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  4. 中科院海岸带环境过程与生态修复重点实验室
Soil salinity estimation incorporating environmental covariables using UAV remote sensing for precision field management

文献类型：期刊论文

作者Ma, Weitong1,6; Han, Wenting1,5; Cui, Xin4,5; Zhang, Huihui3; Zhang, Liyuan2; Dong, Yuxin1,5; Zhai, Xuedong1,5
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2025-10-01
卷号237页码:13
关键词Unmanned aerial vehicle Field soil monitoring Soil salinity Machine learning Environmental factors
ISSN号0168-1699
DOI10.1016/j.compag.2025.110532
通讯作者Han, Wenting(hanwt2000@126.com) ; Cui, Xin(xcui@yic.ac.cn)
英文摘要Timely and precise identification of the extent and intensity of field soil salinization is crucial for effective prevention and treatment. It also supports decision-making for rational irrigation planning, crop yield prediction, and precision field management. This study explored the potential of environmental covariates and spectral variables derived from unmanned aerial vehicle (UAV) multispectral images for estimating field soil salt content (SSC). Aerial and field campaigns were conducted in 18 study areas in October 2021 and April 2022 on bare farmland, simultaneously capturing ground truth data for SSC, soil water content (SWC), and soil surface roughness (SSR). The sensitivity of eleven salinity indices (SIs), ten spectral indices (VIs), and two environmental covariates to SSC in different periods were analyzed using the Pearson's correlation coefficient method and the recursive feature elimination algorithm (RFE). The optimal parameter combination was selected as input variables, and SSC estimation was performed using linear regression model, random forest regression (RFR), artificial neural network (ANN) and support vector regression (SVR) algorithms. Results showed that SIs related to blue and red bands exhibited a strong correlation with SSC, while environmental covariate SSR showed an indirect correlation. The spectral characteristics of the soil in pre-seeding and post-harvest periods had different sensitivity responses to SSC. Among the machine learning algorithms tested, all outperformed the linear regression model in multi-parameter SSC estimation, with the SVR_SSC model demonstrating the highest accuracy (R2 < 0.72, RMSE < 0.15 %, RPD > 1.73, LCCC > 0.77). This study introduced a comprehensive method for SSC estimation that integrates environmental covariates and provides a valuable reference for the accurate assessment of field soil salinization and precision agriculture management.
WOS关键词RANDOM FOREST ; VEGETATION ; COMBINATIONS ; SALINIZATION ; REFLECTANCE ; COEFFICIENT ; REGRESSION ; BIOMASS ; SPACE
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:001501933700005
资助机构National Natural Science Founda-tion of China ; Key Research and Development Project of Shaanxi Province
源URL[http://ir.yic.ac.cn/handle/133337/41295]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
通讯作者Han, Wenting; Cui, Xin
作者单位1.Northwest A&F Univ, Inst Water Saving Agr Arid Areas China, Yangling 712100, Shaanxi, Peoples R China
2.Jiangsu Univ, Sch Agr Engn, Zhenjiang 210031, Jiangsu, Peoples R China
3.USDA ARS, Water Management & Syst Res Unit, 2150 Ctr Ave,Bldg D, Ft Collins, CO 80526 USA
4.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
5.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China
6.Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Weitong,Han, Wenting,Cui, Xin,et al. Soil salinity estimation incorporating environmental covariables using UAV remote sensing for precision field management[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2025,237:13.
APA Ma, Weitong.,Han, Wenting.,Cui, Xin.,Zhang, Huihui.,Zhang, Liyuan.,...&Zhai, Xuedong.(2025).Soil salinity estimation incorporating environmental covariables using UAV remote sensing for precision field management.COMPUTERS AND ELECTRONICS IN AGRICULTURE,237,13.
MLA Ma, Weitong,et al."Soil salinity estimation incorporating environmental covariables using UAV remote sensing for precision field management".COMPUTERS AND ELECTRONICS IN AGRICULTURE 237(2025):13.

入库方式： OAI收割

来源：烟台海岸带研究所

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