Next Article in Journal
Hyperspectral Reflectance Anisotropy Measurements Using a Pushbroom Spectrometer on an Unmanned Aerial Vehicle—Results for Barley, Winter Wheat, and Potato
Next Article in Special Issue
An Algorithm for In-Flight Spectral Calibration of Imaging Spectrometers
Previous Article in Journal
Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping
Previous Article in Special Issue
Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(11), 899;

Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
College of Computer and Information Engineering, Hohai University, Nanjing 210098, China
Department of Space Microwave Remote Sensing System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Wagner and Prasad S. Thenkabail
Received: 19 July 2016 / Revised: 2 October 2016 / Accepted: 24 October 2016 / Published: 2 November 2016
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
Full-Text   |   PDF [15567 KB, uploaded 2 November 2016]   |  


In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products—Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe basin of China. Generally, the direct comparison of TMPA precipitation estimates to 200 collocated rain gauges from 2006 to 2008 demonstrate that the spatial and temporal rainfall characteristics over the region are well captured by the assimilation estimates. Especially, results also show that using Kalman filter to assimilate TRMM-based multi-satellite real-time precipitation estimates tends to perform well over regions, where gauge network is rather sparse. Last, this study highlights that accurate detection and estimation of precipitation in the summer season by Kalman filter, particularly for nonlinear convective precipitation events, is still a challenging task for the future development of assimilation technique for improving the satellite-based precipitation accuracy. View Full-Text
Keywords: precipitation; satellite; data assimilation; Karlman Filter precipitation; satellite; data assimilation; Karlman Filter

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

Chen, J.; Yong, B.; Ren, L.; Wang, W.; Chen, B.; Lin, J.; Yu, Z.; Li, N. Using a Kalman Filter to Assimilate TRMM-Based Real-Time Satellite Precipitation Estimates over Jinghe Basin, China. Remote Sens. 2016, 8, 899.

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