基于Sentinel-1和Landsat 8遥感数据的高原地区土壤水分反演

doi:10.13474/j.cnki.11-2246.2024.0725

测绘通报 ›› 2024, Vol. 0 ›› Issue (7): 140-146.doi: 10.13474/j.cnki.11-2246.2024.0725

基于Sentinel-1和Landsat 8遥感数据的高原地区土壤水分反演

王霞迎¹, 折育霖¹, 张双成², 夏元平¹, 牛玉芬³

1. 东华理工大学测绘与空间信息工程学院, 江西南昌 330013;
2. 长安大学地质工程与测绘学院, 陕西西安 710054;
3. 河北工程大学矿业与测绘工程学院, 河北邯郸 056038

收稿日期:2023-12-25 发布日期:2024-08-02
通讯作者: 折育霖。E-mail:ylinshe@163.com
作者简介:王霞迎(1989—),女,博士,讲师,主要研究方向为InSAR时序形变模型研究。E-mail:201960018@ecut.edu.cn

Soil moisture inversion in highland areas based on Sentinel-1 and Landsat 8 remote sensing data

WANG Xiaying¹, SHE Yulin¹, ZHANG Shuangcheng², XIA Yuanping¹, NIU Yufen³

1. School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China;
2. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China;
3. School of Mining and Geomatics Engineering, Hebei University of Engineering, Handan 056038, China

Received:2023-12-25 Published:2024-08-02

摘要/Abstract

摘要： 土壤水分是农业生产、水资源管理及全球气候等至关重要的参数。合成孔径雷达是获取水分的重要手段,其中植被和粗糙度作为两大关键影响要素是研究的重点。因此,本文针对以下几个方面展开研究。首先,基于Sentinel-1雷达数据获取总体后向散射系数,基于Landsat 8光学数据的3种植被指数(NDVI、NDWI、MSAVI)分别利用水云模型分离出植被的后向散射系数。然后,利用Dobson模型联合高级积分方程模型(AIEM)建立缺少地表粗糙度的后向散射系数表,并使用最小成本函数得到最优粗糙度参数。最后,使用最小二乘法确定反演土壤水分经验方程的系数。试验结果表明:在高原地区,模型反演结果与地面实测结果均具有较好的一致性,其中使用归一化水指数(NDWI2)输入水云模型联合最优粗糙度反演结果最佳,拟合系数达到0.840 2,均方根误差为0.027 21 cm³/cm³。

关键词: 土壤水分, AIEM, 最优粗糙度, 多源遥感, Dobson

Abstract: Soil moisture is a crucial parameter for agricultural production, water resource management, global climate, and other related fields. Synthetic aperture radar (SAR) serves as a significant mean for acquiring soil moisture, among which, vegetation and surface roughness are two key influencing factors. Therefore, this paper focuses on several aspects. Firstly, using Sentinel-1 radar data to obtain overall backscattering coefficients and which of vegetation are separated utilizing three vegetation indices (NDVI, NDWI, MSAVI) deriving from Landsat 8 optical data using the water-cloud model. Subsequently, the Dobson model is employed in conjunction with the advanced integral equation model (AIEM) to establish a table of backscattering coefficients lacking surface roughness, determining optimal roughness parameters through a minimum-cost function. Finally, a least squares method is used to determine the coefficients of the empirical equation for soil moisture inversion. Experimental results demonstrate that in highland areas, the model's inversion results exhibit good consistency with ground-truth measurements. Among these, the use of the normalized difference water index (NDWI2) as input for the water-cloud model combined with optimal roughness inversion yields the best results, with a fitting coefficient of 0.840 2 and a root mean square error of 0.027 21 cm³/cm³.

Key words: soil moisture, AIEM, optimal roughness, multi-source remote sensing, Dobson

中图分类号:

P237

王霞迎, 折育霖, 张双成, 夏元平, 牛玉芬. 基于Sentinel-1和Landsat 8遥感数据的高原地区土壤水分反演[J]. 测绘通报, 2024, 0(7): 140-146.

WANG Xiaying, SHE Yulin, ZHANG Shuangcheng, XIA Yuanping, NIU Yufen. Soil moisture inversion in highland areas based on Sentinel-1 and Landsat 8 remote sensing data[J]. Bulletin of Surveying and Mapping, 2024, 0(7): 140-146.

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基于Sentinel-1和Landsat 8遥感数据的高原地区土壤水分反演

Soil moisture inversion in highland areas based on Sentinel-1 and Landsat 8 remote sensing data

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