Using Satellites to Monitor Soil Texture in Typical Black Soil Areas and Assess Its Impact on Crop Growth

Liren Gao, Yuhong Zhang (), Deqiang Zang, Qian Yang, Huanjun Liu and Chong Luo ()
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Liren Gao: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Yuhong Zhang: Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
Deqiang Zang: School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China
Qian Yang: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Huanjun Liu: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Chong Luo: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Agriculture, 2025, vol. 15, issue 9, 1-19

Abstract: Soil texture is an important physical property of soil. Understanding the spatial distribution of cultivated soil texture in black soil areas is crucial for precise agricultural management and cultivated land protection in these zones. This study utilizes the random forest algorithm, Landsat-8 satellite remote sensing data, and climate- and terrain-related environmental covariates to map the spatial distribution of soil texture and analyze its impact on crop growth. The results show that (1) the order of prediction accuracy differs for different soil texture types; April is determined to have the highest prediction accuracy for silt and sand, while May exhibits the greatest accuracy for clay. (2) Adding environmental variables can significantly improve the accuracy of soil texture predictions; the root mean square error (RMSE) has decreased to varying degrees (silt: 0.84; clay: 0.04; sand: 0.85). (3) Soybean growth has the strongest response to soil texture; clay grain is the key factor affecting crop growth in drought scenarios, and sand grain is the dominant factor influencing flooding. This study improves the accuracy of the remote sensing mapping of soil texture through the combination of remote sensing images and environmental variables and quantitatively evaluates the impact of soil texture on crop growth.

Keywords: soil texture; remote sensing mapping; crop grown; random forest (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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