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.
Sci Total Environ. 2024 Jan 20;909:168594. doi: 10.1016/j.scitotenv.2023.168594. Epub 2023 Nov 14.
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.
准确估算草地叶面积指数（LAI）、植被分数（FVC）和地上生物量（AGB）是草地研究的基础。新发射的 Sentinel-3（S3）上的海洋和陆地颜色成像仪（OLCI）传感器提供了与 MODIS 数据具有可比空间和光谱分辨率的图像。然而，很少评估 S3 OLCI 成像仪在植被变量估算中的应用。本研究评估了 S3 OLCI 和 MODIS 数据在估算欧亚大陆东部草原 LAI、FVC 和 AGB 中的潜力。使用贝叶斯空间模型（带随机偏微分方程的集成嵌套拉普拉斯逼近，INLA-SPDE）来解决现场观测数据的空间自相关问题，并增强我们的预测。结果表明，基于 S3 OLCI 数据的模型比基于 MODIS 数据的模型具有更高的精度。LAI、FVC 和 AGB 预测的 RMSE 分别降低了 3.7-10.8%、3.7-7.5%和 1.6-14.2%。通过组合多个预测因子，我们确认了红边波段对草地变量估算的稳健性，与使用传统可见和近红外波段的模型相比，使用红边变量的模型分别使 LAI、FVC 和 AGB 预测的 RMSE 降低了 3.5%、3.2%和 0.4%。与随机森林和随机森林克里金方法相比，INLA-SPDE 空间模型在大多数模型中产生了更低的偏差和更高的预测精度；INLA-SPDE 预测的 LAI 和 FVC 图与地面观测的一致性也优于 MODIS 和 PROBA-V 陆地产品。
