A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Autor: Francisco Munoz-Arriola, Raaj Ramsankaran, Ashish Kumar, Luca Brocca

Publikováno v: Remote Sensing; Volume 11; Issue 19; Pages: 2221
Remote sensing (Basel) 11 (2019). doi:10.3390/rs11192221
info:cnr-pdr/source/autori:Kumar, Ashish; Ramsankaran, Raaj; Brocca, Luca; Munoz-Arriola, Francisco/titolo:A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture/doi:10.3390%2Frs11192221/rivista:Remote sensing (Basel)/anno:2019/pagina_da:/pagina_a:/intervallo_pagine:/volume:11
Remote Sensing, Vol 11, Iss 19, p 2221 (2019)

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moistur

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::acd5403cc03b94ec9c53558a3c1ee703

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