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首页> 外文期刊>International Journal of Environment and Geoinformatics >Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State
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Soil Moisture Estimation using Sentinel-1 SAR Data and Land Surface Temperature in Panchmahal District, Gujarat State

机译:古吉拉特邦Panchmahal区的Sentinel-1 SAR数据和陆地表面温度的土壤水分估算

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This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index (SMI) for all field test sites was computed using the LST values. The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture (MS %) as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination (R2) of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas.
机译:本文介绍了使用在干涉宽条形(IW)模式中获取的哨兵-1 C波段合成孔径雷达(SAR)数据的土壤湿度(SM)检索潜力以及从Landsat-8分析估计的土地表面温度(LST)数字热数据。在本研究中,从2020年2月24日从网站https://earthexplorer.usgs.gov/,从哥白尼网站和Landsat-8 Oli数据下载,从哥白尼网站和Landsat-8 Oli数据下载了。三个塔塔洛亚不同村庄的田野使用重量法估算土壤水分含量。使用Sentinel应用程序平台(SNAP)软件的开源工具进行分析Sentinel-1 SAR微波数据,以估计反向散射系数。使用Landsat-8热数据估计陆地表面温度。 Landsat8,热红外传感器带-10数据和操作陆地成像频段4和带5数据估计LST。使用LST值计算所有现场测试部位的土壤湿度指数(SMI)。使用Σ0VV和土壤水分偏振的回归分析表明,与Σ0VH极化相比,Σ0VV偏振对土壤水分含量更敏感。使用现场测量的土壤水分(MS%)作为依赖变量的多元回归分析,以及σ0VV和SMI作为独立变量,导致Godhra,Goghamba和Kalol Talukas的测定系数(R2),0.777和0.778,分别。这些线性回归方程用于计算三个塔卢克斯的预测土壤水分。

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