基于高分五号高光谱影像的干旱区盐渍化土壤盐分含量估算

doi:10.3964/j.issn.1000-0593(2023)07-2278-09

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摘要：目前星载高光谱传感器较少，基于高光谱影像的土壤盐分含量估算研究仍处于探索之中。高分五号（GF-5）是目前国内光谱分辨率最高的卫星。目的是研究高分五号高光谱影像大面积估算干旱区盐渍化土壤盐分含量的可行性。选择新疆焉耆为研究区域，采集了198个土壤样本。首先，检测土壤样本中的全盐量、Na⁺、Ca²⁺、SO^2-₄、Cl^-的含量，利用ASD FieldSpec3地物光谱仪在实验室测定土壤样本的光谱反射率。然后，对实验室土壤光谱进行SG（Savitzky-Golay）平滑预处理，采用竞争自适应重加权采样法选择土壤盐分的特征波段，再利用偏最小二乘、岭回归和支持向量机建立土壤盐分含量的回归模型，发现实验室光谱反演土壤盐分的模型精度较高，五种土壤盐分反演模型的校正集决定系数均大于0.97，预测集决定系数均大于0.90。接着，获取土壤采样同期的高分五号高光谱影像数据并进行预处理，根据采样点位置在影像上提取了198个土壤样本的光谱曲线，使用与实验室光谱相同的反演方法，建立了高分五号影像光谱与土壤盐分的反演模型，五种土壤盐分（全盐量、Na⁺、Ca²⁺、SO^2-₄、Cl^-）反演效果最好的模型预测集决定系数分别是0.76、0.66、0.76、0.63、0.77。最后，根据高分五号影像光谱对土壤盐分的反演结果，选择精度最高的特征波段组合和建模方法，用于整个研究区域的土壤盐分含量估算。估算结果按盐渍化等级划分，研究区盐土占比76%，土地已无法耕作，非盐渍土占比16%，可种植农作物，弱盐渍土、中盐渍土和强盐渍土分布面积较小，共占8%。五种土壤盐分的估算图与全盐量插值图的空间分布趋势一致。结果表明，高分五号高光谱影像估算本研究区域的土壤盐分含量结果可信度较高。

关键词：高分五号；高光谱遥感；土壤盐分含量；大面积估算

Abstract：There are few spaceborne hyperspectral sensors, and the estimation of soil salt content based on hyperspectral images is still under exploration. GF-5 is the satellite with the highest spectral resolution in China. This paper aims to study the feasibility of estimating salt content of saline soil in arid areas on a large area using GF-5 hyperspectral image. In this paper, 198 soil samples were collected from the experimental field at Yanqi, Xinjiang. Firstly, the soil salt contents (total salt content, Na⁺, Ca²⁺, SO^2-₄ and Cl^-) were determined, and the spectra of the soil samples were measured with an ASD Fieldspec3 field spectrometer in the laboratory. Then, the laboratory soil spectra were subjected to SG (Savitzky-Golay) smoothing pretreatment, and the competitive adaptive reweighted sampling method was used to select the characteristic bands of soil salt. Partial least squares, ridge regression and support vector machine established the regression model of soil salt content. It is found that the soil salt retrieval model established by laboratory spectra has high accuracy. The determination coefficients of the correction set and prediction set of the five soil salt retrieval models are greater than 0.97 and 0.90 respectively. Next, the GF-5 hyperspectral image data at the same time as soil sampling are obtained and preprocessed. The spectra of 198 soil samples were extracted from the image based on the location of the sampling points. Soil salt retrieval models based on GF-5 hyperspectral image spectra were established using the same retrieval method of laboratory spectra. The best prediction set determination coefficients of the five soil salt (total salt content, Na⁺, Ca²⁺, SO^2-₄ and Cl^-) retrieval models were 0.76, 0.66, 0.76, 0.63 and 0.77 respectively. Finally, according to the retrieval results of soil salt based on the GF-5 image spectra, the characteristic band combination and modeling method with the best accuracy were selected estimate soil salt content in the whole study area. The estimation results have been divided according to the salinization grade. The saline soil in the study area accounts for 76%, and the land can not be cultivated. Non saline soil accounts for 16%, and crops can be planted. The distribution area of weak, medium and strong saline soil is small, accounting for 8% in total. The spatial distribution trend of the five soil salt estimation maps is consistent with the total salt content interpolation map. This paper shows that the results of estimating soil salt content in this study are based on GF-5 hyperspectral image are highly reliable.

Key words：GF-5; Hyperspectral remote sensing; Soil salt content; Large area estimation

收稿日期: 2022-02-21 修订日期: 2023-04-20

中图分类号:

TP79

基金资助: 国家自然科学基金项目（62071184），自然资源部国土卫星遥感应用重点实验室基金项目（KLSMNR-202104），中国煤炭地质总局碳中和研究院基金项目(ZMKJ-2021-ZX02-01)

通讯作者: 万余庆 E-mail: Wanyuqing@163.com

作者简介: 王惠敏，女，1994年生，西安煤航遥感信息有限公司助理工程师 e-mail: wanghm9423@126.com

引用本文:

王惠敏，于磊，徐凯磊，江晓光，万余庆. 基于高分五号高光谱影像的干旱区盐渍化土壤盐分含量估算[J]. 光谱学与光谱分析, 2023, 43(07): 2278-2286.
WANG Hui-min, YU Lei, XU Kai-lei, JIANG Xiao-guang, WAN Yu-qing. Estimation of Salt Content of Saline Soil in Arid Areas Based on GF-5 Hyperspectral Image. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2278-2286.

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