Paper Info
Title
A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China.
Abstract
Environmental monitoring of Earth from space has provided invaluable information for understanding land-atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation-land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day-night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation-land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data.
Year
DOI
Venue
2016
10.3390/rs8100835
REMOTE SENSING
Keywords
Field
DocType
TRMM,precipitation,downscaling,land surface temperature,machine learning
Longitude,Downscaling,Remote sensing,Digital elevation model,Normalized Difference Vegetation Index,Latitude,Random forest,Climatology,Precipitation,Meteorology,Rain gauge,Algorithm,Geology
Journal
Volume
Issue
Citations 
8
10
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wenlong Jing111.11
Yaping Yang2175.99
Xiafang Yue321.81
Xiaodan Zhao4548.84