Next Article in Journal
Long-Term Satellite Monitoring of the Slumgullion Landslide Using Space-Borne Synthetic Aperture Radar Sub-Pixel Offset Tracking
Previous Article in Journal
Mapping the Mangrove Forest Canopy Using Spectral Unmixing of Very High Spatial Resolution Satellite Images
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(3), 368;

Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Author to whom correspondence should be addressed.
Received: 18 December 2018 / Revised: 2 February 2019 / Accepted: 8 February 2019 / Published: 12 February 2019
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Full-Text   |   PDF [4484 KB, uploaded 12 February 2019]   |  


Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time. View Full-Text
Keywords: SMAP; GPM; precipitation; soil moisture; correction; assimilation SMAP; GPM; precipitation; soil moisture; correction; assimilation

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhang, Z.; Wang, D.; Wang, G.; Qiu, J.; Liao, W. Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time. Remote Sens. 2019, 11, 368.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top