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Remote Sens. 2017, 9(3), 255; doi:10.3390/rs9030255

Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions

1
Department of Geoinformatic Engineering, Inha Univeristy, Incheon 22212, Korea
2
Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus
3
Department of Environment, Energy, and Geoinformatics, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Yuei-An Liou, Chyi-Tyi Lee, Yuriy Kuleshov, Jean-Pierre Barriot, Chung-Ru Ho, Magaly Koch and Prasad S. Thenkabail
Received: 19 December 2016 / Accepted: 7 March 2017 / Published: 9 March 2017
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
View Full-Text   |   Download PDF [2972 KB, uploaded 10 March 2017]   |  

Abstract

This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges. View Full-Text
Keywords: downscaling; multivariate kriging; Tropical Rainfall Measuring Mission (TRMM) downscaling; multivariate kriging; Tropical Rainfall Measuring Mission (TRMM)
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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).

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MDPI and ACS Style

Park, N.-W.; Kyriakidis, P.C.; Hong, S. Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions. Remote Sens. 2017, 9, 255.

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