首页|Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R2 value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R2 value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

soil organic matterSentinel-2monthly synthetic imagesmachine learning modelspatial prediction

Jie Song、Dongsheng Yu、Siwei Wang、Yanhe Zhao、Xin Wang、Lixia Ma、Jiangang Li

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State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210008,China

Chinese Academy of Sciences University,Beijing 100049,China

Agricultural and Rural Bureau of Luanping County,Luanping 068250,China

special project of the National Key Research and Development Program of Chinaspecial project of the National Key Research and Development Program of China

2022YFB39033022021YFC1809104

2024

10.1016/j.jia.2023.09.017

农业科学学报(英文)
中国农业科学院农业信息研究所

农业科学学报(英文)

CSTPCD
影响因子：0.576
ISSN：2095-3119
年,卷(期)：2024.23(4)
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