Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest

Author:

He Wenjie12,Xiao Zhiwei34,Lu Qikai1256ORCID,Wei Lifei125,Liu Xing1

Affiliation:

1. Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China

2. Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China

3. Tianjin Institute of Geological Survey, Tianjin 300191, China

4. Tianjin Monitoring Central Station of Geological Environment, Tianjin 300191, China

5. Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 410118, China

6. Key Laboratory of Digital Mapping and Land Information Application, Ministry of Natural Resources, Wuhan University, Wuhan 430079, China

Abstract

Soil particle size fractions (PSFs) are important properties for understanding the physical and chemical processes in soil systems. Knowledge about the distribution of soil PSFs is critical for sustainable soil management. Although log-ratio transformations have been widely applied to soil PSFs prediction, the statistical distribution of original data and the transformed data given by log-ratio transformations is different, resulting in biased estimates of soil PSFs. Therefore, multivariate random forest (MRF) was utilized for the simultaneous prediction of soil PSFs, as it is able to capture dependencies and internal relations among the three components. Specifically, 243 soil samples collected across the Loess Plateau were used. Meanwhile, Landsat data, terrain attributes, and climatic variables were employed as environmental variables for spatial prediction of soil PSFs. The results depicted that MRF gave satisfactory soil PSF prediction performance, where the R2 values were 0.62, 0.53, and 0.73 for sand, silt, and clay, respectively. Among the environmental variables, nighttime land surface temperature (LST_N) presented the highest importance in predicting soil PSFs in the Loess Plateau, China. Maps of soil PSFs and texture were generated at a 30 m resolution, which can be utilized as alternative data for soil erosion management and ecosystem conservation.

Funder

Open Fund of Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources

Open Research Fund Program of the Key Laboratory of Digital Mapping and Land Information Application, Ministry of Natural Resources

Scientific Research Project of Hubei Provincial Education Department

Natural Science Foundation of Hubei Province

Hubei Key Research and Development Program

National Natural Science Foundation of China

Opening Foundation of Hubei Key Laboratory of Regional Development and Environmental Response

Teaching Research Project of Hubei University

Publisher

MDPI AG

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