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Sustainability 2017, 9(10), 1912; doi:10.3390/su9101912

Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China

1,2,3
,
1,2,3,* and 4,*
1
Guangzhou Institute of Geography, Guangzhou 510070, China
2
Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China
3
Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China
4
College of Environment and Planning, Henan University, Kaifeng 475004, China
*
Authors to whom correspondence should be addressed.
Received: 13 September 2017 / Revised: 16 October 2017 / Accepted: 19 October 2017 / Published: 23 October 2017
(This article belongs to the Special Issue Methodological Advances in Research on Sustainable Ecosystems)
View Full-Text   |   Download PDF [28325 KB, uploaded 23 October 2017]   |  

Abstract

Precipitation data are important for the fields of hydrology and meteorology, and are fundamental for ecosystem monitoring and climate change research. Satellite-based precipitation products are already able to provide high temporal resolution precipitation information at a global level. However, the coarse spatial resolution has restricted their use in regional level studies. In this study, monthly fine spatial resolution land precipitation data in China was obtained by downscaling the TRMM 3B43 V7 monthly precipitation products. The downscaling model was constructed based on the ensemble learning method called random forest (RF). In addition to the RF model, the classification and regression tree (CART) model was also used to downscale the precipitation data for the purpose of comparison. The results were validated with in situ measurements. Results showed that the RF model outperformed the CART model. The downscaled precipitation data were strongly correlated with the in situ measurements. The downscaling method was applied to mapping fine spatial resolution precipitation over all of China, and is valuable for developing high spatial resolution precipitation products for studies on hydrology, meteorology, and climate science. View Full-Text
Keywords: precipitation; spatial downscaling; Tropical Rainfall Measuring Mission; China precipitation; spatial downscaling; Tropical Rainfall Measuring Mission; China
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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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Zhao, X.; Jing, W.; Zhang, P. Mapping Fine Spatial Resolution Precipitation from TRMM Precipitation Datasets Using an Ensemble Learning Method and MODIS Optical Products in China. Sustainability 2017, 9, 1912.

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