European Journal of Remote Sensing ( IF 4 ) Pub Date : 2020-05-12 , DOI: 10.1080/22797254.2020.1762247 Xiaoyan Shi 1, 2 , Jianghui Song 1, 2 , Haijiang Wang 1, 2 , Xin Lv 1, 2
ABSTRACT
Large-scale and accurate monitoring soil salinization is essential for controlling soil degradation and sustainable agricultural development. The agricultural irrigation area of the Manas River Basin in the arid area of Northwest China was selected as the test area. The soil salinization monitoring model based on spectral index group was constructed by comparing the accuracy of PCR, PLSR and MLR models using the transformation of multi-spectral index group and index screening. The results showed that there was a certain correlation between the 28 spectral index groups, with a maximum correlation coefficient -0.3689 between the original spectral group and the soil salt content was B10 band. After the transformation of original data for the logarithm Ln(R), exponential eR and square root R1/2 respectively, the correlation between each index and soil salinity was significantly improved, with the maximum correlation coefficient was up to -0.7564 of R1/2. The salt content estimation models were constructed by different data transformation using PLSR, PCR and MLR methods, respectively. This study provides a fast and accurate method for monitoring regional soil salinity content and the results can provide a reference for soil salinity grading management in arid and semi-arid areas.
中文翻译:
基于多光谱指数组的西北玛纳斯河流域土壤盐渍化监测
摘要
大规模准确监测土壤盐渍化对于控制土壤退化和可持续农业发展至关重要。选取西北干旱区玛纳斯河流域农业灌溉区作为试验区。通过多光谱指标组转化和指标筛选,比较PCR、PLSR和MLR模型的准确性,构建了基于光谱指标组的土壤盐渍化监测模型。结果表明,28个光谱指标组之间存在一定的相关性,原始光谱组与土壤含盐量B10波段的相关系数最大为-0.3689。原始数据为对数Ln(R)、指数e R 和平方根R 1/2变换后 各指标与土壤盐分的相关性显着提高,相关系数最大可达R 1/2 的-0.7564 。分别使用PLSR、PCR和MLR方法通过不同的数据转换构建盐含量估计模型。该研究为区域土壤盐分含量的监测提供了一种快速、准确的方法,其结果可为干旱半干旱地区土壤盐分分级管理提供参考。