High-resolution global soil salinity and sodicity mapping (1980-2024): Box-Cox-based sample optimization, multi-source remote sensing features, and uncertainty quantification
文献类型：期刊论文
| 作者 | Wang, Tiantian1,3; Dong, Jinwei1,3; Lyu, Binyan1,3; Gao, Xuan1; Wang, Nan2; Shi, Zhou2 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 330页码:114991 |
| 关键词 | Soil salinity Soil sodicity Box-Cox transformation Time-series analysis |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2025.114991 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Global soil salinization and sodification threaten food security by causing excessive salt accumulation in soils, degrading productivity. However, their spatiotemporal patterns have not been well documented due to the lack of high-accuracy estimates of soil salinity and sodicity with a long-term perspective. Here we propose a novel framework combining the Box-Cox transformation that addresses the skewed distribution of samples and more critical predictors as well as remote sensing indices, to predict global soil salinity (electrical conductivity of the saturated soil extract, ECe) and sodicity (exchangeable sodium percentage, ESP) from 1980 to 2024 with a 1 km x 1 km resolution using random forest. Model accuracy is higher than previous studies, with root mean square error (RMSE) of ECe and ESP as 2.24 and 6.04 respectively, and an R2 of 0.65, and 0.60 respectively. The implementation of the Box-Cox transformation significantly improved the model performance (R2) by approximately 100 % (from 0.35 to 0.79 for ECe and 0.59 to 0.85 for ESP), while the additional predictors further enhanced the performance (R2 increased by 15 %), ranking in the top 30 % of the feature importance list. Results revealed that global multiple-year average salinization and sodification are primarily concentrated in arid regions characterized by low precipitation and high temperatures. We also found a significant increasing trend of soil salinization in 20 % of global land and of sodification in 48 % of global land from 1980 to 2024, both most pronounced near the equator, as well as in central and eastern North America, Europe, southeastern China, and Mongolia. This study provides updated long-term soil salinity and sodicity maps with improved accuracies, offering critical insights for sustainable land management under climate change, serving as an essential resource for addressing food security and land degradation challenges worldwide. |
| URL标识 | 查看原文 |
| WOS关键词 | HYDRAULIC CONDUCTIVITY ; ELECTROMAGNETIC INDUCTION ; PROFILE DATA ; TIME-SERIES ; PREDICTION ; DYNAMICS ; IRRIGATION ; OASIS ; ACCURACY ; PATTERNS |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001562199200004 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216102]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Dong, Jinwei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Zhejiang Univ, Inst Agr Remote Sensing & Informat Tech Applicat, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, Tiantian,Dong, Jinwei,Lyu, Binyan,et al. High-resolution global soil salinity and sodicity mapping (1980-2024): Box-Cox-based sample optimization, multi-source remote sensing features, and uncertainty quantification[J]. REMOTE SENSING OF ENVIRONMENT,2025,330:114991. |
| APA | Wang, Tiantian,Dong, Jinwei,Lyu, Binyan,Gao, Xuan,Wang, Nan,&Shi, Zhou.(2025).High-resolution global soil salinity and sodicity mapping (1980-2024): Box-Cox-based sample optimization, multi-source remote sensing features, and uncertainty quantification.REMOTE SENSING OF ENVIRONMENT,330,114991. |
| MLA | Wang, Tiantian,et al."High-resolution global soil salinity and sodicity mapping (1980-2024): Box-Cox-based sample optimization, multi-source remote sensing features, and uncertainty quantification".REMOTE SENSING OF ENVIRONMENT 330(2025):114991. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。