Retrieval of grassland aboveground biomass across three ecoregions in China during the past two decades using satellite remote sensing technology and machine learning algorithms

作者：Huoqi Wu, Shuai An, Bin Meng, Xiaoqiu Chen, Fangjun Li, Shillong Ren · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2024 · DOI：10.1016/j.jag.2024.103925 · 被引用次数：21 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Remote Sensing and LiDAR Applications

The aboveground biomass (AGB) is closely linked to the carbon cycle in grassland ecosystems worldwide. Accurately quantifying AGB variations is thus essential for assessing grassland carbon sequestration and its feedback on climate change. Although many studies have investigated grassland AGB, they are limited to local areas and few research efforts have been attempted to estimate AGB at large scales with the constraint of in situ quadrat harvested AGB. In this study, we used multi-source satellite remote sensing data from 2000 to 2021 and abundant harvest quadrats data to explore AGB estimation methods and then analyze the spatiotemporal patterns of AGB for various grassland types across China’s three ecoregions. The results indicate that: (1) The temporal resolution improvement of remote sensing data results in a higher correlation between satellite remotely sensed NDVI and in situ AGB. Therefore, the MODIS MCD43A4 dataset with higher temporal resolution has a better fit with the harvesting AGB data. (2) Compared to the statistical methods, the machine learning algorithms exhibit high accuracy in estimating grassland AGB. Among them, the random forest (RF) model performs the most robustly, with the highest R2 of 0.83 (explaining 83 % of the variation of the harvesting AGB), and the lowest RMSE of 43.84 gm−2. (3) The multi-year average annual maximum grassland AGB decreases from the southeast to the northwest, with the temperate steppe region having the highest, followed by ...