文献类型：期刊文献

英文题名：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：Wu, Huoqi[1];An, Shuai[1];Meng, Bin[1];Chen, Xiaoqiu[2];Li, Fangjun[3];Ren, Shillong[4]

第一作者：Wu, Huoqi

通讯作者：An, S[1]

机构：[1]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China;[2]Peking Univ, Coll Urban & Environm Sci, Lab Earth Surface Proc, Minist Educ, Beijing 100871, Peoples R China;[3]South Dakota State Univ, Geospatial Sci Ctr Excellence GSCE, Dept Geog & Geospatial Sci, Brookings, SD 57007 USA;[4]Shandong Univ, Environm Res Inst, Qingdao 266237, Peoples R China

第一机构：北京联合大学应用文理学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China.|[114172]北京联合大学应用文理学院;[11417]北京联合大学;

年份：2024

卷号：130

外文期刊名：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

收录：;Scopus(收录号：2-s2.0-85193635640);WOS:【SCI-EXPANDED(收录号:WOS:001284302200001)】；

基金：National Natural Science Foundation of China (No. 42001101) ; the R&D Program of Beijing Municipal Education Commission (KM202111417013) ; Academic Research Projects of Beijing Union University (No.ZKZD202305) .

语种：英文

外文关键词：Grassland aboveground biomass; Statistical model; Machine learning; Linear trend; Spatiotemporal patterns

摘要：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 R-2 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 the alpine vegetation region and the temperate desert region. (4) While approximately 61.94 % of grassland pixels show an increasing trend in average annual maximum AGB from 2000 to 2021, significant (P < 0.05) changes are mainly concentrated in the eastern areas of each ecoregion. Our study presents a valuable framework for estimating grassland aboveground biomass using satellite remote sensing and in situ quadrat datasets. Additionally, it provides a robust product of annual maximum AGB for China's grasslands from 2000 to 2021, contributing to our understanding of long-term AGB changes in China's grassland ecosystems.