Improved soil organic matter monitoring by using cumulative crop residue indices derived from time-series remote sensing images in the central black soil region of China,Soil and Tillage Research

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Improved soil organic matter monitoring by using cumulative crop residue indices derived from time-series remote sensing images in the central black soil region of China
Soil and Tillage Research ( IF 6.1 ) Pub Date : 2024-11-13 , DOI: 10.1016/j.still.2024.106357
Mei-Wei Zhang, Xiao-Lin Sun, Mei-Nan Zhang, Hao-Xuan Yang, Huan-Jun Liu, Hou-Xuan Li

Soil organic matter (SOM) determines soil fertility and functions, playing a key role in agriculture, the environment and climate change. During the past century, the SOM of the world, e.g., the black soil (Mollisol) in croplands of Northeast China, experienced extensive changes, making SOM monitoring crucial. Recently, digital soil mapping (DSM) with time-series remote sensing images has become a mainstream method for SOM monitoring, but there is room for its accuracy to be improved. To fulfill this purpose, we propose utilizing crop residue indices (CRIs) derived from remote sensing images within the method, as crop residues are a main source of the SOM. In this study, performances of five commonly used CRIs, e.g., normalized difference tillage index (NDTI), on SOM monitoring was evaluated based on a series of topsoil samples collected from 2014 to 2018 in croplands of the center black soil region in Northeast China. The performances and those of cumulative CRIs computed over some years were compared to those of basic climate and terrain attributes, spectral bands, an empirical index, and commonly used vegetation indices (VIs, e.g., normalized difference vegetation index (NDVI)). Results showed that temporal CRIs had a stronger correlation with SOM content (0.52–0.73) than did the others (0.04–0.69). Integrating CRIs with basic soil covariates increased prediction accuracy by 7.27 % in Lin’s concordance correlation coefficient (CCC). Further, the CRIs and VIs accumulated over 3 and 4 years, respectively, had a much stronger correlation with SOM (0.65–0.73 and 0.67–0.69, respectively) and led to better accuracies with an average increase of 2.62 % in CCC compared to indices of the current sampling year. While annual SOM maps predicted with and without the optimal cumulative CRI showed similar spatial patterns, they were statistically significantly different. It is recommended to utilize the cumulative NDTI for monitoring SOM.

更新日期：2024-11-13

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