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.

High‐resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States

“…PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) is one application of a machine learning technique, which employed an artificial neural network (ANN) algorithm to estimate precipitation from satellite data (Hsu et al, 1997). Several experimental studies have applied the random forest algorithm (Breiman, 2001) to develop a two-dimensional climate surface (Shi and Song, 2015;Higuchi et al, 2016;Jing et al, 2016). However, no study has applied the random forest to dozens of input data sets to create consistent climate data sets across many different climate variables.…”

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

“…The downscaling method was based on two assumptions: (1) precipitation has a spatial relationship with environmental variables, and this relationship can be addressed by established models; and (2) the models established at low spatial resolution can also be used to predict the precipitation at a fine resolution with the higher resolution environmental variables dataset [36]. The specific steps used for downscaling in this study are described as follows:…”

“…Although a large number of algorithms have been developed and applied for the downscaling of satellite-based precipitation data and improvements in accuracy [36], there exists a challenge to generate accurate precipitation in mountainous watersheds due to the sparse gauge network and high spatial-temporal variability of precipitation. In this study, our main goal was to map annual and monthly precipitation with a high spatial resolution over a mountainous, monsoon driven watershed.…”

“…Many studies used Normalized Difference Vegetation Index (NDVI) as an independent variable to downscale the coarse TRMM precipitation data [32,36,[41][42][43]. Although the response of NDVI to precipitation usually lagged by two or three months [41,44], it was also feasible to downscale monthly, weekly, or daily satellite precipitation datasets by considering lag time.…”

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

“…As two of the most distinguished ensemble learning algorithms, the RF and XGB have been reported strongly outperforming many other classic machine learning algorithms, such as support vector machine, k-nearest neighbors, decision trees, and artificial neural network (ANN), in many applications. Successful stories could be found in precipitation and soil moisture mapping and downscaling (Jing et al, 2016;Liu et al, 2018), land cover mapping (Cracknell & Reading, 2014), surface temperature rescaling (Hutengs & Vohland, 2016), and geological mapping (Cracknell & Reading, 2014). Their flexibility (e.g., no required feature normalization and simple parameters) and tree-based structure make them easier and more efficient to be used and interpreted than support vector machine, k-nearest neighbors, and ANN.…”