Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil

IF 0.9 Q4 WATER RESOURCES

RBRH-Revista Brasileira de Recursos Hidricos Pub Date : 2022-01-01 DOI:10.1590/2318-0331.272220220016

Marcella Vasconcelos Quintella Jucá, Alfredo Ribeiro Neto

{"title":"Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil","authors":"Marcella Vasconcelos Quintella Jucá, Alfredo Ribeiro Neto","doi":"10.1590/2318-0331.272220220016","DOIUrl":null,"url":null,"abstract":"ABSTRACT The present study aimed to apply and assess an exponential filter that calculates the root-zone soil moisture using surface data from the soil moisture and ocean salinity (SMOS) satellite, as well as to assess soil moisture simulated in land-surface models from global databases. The soil water index (obtained after application of the exponential filter) and soil moisture simulated using land surface models (GLDAS-CLSM, GLDAS-Noah, and ERA5-Land) from global databases were compared with in situ data to evaluate their efficiency in estimating soil water content at different depths. Surface measurements from the SMOS satellite allowed the estimation of soil moisture at depths of 20 and 40 cm by applying the exponential filter. At both depths, the application of the exponential filter significantly improved the estimation of soil moisture measured by the SMOS satellite. The GLDAS-Noah model had the best root mean square error values, whilst the GLDAS-CLSM and ERA5-Land models overestimated the soil moisture. Nevertheless, the seasonal variation was well represented by all land surface models.","PeriodicalId":54151,"journal":{"name":"RBRH-Revista Brasileira de Recursos Hidricos","volume":"1 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"RBRH-Revista Brasileira de Recursos Hidricos","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1590/2318-0331.272220220016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}

引用次数: 0

Abstract

ABSTRACT The present study aimed to apply and assess an exponential filter that calculates the root-zone soil moisture using surface data from the soil moisture and ocean salinity (SMOS) satellite, as well as to assess soil moisture simulated in land-surface models from global databases. The soil water index (obtained after application of the exponential filter) and soil moisture simulated using land surface models (GLDAS-CLSM, GLDAS-Noah, and ERA5-Land) from global databases were compared with in situ data to evaluate their efficiency in estimating soil water content at different depths. Surface measurements from the SMOS satellite allowed the estimation of soil moisture at depths of 20 and 40 cm by applying the exponential filter. At both depths, the application of the exponential filter significantly improved the estimation of soil moisture measured by the SMOS satellite. The GLDAS-Noah model had the best root mean square error values, whilst the GLDAS-CLSM and ERA5-Land models overestimated the soil moisture. Nevertheless, the seasonal variation was well represented by all land surface models.

查看原文本刊更多论文

巴西东北部伯南布哥州不同深度土壤水分估算的遥感和全球数据库

本研究旨在应用和评估一种指数滤波器，该滤波器利用土壤湿度和海洋盐度(SMOS)卫星的地表数据计算根区土壤湿度，并评估来自全球数据库的陆地表面模型模拟的土壤湿度。利用GLDAS-CLSM、GLDAS-Noah和ERA5-Land陆面模型模拟的全球数据库土壤水分指数(应用指数滤波后获得)和土壤水分与现场数据进行比较，以评估它们在估算不同深度土壤含水量方面的效率。SMOS卫星的地表测量可以通过应用指数滤波来估计深度为20和40厘米的土壤湿度。在这两个深度，指数滤波的应用显著改善了SMOS卫星测量土壤湿度的估计。GLDAS-Noah模型具有最佳的均方根误差值，而GLDAS-CLSM和ERA5-Land模型高估了土壤湿度。然而，所有陆地表面模式都很好地反映了季节变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊