Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content

被引:0
|
作者
Xiao, Xiao [1 ]
He, Qijin [1 ,2 ]
Ma, Selimai [1 ]
Liu, Jiahong [1 ]
Sun, Weiwei [1 ]
Lin, Yujing [1 ]
Yi, Rui [1 ]
机构
[1] China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Nanjing 210044, Peoples R China
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
基金
中国国家自然科学基金;
关键词
Soil organic carbon; Landsat; 5; TM; Environmental variables; Model comparison; Northeast China transect; PREDICTION; MATTER; SEQUESTRATION; SPECTROSCOPY; REFLECTANCE; CLIMATE; CHINA; MAP;
D O I
10.1038/s41598-024-68424-5
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the "OSR + SVM" model (R2 = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.
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页数:14
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