Soil Salt Content Estimation Through Integrated Analysis of ZiYuan1 Hyperspectral and Sentinel‐1 Synthetic Aperture Radar Images

IF 3.7 2区农林科学 Q2 ENVIRONMENTAL SCIENCES

Land Degradation & Development Pub Date : 2025-09-27 DOI:10.1002/ldr.70222

Youxin Sun, Xia Zhang, Weihao Wang, Kun Shang, Songtao Ding

{"title":"Soil Salt Content Estimation Through Integrated Analysis of ZiYuan1 Hyperspectral and Sentinel‐1 Synthetic Aperture Radar Images","authors":"Youxin Sun, Xia Zhang, Weihao Wang, Kun Shang, Songtao Ding","doi":"10.1002/ldr.70222","DOIUrl":null,"url":null,"abstract":"Soil salinization adversely affects soil health and poses a threat to crop growth. Accurately estimating soil salt content (SSC) is of critical importance for achieving sustainable agricultural development. Hyperspectral remote sensing provides abundant spectral information that reflects the spectral reflectance characteristics of soil salinity, while synthetic aperture radar (SAR) data offer backscatter coefficient features associated with soil salinity. This study presents a method for estimating SSC using a combination of ZiYuan1 (ZY1) hyperspectral and Sentinel‐1 SAR data. This approach determines the spectral bands characteristic of soil salinity and extracts them using continuum removal. On this basis, fractional order derivative is applied to enhance spectral features (soil salinity spectral bands and spectral indices), which are then combined with SAR features (backscatter coefficients and radar indices) to estimate SSC using the extremely randomized trees algorithm. Validation of the proposed method was carried out using 84 soil samples and satellite images collected from Zhaoyuan County, Heilongjiang Province, China. Analysis of the results suggests that extracting soil salinity spectral bands reduces data redundancy, enhances the mechanism of the estimation process, and improves estimation accuracy. Compared to using the full spectral range (400–2400 nm), the proposed method increased the coefficient of determination (R2) from 0.56 to 0.69 and the residual predictive deviation (RPD) from 1.51 to 1.80. The incorporation of spectral indices further increased R2 to 0.74, and the combination of spectral features with SAR features improved the estimation accuracy even further, with R2 and RPD increasing to 0.84 and 2.57, respectively. In this work, we develop a novel approach for SSC estimation by combining hyperspectral and SAR data for soil salinization monitoring and evaluation.","PeriodicalId":203,"journal":{"name":"Land Degradation & Development","volume":"78 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Land Degradation & Development","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1002/ldr.70222","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Soil salinization adversely affects soil health and poses a threat to crop growth. Accurately estimating soil salt content (SSC) is of critical importance for achieving sustainable agricultural development. Hyperspectral remote sensing provides abundant spectral information that reflects the spectral reflectance characteristics of soil salinity, while synthetic aperture radar (SAR) data offer backscatter coefficient features associated with soil salinity. This study presents a method for estimating SSC using a combination of ZiYuan1 (ZY1) hyperspectral and Sentinel‐1 SAR data. This approach determines the spectral bands characteristic of soil salinity and extracts them using continuum removal. On this basis, fractional order derivative is applied to enhance spectral features (soil salinity spectral bands and spectral indices), which are then combined with SAR features (backscatter coefficients and radar indices) to estimate SSC using the extremely randomized trees algorithm. Validation of the proposed method was carried out using 84 soil samples and satellite images collected from Zhaoyuan County, Heilongjiang Province, China. Analysis of the results suggests that extracting soil salinity spectral bands reduces data redundancy, enhances the mechanism of the estimation process, and improves estimation accuracy. Compared to using the full spectral range (400–2400 nm), the proposed method increased the coefficient of determination (R2) from 0.56 to 0.69 and the residual predictive deviation (RPD) from 1.51 to 1.80. The incorporation of spectral indices further increased R2 to 0.74, and the combination of spectral features with SAR features improved the estimation accuracy even further, with R2 and RPD increasing to 0.84 and 2.57, respectively. In this work, we develop a novel approach for SSC estimation by combining hyperspectral and SAR data for soil salinization monitoring and evaluation.

查看原文本刊更多论文

基于紫园一号高光谱和哨兵一号合成孔径雷达图像综合分析的土壤含盐量估算

土壤盐碱化对土壤健康产生不利影响，对作物生长构成威胁。准确估算土壤含盐量对实现农业可持续发展至关重要。高光谱遥感提供了丰富的光谱信息，反映了土壤盐分的光谱反射率特征，而合成孔径雷达（SAR）数据提供了与土壤盐分相关的后向散射系数特征。本研究提出了一种利用ZiYuan1 （ZY1）高光谱和Sentinel‐1 SAR数据组合估算SSC的方法。该方法确定土壤盐分特征的光谱带，并采用连续体去除法提取。在此基础上，利用分数阶导数增强光谱特征（土壤盐度光谱带和光谱指数），再结合SAR特征（后向散射系数和雷达指数），利用极随机树算法估算SSC。利用黑龙江省招远县84份土壤样品和卫星影像对该方法进行了验证。结果分析表明，提取土壤盐分谱带减少了数据冗余，增强了估算过程的机制，提高了估算精度。与全光谱范围（400 ~ 2400 nm）相比，该方法将测定系数（R2）从0.56提高到0.69，残差预测偏差（RPD）从1.51提高到1.80。光谱特征与SAR特征的结合进一步提高了估算精度，R2和RPD分别提高到0.84和2.57。在这项工作中，我们开发了一种新的方法，将高光谱和SAR数据结合起来用于土壤盐渍化监测和评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Land Degradation & Development 农林科学-环境科学

CiteScore

7.70

自引率

8.50%

发文量

379

审稿时长

5.5 months

期刊介绍： Land Degradation & Development is an international journal which seeks to promote rational study of the recognition, monitoring, control and rehabilitation of degradation in terrestrial environments. The journal focuses on: - what land degradation is; - what causes land degradation; - the impacts of land degradation - the scale of land degradation; - the history, current status or future trends of land degradation; - avoidance, mitigation and control of land degradation; - remedial actions to rehabilitate or restore degraded land; - sustainable land management.