{"title":"Soil Moisture Estimation at 500m using Sentinel-1: application to African sites","authors":"Myriam Foucras, M. Zribi, A. Kallel","doi":"10.1109/ATSIP49331.2020.9231733","DOIUrl":null,"url":null,"abstract":"This paper proposes a change detection approach for the estimation of soil water content, at a spatial resolution of 0.5 km and a temporal resolution of 6 days. The algorithm proposes a soil moisture index between 0 and 1. 0 corresponds to the driest context, 1 corresponds to the wettest context. The approach is being tested on different study sites with sentinel-1 radar data. Unlike the classic change detection approach, the algorithm takes into account the effects of land use, vegetation development and the seasonal context. Results show a good correlation between satellite estimations and true measurements for the African studied regions.","PeriodicalId":384018,"journal":{"name":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATSIP49331.2020.9231733","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper proposes a change detection approach for the estimation of soil water content, at a spatial resolution of 0.5 km and a temporal resolution of 6 days. The algorithm proposes a soil moisture index between 0 and 1. 0 corresponds to the driest context, 1 corresponds to the wettest context. The approach is being tested on different study sites with sentinel-1 radar data. Unlike the classic change detection approach, the algorithm takes into account the effects of land use, vegetation development and the seasonal context. Results show a good correlation between satellite estimations and true measurements for the African studied regions.