卫星遥感降水产品是当前获取大范围、连续性降水观测的主要来源，但目前已有的卫星遥感降水产品空间分辨率粗糙，且存在一定的系统偏差。为此，本文充分考虑高分辨率环境变量（包括地形、NDVI、地表温度、经纬度）对降水影响以及邻近遥感降水（站点）空间相关性，构建了一种双阶段空间随机森林（Spatial Random Forest, SRF）方法（SRF-SRF）。以四川省2015年—2019年GPM（Global Precipitation Measurement Mission）月降水数据为例，借助SRF-SRF对其质量提升，并将计算结果与现有七种方法比较，包括地理加权回归（GWR）、反向传播神经网络（BPNN）、随机森林（RF）、站点降水Kriging插值（Kriging）、经SRF降尺度后的地理差异分析校正（SRF-GDA）、经双线性插值降尺度后的SRF校正（Bi-SRF）以及年降水经SRF降尺度后按月比例分解并利用SRF校正（SRFdis）等。实验分析表明：（1）在月尺度上，与原始GPM相比，SRF-SRF的平均绝对误差（MAE）降低了19.51%，中误差（RMSE）降低了16.35%，而且精度优于其他方法；在季尺度上， SRF-SRF在冬季误差最小，在夏季误差最大，但其计算精度均优于其他方法；在年尺度上，基于SRF的四种方法（包括SRF-SRF、SRF-GDA、Bi-SRF和SRFdis）优于GWR、BPNN、RF，并且SRF-SRF计算精度优于单阶段的Bi-SRF和SRF-GDA。（2）SRF-SRF降水产品空间分布连续性较好，且局部降水细节得到明显提升。（3）借助RF对各自变量重要性分析得出，降水空间相关性对卫星遥感降水质量提升具有重要作用。（4）基于月尺度的SRF-SRF融合校正效果优于基于年尺度的SRFdis，表明NDVI可用于该区域月尺度降水质量提升。

Objective Satellite remote sensing precipitation products are currently the main source for obtaining large-scale and continuous precipitation observations, but the currently available satellite remote sensing precipitation products have coarse spatial resolution and suffer from certain systematic biases. Thus, this paper aims to downscale the precipitation data as well as remove its inherent systematic biases. Method This paper proposes a two-stage Spatial Random Forest (SRF) method (SRF-SRF) by fully considering the influence of high-resolution environmental variables (including topography, NDVI, surface temperature, latitude and longitude) on precipitation and the spatial correlation of neighbouring remotely sensed precipitation (stations). Taking the GPM (Global Precipitation Measurement Mission) monthly precipitation data of Sichuan Province from 2015-2019 as an example, its quality is enhanced with the help of SRF-SRF, and the calculation results are compared with seven existing methods, including geo-weighted regression (GWR), back propagation Neural Network (BPNN), Random Forest (RF), Kriging interpolation of station precipitation (Kriging), Geographic Difference Analysis correction after downscaling by SRF (SRF-GDA), SRF correction after downscaling by bilinear interpolation (Bi-SRF), and annual precipitation downscaled by SRF and scaled by month and corrected using SRF (SRFDis). Result The experimental analysis shows that: (1) at the monthly scale, compared with the original GPM, the mean absolute error (MAE) of SRF-SRF is reduced by 19.51% and the medium error (RMSE) is reduced by 16.35%, and the accuracy is better than other methods; at the seasonal scale, SRF-SRF has the smallest error in winter and the largest error in summer, but its calculation accuracy is better than other methods; at the annual scale, the four SRF-based methods (including SRF-SRF, SRF-GDA, Bi-SRF and SRFdis) outperform GWR, BPNN and RF, and the accuracy of SRF-SRF is higher than that of Bi-SRF and SRF-GDA.(2) The spatial distribution continuity of SRF-SRF precipitation products is better, and the local precipitation details are significantly improved. (3) The spatial correlation of precipitation plays an important role in the improvement of GPM precipitation quality. (4) SRF-SRF based on the monthly scale is better than SRFdis based on the annual scale, indicating that NDVI can be used for precipitation quality enhancement on the monthly scale in Sichuan province. Conclusion This paper proposes a two-stage satellite precipitation product quality enhancement method that takes into account spatial correlation. The method takes into account the spatial autocorrelation between precipitation and combines downscaling and calibration while integrating environmental factors, thus improving the spatial resolution and accuracy of precipitation products. The experimental results show that the new method outperforms the other seven classical methods and is more applicable to the quality improvement of precipitation products in complex terrain.