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

Remote sensing (Basel, Switzerland), 2019-10, Vol.11 (19), p.2221 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11192221

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Title:
A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture
Author: Kumar, Ashish ; Ramsankaran, RAAJ ; Brocca, Luca ; Munoz-Arriola, Francisco
Subjects: ASCAT ; Correlation coefficient ; Correlation coefficients ; Data assimilation ; Datasets ; Drought ; Environmental monitoring ; Estimates ; Learning algorithms ; Machine learning ; near real time satellite rainfall estimates ; Precipitation ; Rainfall ; Rainfall intensity ; Real time ; Regression models ; Remote sensing ; River basins ; Root-mean-square errors ; satellite-based soil moisture estimates ; Satellites ; Seasons ; Soil moisture ; Spatial analysis ; Statistical analysis ; support vector machine-based regression (SVR) ; Support vector machines ; TMPA 3B42RT ; Topography ; Wind
Is Part Of: Remote sensing (Basel, Switzerland), 2019-10, Vol.11 (19), p.2221
Description: 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 moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coefficient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 2072-4292
EISSN: 2072-4292
DOI: 10.3390/rs11192221
Source: TestCollectionTL3OpenAccess
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central

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

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