Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over high latitude areas due to the limited spatial extent of several satellite-based precipitation products. In this study, we propose an approach for the reconstruction of the Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data over Northeast China based on the interaction between precipitation and surface environment. Two machine learning algorithms, support vector machine (SVM) and random forests (RF), are implemented to detect possible relationships between precipitation and normalized difference vegetation index (NDVI), land surface temperature (LST), and digital elevation model (DEM). The relationships between precipitation and geographical location variations based on longitude and latitude are also considered in the reconstruction model. The reconstruction of monthly precipitation in the study area is conducted in two spatial resolutions (25 km and 1 km). The validation is performed using in-situ observations from eight meteorological stations within the study area. The results show that the RF algorithm is robust and not sensitive to the choice of parameters, while the training accuracy of the SVM algorithm has relatively large fluctuations depending on the parameter settings and month. The precipitation data reconstructed with RF show strong correlation with in situ observations at each station and are more accurate than that obtained using the SVM algorithm. In general, the accuracy of the estimated precipitation at 1 km resolution is slightly lower than that of data at 25 km resolution. The estimation errors are positively related to the average precipitation.
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