Hrushikesh Rajeev, Punithraj Gururaj, Abhishek A Pathak
{"title":"利用合成孔径雷达和数据驱动算法动态监测地表土壤水分波动","authors":"Hrushikesh Rajeev, Punithraj Gururaj, Abhishek A Pathak","doi":"10.1007/s12518-024-00606-2","DOIUrl":null,"url":null,"abstract":"<div><p>The primary goal of the study is to employ Synthetic Aperture Radar (SAR) data and efficacy data driven approaches in modeling Surface Soil Moisture (SSM) of cultivable marginal bare fields. Three experimental test fields were selected which are basically cultivable but due water deficiency the fields are left bare. Samples for surface soil moisture, soil surface roughness and bulk density are collected from test fields in grid sampling manner in parallel with SAR data pass over study area. Sentinel-1 A data is pre-processed and each field sampling grid backscattering energy values are obtained. Surface roughness, dielectric constant and backscattered energy were used as input features to model SSM using Random Forest Regression (RFR), Support Vector Regression (SVR) and Back Propagation Artificial Neural Network (BPANN).We observed that BPANN outperformed SVR and RF by accurately predicting soil moisture with RMSE = 0.077 m<sup>3</sup>m<sup>−3</sup>, bias = 0.013m<sup>3</sup>m<sup>−3</sup>, and <i>R</i> = 0.94.This study sheds light on small scale agricultural lands which are deficient of water to support crop growth.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"17 1","pages":"1 - 15"},"PeriodicalIF":2.3000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic monitoring of surface soil moisture fluctuations using synthetic aperture radar and data-driven algorithms\",\"authors\":\"Hrushikesh Rajeev, Punithraj Gururaj, Abhishek A Pathak\",\"doi\":\"10.1007/s12518-024-00606-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The primary goal of the study is to employ Synthetic Aperture Radar (SAR) data and efficacy data driven approaches in modeling Surface Soil Moisture (SSM) of cultivable marginal bare fields. Three experimental test fields were selected which are basically cultivable but due water deficiency the fields are left bare. Samples for surface soil moisture, soil surface roughness and bulk density are collected from test fields in grid sampling manner in parallel with SAR data pass over study area. Sentinel-1 A data is pre-processed and each field sampling grid backscattering energy values are obtained. Surface roughness, dielectric constant and backscattered energy were used as input features to model SSM using Random Forest Regression (RFR), Support Vector Regression (SVR) and Back Propagation Artificial Neural Network (BPANN).We observed that BPANN outperformed SVR and RF by accurately predicting soil moisture with RMSE = 0.077 m<sup>3</sup>m<sup>−3</sup>, bias = 0.013m<sup>3</sup>m<sup>−3</sup>, and <i>R</i> = 0.94.This study sheds light on small scale agricultural lands which are deficient of water to support crop growth.</p></div>\",\"PeriodicalId\":46286,\"journal\":{\"name\":\"Applied Geomatics\",\"volume\":\"17 1\",\"pages\":\"1 - 15\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geomatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12518-024-00606-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geomatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s12518-024-00606-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Dynamic monitoring of surface soil moisture fluctuations using synthetic aperture radar and data-driven algorithms
The primary goal of the study is to employ Synthetic Aperture Radar (SAR) data and efficacy data driven approaches in modeling Surface Soil Moisture (SSM) of cultivable marginal bare fields. Three experimental test fields were selected which are basically cultivable but due water deficiency the fields are left bare. Samples for surface soil moisture, soil surface roughness and bulk density are collected from test fields in grid sampling manner in parallel with SAR data pass over study area. Sentinel-1 A data is pre-processed and each field sampling grid backscattering energy values are obtained. Surface roughness, dielectric constant and backscattered energy were used as input features to model SSM using Random Forest Regression (RFR), Support Vector Regression (SVR) and Back Propagation Artificial Neural Network (BPANN).We observed that BPANN outperformed SVR and RF by accurately predicting soil moisture with RMSE = 0.077 m3m−3, bias = 0.013m3m−3, and R = 0.94.This study sheds light on small scale agricultural lands which are deficient of water to support crop growth.
期刊介绍：
Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences.
The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology.
Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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