光学和微波遥感数据联合反演植被覆盖表层土壤含水率

周美玲; 张德宁; 王 浩; 魏 征; 林人财

引用本文:	周美玲,张德宁,王浩,等.光学和微波遥感数据联合反演植被覆盖表层土壤含水率[J].灌溉排水学报,2024,43(1):45-51.
	ZHOU Meiling,ZHANG Dening,WANG Hao,et al.光学和微波遥感数据联合反演植被覆盖表层土壤含水率[J].灌溉排水学报,2024,43(1):45-51.

【打印本页】【下载PDF全文】【查看/发表评论】【EndNote】【RefMan】【BibTex】

←前一篇|后一篇→

过刊浏览高级检索

本文已被：浏览 671次下载 219次	码上扫一扫！
分享到：微信更多字体:加大+\|默认\|缩小-
光学和微波遥感数据联合反演植被覆盖表层土壤含水率
周美玲，张德宁，王浩，魏征，林人财
1.江西省水投江河信息技术有限公司，南昌 330029；2.德州市潘庄灌区运行维护中心，山东德州 253000；3.中国电建集团昆明勘测设计研究院有限公司，昆明 650051； 4.中国水利水电科学研究院，北京 100038

摘要:

【目的】探究Vertical-Vertical（VV）、Vertical-Horizontal（VH）极化及双极化方式对微波遥感反演表层0~10 cm土壤含水率影响，分析不同数据源（Landsat-8, L8; Sentinel-2, S2）得到的归一化植被指数（NDVI）、归一化水体指数（NDWI）对表层土壤含水率遥感反演精度的影响。【方法】基于VV、VH单一极化和双极化模式，结合S2和L8计算的NDVI与NDWI估算植被含水率（VWC），消除植被对土壤的后向散射影响，得到土壤后向散射系数，基于水云模型反演北京市大兴区表层土壤含水率。【结果】对于VV极化，VV+S2NDWI反演0~10 cm土层的土壤含水率精度最高（R2=0.763，RMSE=1.55%）；对于VH极化，VH+S2NDVI反演的0~10 cm土层的土壤含水率精度最高（R2=0.622，RMSE=1.66%）；对于双极化，Dual-Polarized（DP）+S2NDVI反演的0~10 cm土层的土壤含水率精度最高（R2=0.895，RMSE=0.89%）。【结论】NDVI更适用于去除水云模型中的植被影响，且双极化方式反演0~10 cm土层的土壤含水率精度较高。

关键词: 含水率；Sentinel-1；双极化；多源遥感；NDVI；NDWI

DOI：10.13522/j.cnki.ggps.2023312

分类号:

基金项目:

Inversion of surface soil moisture under vegetated areas based on optical and microwave remote sensing data

ZHOU Meiling, ZHANG Dening, WANG Hao, WEI Zheng, LIN Rencai

1. Jiangxi Provincial Water Conservancy InvestmentJiangheInformation Technology Co.,Ltd, Nanchang 330029, China; 2. Operation and Maintenance Center of PanzhuangIrrigation District, Dezhou 253000, China; 3. Kunming Engineering Corporation Limited, Kunming 650051, China; 4. China Institute of Water Resources and Hydropower Research, Beijing 100038, China

Abstract:

【Objective】The effects of Vertical-Vertical (VV) polarization, Vertical-Horizontal (VH) polarization, and dual polarization on microwave remote sensing retrieval of surface soil moisture (0-10 cm) were investigated. And analyze different data sources (Landsat-8, L8; Sentinel-2, S2) Effect of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) on the accuracy of remote sensing estimation of surface soil moisture.【Method】Based on the single polarization of VV and VH and the dual polarization model, NDVI and NDWI calculated by S2 and L8 were used to estimate vegetation water content (VWC), eliminate the influence of vegetation on backscattering, and obtain the soil backscattering coefficient. Then the water cloud model was used to invert the surface soil moisture in Daxing District of Beijing.【Result】For VV polarization, the estimation accuracy of VV+S2NDWI was best (R2=0.763, RMSE=1.55%). For VH polarization, the estimation accuracy of VH+S2NDVI was best (R2=0.622, RMSE=1.66%); The dual polarization (DP) + S2NDVI had a highest accuracy in retrieving surface soil moisture (R2=0.895, RMSE=0.89%); Compared with the NDWI, the NDVI has a better effect on removing the influence of vegetation, and the NDVI can be used to characterize the VWC.【Conclusion】NDVI is more suitable for removing the influence of vegetation in water cloud model, and the dual polarization method has higher accuracy in retrieving surface soil moisture.

Key words: moisture; Sentinel-1; dual polarization; remote sensing; NDVI; NDWI