Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model
文献类型： 外文期刊
作者： Li, Zhenwang 1 ; Ding, Lei 3 ; Shen, Beibei 4 ; Chen, Jiquan 5 ; Xu, Dawei 4 ; Wang, Xu 4 ; Fang, Wei 6 ; Pulatov, Alim 7 ; Kussainova, Maira 8 ; Amarjargal, Amartuvshin 9 ; Isaev, Erkin 10 ; Liu, Tao 1 ; Sun, Chengming 1 ; Xin, Xiaoping 4 ;
作者机构： 1.Yangzhou Univ, Agr Coll, Jiangsu Key Lab Crop Genet & Physiol, Jiangsu Key Lab Crop Cultivat & Physiol, Yangzhou 225009, Peoples R China
2.Yangzhou Univ, Jiangsu Coinnovat Ctr Modern Prod Technol Grain Cr, Yangzhou 225009, Peoples R China
3.Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China
4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Natl Hulunber Grassland Ecosyst Observat & Res Stn, Beijing 100081, Peoples R China
5.Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48824 USA
6.Pace Univ, Dept Biol, New York, NY 10038 USA
7.Natl Res Univ, Tashkent Inst Irrigat & Agr Mechanizat Engineers N, EcoGIS Ctr, Tashkent 100000, Uzbekistan
8.Kazakh Natl Agrarian Res Univ, Sustainable Agr Ctr, Alma Ata 050010, Kazakhstan
9.Univ Humanities, Ulaanbaatar, Mongolia
10.Univ Cent Asia, Mt Soc Res Inst, Bishkek 720001, Kyrgyzstan
关键词： Biophysical parameter; Remote sensing; Bayesian modeling; Sentinel-3; MODIS; Grassland
期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：9.8； 五年影响因子：9.6 ）
ISSN： 0048-9697
年卷期： 2024 年 909 卷
页码：
收录情况： SCI
摘要： 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.
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