Multispectral Remote Sensing Monitoring Methods for Soil Fertility Assessment and Spatiotemporal Variation Characteristics in Arid and Semi-Arid Mining Areas

Quanzhi Li, Zhenqi Hu (), Yanwen Guo and Yulong Geng
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Quanzhi Li: School of Geosciences & Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
Zhenqi Hu: School of Geosciences & Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
Yanwen Guo: School of Environment Science & Spatial Informatics, China University of Mining & Technology, Xuzhou 221116, China
Yulong Geng: School of Geosciences & Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China

Land, 2025, vol. 14, issue 8, 1-23

Abstract: Soil fertility is the essential attribute of soil quality. Large-scale coal mining has led to the continuous deterioration of the fragile ecosystems in arid and semi-arid mining areas. As one of the key indicators for land ecological restoration in these coal mining regions, rapidly and accurately monitoring topsoil fertility and its spatial variation information holds significant importance for ecological restoration evaluation. This study takes Wuhai City in the Inner Mongolia Autonomous Region of China as a case study. It establishes and evaluates various soil indicator inversion models using multi-temporal Landsat8 OLI multispectral imagery and measured soil sample nutrient content data. The research constructs a comprehensive evaluation method for surface soil fertility based on multispectral remote sensing monitoring and achieves spatiotemporal variation analysis of soil fertility characteristics. The results show that: (1) The 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version)-SVM (Support Vector Machine) prediction model for surface soil indicators based on Landsat8 OLI imagery achieved prediction accuracy with R 2 values above 0.85 for all six soil nutrient contents in the study area, thereby establishing for the first time a rapid assessment method for comprehensive topsoil fertility using multispectral remote sensing monitoring. (2) Long-term spatiotemporal evaluation of soil indicators was achieved: From 2015 to 2025, the spatial distribution of soil indicators showed certain variability, with soil organic matter, total phosphorus, available phosphorus, and available potassium contents demonstrating varying degrees of increase within different ranges, though the increases were generally modest. (3) Long-term spatiotemporal evaluation of comprehensive soil fertility was accomplished: Over the 10 years, Grade IV remained the dominant soil fertility level in the study area, accounting for about 32% of the total area. While the overall soil fertility level showed an increasing trend, the differences in soil fertility levels decreased, indicating a trend toward homogenization.

Keywords: soil quality assessment; remote sensing inversion; 6SV; mine land reclamation; ecological monitoring in mining areas (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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