Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model
Li Z. Ding L. Shen B. Chen J. Xu D. Wang X. Fang W. Pulatov A. Kussainova M. Amarjargal A. Isaev E. Liu T. Sun C. Xin X.
20 January 2024Elsevier B.V.
Science of the Total Environment
2024#909
Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7–10.8 %, 3.7–7.5 %, and 1.6–14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.
Bayesian modeling , Biophysical parameter , Grassland , MODIS , Remote sensing , Sentinel-3
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Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou, 225009, China
Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, 225009, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, 48824, MI, United States
Department of Biology, Pace University, New York, 10038, NY, United States
EcoGIS center, National Research University “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” (NRU-TIIAME), Tashkent, 100000, Uzbekistan
Sustainable Agriculture Center, Kazakh National Agrarian Research University, Almaty, 050010, Kazakhstan
University of the Humanities, Ulaanbaatar, Mongolia
Mountain Societies Research Institute, University of Central Asia, Bishkek, 720001, Kyrgyzstan
Jiangsu Key Laboratory of Crop Genetics and Physiology
Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops
College of Environmental and Resource Sciences
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China
Department of Geography
Department of Biology
EcoGIS center
Sustainable Agriculture Center
University of the Humanities
Mountain Societies Research Institute
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