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Remote Sens. 2014, 6(12), 12118-12137;

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

1,†, 1,*, 2,† and 3,†
The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Department of Geography and Program in Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
National Meteorological Information Center (NMIC), China Meteorological Administration (CMA), Beijing100081, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 19 August 2014 / Revised: 18 November 2014 / Accepted: 19 November 2014 / Published: 4 December 2014
(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)
PDF [13574 KB, uploaded 4 December 2014]


False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB37V and TB19V, respectively) and the sum of TB37V and TB19V derived from the observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI), a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods. Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets. View Full-Text
Keywords: probabilistic neural network; rain area delineation; Scatter Index; dynamic cluster K-means; support vector machine probabilistic neural network; rain area delineation; Scatter Index; dynamic cluster K-means; support vector machine

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Xu, S.; Wu, C.; Gonsamo, A.; Shen, Y. Delineation of Rain Areas with TRMM Microwave Observations Based on PNN. Remote Sens. 2014, 6, 12118-12137.

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