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Mapping cropland soil salinity using multi-cycle classification to mitigate retrieval errors

文献类型：期刊论文

作者Gao, Liaoran1,2; Xu, Erqi2
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2025-04-01
卷号231页码:110055
关键词Soil salinization Remote sensing Multi-cycle classification Machine learning
ISSN号0168-1699
DOI10.1016/j.compag.2025.110055
产权排序1
文献子类Article
英文摘要Soil salinization is a primary factor limiting agricultural development in arid regions. Accurately predicting soil salinity in croplands using remote sensing is advantageous for precise agricultural management and sustainable development; however, limited separability between non-salinized and mild-salinized croplands in remotesensing images, combined with the impacts of environmental conditions and agricultural practices, results in existing methods exhibiting low accuracy in predicting cropland soil salinity. This study develops a two-step strategy involving classification and retrieval. Initially, a multi-cycle classification method is established, dividing the original sample set into rested sample set (R) obtained through multiple sample deletion cycles and a deleted sample set (D). Separate classifiers were trained on the R and D sample sets, and rules were generated to distinguish areas where these classifiers are applicable. Subsequently, the soil salinity in salinized croplands was predicted using the optimal remote sensing retrieval features, with Pearson correlation coefficients (PCCs) and backward feature elimination. Then, machine learning techniques were applied to predict soil salinity. The proposed method achieved superior classification accuracy for salinized cropland (Fsalinized = 0.82, Fnon-salinized = 0.87) compared with traditional methods such as random forest (RF) (Fsalinized = 0.74, Fnon-salinized = 0.64) and 1D-CNN (Fsalinized = 0.76, Fnon-salinized = 0.65). The R2 value of the proposed method in soil salinity retrieval in salinized croplands was 0.66, outperforming the R2 for the salinity retrieval results without classification obtained using traditional methods (0.23). The two-step strategy proposed in this study provides a feasible and highly accurate approach for distinguishing mild-salinized and non-salinized croplands, thus enhancing cropland soil salinity content (SSC) retrieval accuracy.
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WOS关键词VEGETATION INDEX
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:001423105000001
出版者ELSEVIER SCI LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/212281]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Xu, Erqi
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Gao, Liaoran,Xu, Erqi. Mapping cropland soil salinity using multi-cycle classification to mitigate retrieval errors[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2025,231:110055.
APA Gao, Liaoran,&Xu, Erqi.(2025).Mapping cropland soil salinity using multi-cycle classification to mitigate retrieval errors.COMPUTERS AND ELECTRONICS IN AGRICULTURE,231,110055.
MLA Gao, Liaoran,et al."Mapping cropland soil salinity using multi-cycle classification to mitigate retrieval errors".COMPUTERS AND ELECTRONICS IN AGRICULTURE 231(2025):110055.

入库方式： OAI收割

来源：地理科学与资源研究所

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