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A novel index for mapping crop residue covered cropland using remote sensing data

文献类型： 外文期刊

作者： Zhang, Wenqian 1 ; Li, Wenjuan 1 ; Wang, Cong 1 ; Yu, Qiangyi 1 ; Tang, Huajun 1 ; Wu, Wenbin 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arable Land China, Beijing 100081, Peoples R China

2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China

关键词： Crop residue covered cropland mapping; Crop residue covered spectral index (CRCSI); Spectral analysis; Multi-band index

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 231 卷

页码：

收录情况： SCI

摘要： Crop residue cover is a key measure in conservation agriculture, protecting soil from erosion and enhancing fertility. Accurate and rapid mapping of crop residue covered cropland (CRCC) is crucial for monitoring tillage practices and planning sustainable agricultural development. However, CRCC mapping still remains challenging. Compared with classifier-based methods and crop residue coverage-based methods, the index-based methods offer rapid processing and convenience, making them suitable for CRCC mapping. Most existing indices are limited by coarse-resolution data, limited band information, and poor background differentiation. To solve these problems, this study proposes a crop residue covered spectral index (CRCSI) based on spectral and separability analysis of Sentinel-2A images. CRCSI is a 3-band index, designed to widen the difference between CRCC and background for best mapping CRCC. Three study cases in different seasons and regions were selected to develop and assess the performance of the CRCSI. Specifically, we verify the reliability and superiority of the index from three aspects: visual evaluation, separability analysis and CRCC mapping results. The experimental results indicate that, compared with the existing indices, CRCSI has obvious advantages in enhancing CRCC information and suppressing background, and can successfully detect CRCC. In the three study cases, the overall accuracy of 88% - 95%, Kappa is 0.67 - 0.89, and F1 score of 0.74 - 0.92 were achieved when applying CRCSI to map CRCC. The results show that CRCSI can be used for mapping CRCC in medium-to-high resolution images across seasons and regions, offering an effective method to monitor CRCC and conservation tillage with global application potential.

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