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Article

Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing

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State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
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College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt
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Division of Agriculture and Natural Resources (ANR), University of California Agriculture and Natural Resources, Davis, CA 95618, USA
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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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China Three Gorges Corporation, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(23), 3860; https://doi.org/10.3390/rs12233860
Received: 28 September 2020 / Revised: 9 November 2020 / Accepted: 21 November 2020 / Published: 25 November 2020
Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution. View Full-Text
Keywords: TRMM; statistical downscaling; Google Earth engine; Google colaboratory; machine learning TRMM; statistical downscaling; Google Earth engine; Google colaboratory; machine learning
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MDPI and ACS Style

Elnashar, A.; Zeng, H.; Wu, B.; Zhang, N.; Tian, F.; Zhang, M.; Zhu, W.; Yan, N.; Chen, Z.; Sun, Z.; Wu, X.; Li, Y. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sens. 2020, 12, 3860. https://doi.org/10.3390/rs12233860

AMA Style

Elnashar A, Zeng H, Wu B, Zhang N, Tian F, Zhang M, Zhu W, Yan N, Chen Z, Sun Z, Wu X, Li Y. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sensing. 2020; 12(23):3860. https://doi.org/10.3390/rs12233860

Chicago/Turabian Style

Elnashar, Abdelrazek, Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Miao Zhang, Weiwei Zhu, Nana Yan, Zeqiang Chen, Zhiyu Sun, Xinghua Wu, and Yuan Li. 2020. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing" Remote Sensing 12, no. 23: 3860. https://doi.org/10.3390/rs12233860

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