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Monitoring Soil Moisture Drought over Northern High Latitudes from Space
Article

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

1
Hydro-Remote Sensing Applications (H-RSA) Group, Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India
2
Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai 400 076, India
3
Research Institute for Geo-Hydrological Protection, National Research Council, 06123 Perugia, Italy
4
Department Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE 68583-0726, USA
5
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583-0726, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2221; https://doi.org/10.3390/rs11192221
Received: 13 August 2019 / Revised: 12 September 2019 / Accepted: 17 September 2019 / Published: 24 September 2019
(This article belongs to the Special Issue Remote Sensing of Hydrometeorological Extremes)
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. View Full-Text
Keywords: ASCAT; near real time satellite rainfall estimates; satellite-based soil moisture estimates; support vector machine-based regression (SVR); TMPA 3B42RT ASCAT; near real time satellite rainfall estimates; satellite-based soil moisture estimates; support vector machine-based regression (SVR); TMPA 3B42RT
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MDPI and ACS Style

Kumar, A.; Ramsankaran, R.; Brocca, L.; Munoz-Arriola, F. A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture. Remote Sens. 2019, 11, 2221. https://doi.org/10.3390/rs11192221

AMA Style

Kumar A, Ramsankaran R, Brocca L, Munoz-Arriola F. A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture. Remote Sensing. 2019; 11(19):2221. https://doi.org/10.3390/rs11192221

Chicago/Turabian Style

Kumar, Ashish, RAAJ Ramsankaran, Luca Brocca, and Francisco Munoz-Arriola. 2019. "A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture" Remote Sensing 11, no. 19: 2221. https://doi.org/10.3390/rs11192221

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