate that the concept of the method satisfies the requirements and the adjustability to various conditions can be realized.
Quantitative mapping of groundwater depletion at the water management scale using a combined GRACE/InSAR approach Remote Sens. Environ. (IF 6.265) Pub Date : 2017-12-12 Pascal Castellazzi, Laurent Longuevergne, Richard Martel, Alfonso Rivera, Charles Brouard, Estelle Chaussard
GRACE gravity variation recovery and InSAR-derived ground displacement data show promise in supporting and assessing groundwater management policies. However, GRACE system's resolution is too low, and the inversion of InSAR data into volume of groundwater storage loss requires extensive and often unavailable lithological data. Here we present how InSAR can be used to constrain and spatially focus GRACE-derived groundwater mass loss to depletion areas, reducing the gap between the GRACE scale and the typical water management scales. While we highlight the tremendous potential of a fully geodetic, quantitative, and high resolution mapping of groundwater storage loss, we also point out the crucial need for producing guidelines on the proper GRACE solution to use for any study area and/or application. In order to illustrate the GRACE/InSAR combination procedure, we present a case study in Central Mexico, where groundwater depletion of ~ 5000 Million Cubic Meters per year (MCM/yr) is reported by the water governance agencies and is well documented in the scientific literature. However, in this region, not all GRACE solutions provide reasonable groundwater depletion estimates. Using two of them, an inversion is performed to focus the groundwater-related GRACE signal over different mass distribution maps. Several mass distributions are tested, including two from InSAR-derived aquifer compaction mapping. The results show that the regions of Mexico City and Bajio, an agricultural and industrial corridor 250 km North of Mexico City, are the main contributors to the regional groundwater depletion. The mass distribution map produced directly from InSAR leads to results closer to official groundwater budgets than the others tested.
Land cover classification and wetland inundation mapping using MODIS Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-11 Courtney A. Di Vittorio, Aris P. Georgakakos
Hydrologic models of wetlands enable water resources managers to quantify the environmental and societal roles of wetlands and manage them in ways that sustain their valuable services. However, reliable wetland models require data that are not typically available from in-situ measurements. In this article, we use satellite information from MODIS (500-meter, 8-day composite land surface reflectance product) and limited ground data to quantify the seasonal and inter-annual changes of wetland extent. This information is used to calibrate a new, non-parametric land cover classification approach. Extensive tests demonstrate that the new approach performs well in (i) classifying accurately land cover classes and (ii) delineating reliably seasonal and inter-annual wetland area changes. The new approach is applied to the Sudd wetland in South Sudan, a vast wetland of vital socioeconomic and environmental services, toward developing better, policy-relevant information and tools.
Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-11 C. Gomez, K. Adeline, S. Bacha, B. Driessen, N. Gorretta, P. Lagacherie, J.M. Roger, X. Briottet
The use of digital soil mapping, with the help of spectroscopic data, provides a non-destructive and cost-efficient alternative to soil property laboratory measurements. Visible, near-infrared and short wave infrared (VNIR/SWIR, 400–2500 nm) hyperspectral imaging is one of the most promising tools for topsoil property mapping. The aim of this study was to test the sensitivity of soil property prediction results to coarsening image spectral resolution. This may offer an analysis of the potential of forthcoming hyperspectral satellite sensors, e.g., HYPerspectral X IMagery (HYPXIM) or Environmental Mapping and Analysis Program (EnMAP), and existing multispectral sensors, e.g., SENTINEL-2 Multispectral Sensor Instrument (MSI) or LANDSAT-8 Operational Land Imager (OLI), for soil properties mapping. This study used VNIR/SWIR hyperspectral airborne data acquired by the AISA-DUAL sensor (initial spectral and spatial resolutions of approximately 5 nm and 5 m, respectively) over a 300 km2 Mediterranean rural region. Ten spectral configurations were built and divided in the following two groups: i) six spectral configurations corresponding to simulated sensors with regular spectral resolution from 5 nm to 200 nm (i.e., the Full Width at Half Maximum (FWHM) remains constant throughout the considered spectral domain; this includes the simulation of the forthcoming HYPXIM and EnMAP hyperspectral satellites) and ii) four spectral configurations corresponding to existing multispectral sensors with irregular spectral resolution (i.e., the FWHM differs from spectral sampling interval; Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SENTINEL-2 MSI, LANDSAT-7 Enhanced Thematic Mapper (ETM +) and LANDSAT-8 OLI). The soil property studied in this paper is the clay content, defined as the percentage of granulometric fraction finer than 2 μm by weight of the soil, which will be estimated using the partial least squares regression method. Our results showed that i) spectral configurations with regular spectral resolutions from 5 to 100 nm provided similar and good clay content prediction performances (R2val > 0.7 and RPIQ > 3) and allowed clay mapping with correct short-scale variations, ii) the spectral configuration with a regular spectral resolution of 200 nm provided unsatisfactory clay content prediction performance (R2val ≃ 0.01 and RPIQ ≃ 1.65) and iii) the ASTER sensor was the only existing multispectral sensor that provided both correct performance of clay content estimation (R2val ≃ 0.8 and RPIQ ≃ 3.72) and correct clay mapping. Therefore, clay mapping by the ASTER multispectral data should be pursued while awaiting the launch of forthcoming hyperspectral satellite sensors (e.g., HYPXIM and EnMAP), which will be good candidates for future large clay mapping campaigns over bare soils.
Spatio-temporal fusion for daily Sentinel-2 images Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-06 Qunming Wang, Peter M. Atkinson
Sentinel-2 and Sentinel-3 are two newly launched satellites for global monitoring. The Sentinel-2 Multispectral Imager (MSI) and Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensors have very different spatial and temporal resolutions (Sentinel-2 MSI sensor 10 m, 20 m and 60 m, 10 days, albeit 5 days with 2 sensors, conditional upon clear skies; Sentinel-3 OLCI sensor 300 m, < 1.4 days with 2 sensors). For local monitoring (e.g., the growing cycle of plants) one either has the desired spatial or temporal resolution, but not both. In this paper, spatio-temporal fusion is considered to fuse Sentinel-2 with Sentinel-3 images to create nearly daily Sentinel-2 images. A challenging issue in spatio-temporal fusion is that there can be very few cloud-free fine spatial resolution images temporally close to the prediction time, or even available, strong temporal (i.e., seasonal) changes may exist. To this end, a three-step method consisting of regression model fitting (RM fitting), spatial filtering (SF) and residual compensation (RC) is proposed, which is abbreviated as Fit-FC. The Fit-FC method can be performed using only one Sentinel-3–Sentinel-2 pair and is advantageous for cases involving strong temporal changes (i.e., mathematically, the correlation between the two Sentinel-3 images is small). The effectiveness of the method was validated using two datasets. The created nearly daily Sentinel-2 time-series images have great potential for timely monitoring of highly dynamic environmental, agricultural or ecological phenomena.
Estimating surface soil moisture from SMAP observations using a Neural Network technique Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-11 J. Kolassa, R.H. Reichle, Q. Liu, S.H. Alemohammad, P. Gentine, K. Aida, J. Asanuma, S. Bircher, T. Caldwell, A. Colliander, M. Cosh, C. Holifield Collins, T.J. Jackson, J. Martínez-Fernández, H. McNairn, A. Pacheco, M. Thibeault, J.P. Walker
A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2–3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m3m −3, 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m3m −3, 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.
Atmospheric correction for hyperspectral ocean color retrieval with application to the Hyperspectral Imager for the Coastal Ocean (HICO) Remote Sens. Environ. (IF 6.265) Pub Date : 2017-11-06 Amir Ibrahim, Bryan Franz, Ziauddin Ahmad, Richard Healy, Kirk Knobelspiesse, Bo-Cai Gao, Chris Proctor, Peng-Wang Zhai
The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's SeaWiFS Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/.
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