Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms

被引:122
作者
Chen, Di [1 ,2 ,3 ]
Chang, Naijie [1 ,3 ]
Xiao, Jingfeng [3 ]
Zhou, Qingbo [1 ,2 ]
Wu, Wenbin [1 ,2 ]
机构
[1] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
[2] Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China
[3] Univ New Hampshire, Earth Syst Res Ctr, Inst Study Earth Oceans & Space, Durham, NH 03824 USA
基金
美国国家航空航天局; 中国国家自然科学基金;
关键词
Digital soil mapping; Multi-year; Soil organic carbon; MODIS; Machine learning algorithms; Cropland; CARBON STOCKS; TERRAIN ATTRIBUTES; RANDOM FORESTS; REGRESSION; VEGETATION; PREDICTION; VARIABLES; NITROGEN; IMPACTS; QUALITY;
D O I
10.1016/j.scitotenv.2019.03.151
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
As an important indicator of soil quality, soil organicmatter (SOM) significantly contributes to land productivity and ecosystemhealth. Accuratelymapping SOMat regional scales is of critical importance for sustainable agriculture and soil utilization management and remains a grand challenge. Many studies used soil sampling data and machine learning algorithms to predict SOM at regional scales for a given year, while few studies mapped SOM formultiple years and examined its temporal dynamics. We compared the performance of fourmachine learning algorithms: decision tree (DT), bagging decision tree (BDT), randomforest (RF), and gradient boosting regression trees (GBRT) in mapping SOM in Hubei province, China over the 18-year period from 2000 to 2017. Our results showed that RF and DT had the highest coefficient of determination (R-2) (0.61) and the lowest potential bias (9.48 g/kg), respectively, while GBRT had the lowest mean error (ME) (1.26 g/kg), root mean squared error (RMSE) (5.41 g/kg) and Lin's concordance correlation coefficient (LCCC) (0.72). The SOM map based on GBRT better captured the distribution of the soil sample data than that based on RF. The trained GBRT model and the spatially explicitly data on explanatory variables (e.g., climate, terrain, remote sensing) were used to predict SOM for each 500 m x 500 m grid cell in Hubei for the period from 2000 to 2017. Our results showed that the SOM content of cropland was relatively high in the southeast and relatively low in the north. The SOM content in the topsoil varied from 0.89 to 58.86 g/kg and was averaged at 20.52 g/kg. The mean cropland SOM content of the province exhibited an increasing trend from 2000 to 2017 with an increase of 0.26 g/kg and a growth rate of 1.28%. Spatially, the SOMcontent increased in southernHubei and decreased in central and northern parts of the province. A large portion of the areas with decreasing SOM content in northern Hubei was reclaimed cropland, while a large part of the high-quality cropland with rising SOM content in the east (similar to 0.45 x 10(4) ha) was lost due to land use change (e.g., urbanization). (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:844 / 855
页数:12
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