Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R² and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.

中文翻译：

---

基于三种不同模式的MODIS地面低地表温度分析城市地表热岛-以杭州市为例

遥感技术在地表温度（LST）研究中起着越来越重要的作用。但是，各种遥感数据具有时空尺度上的矛盾。为了解决LST研究中的这个问题，当前的研究基于1种不同模型（多重线性回归（MLR），热锐化（TsHARP）和随机森林（RF））从1 km到100 m对LST进行了缩减，以分析地表城市根据中等分辨率成像光谱仪（MODIS）/ LST产品和Landsat 8 Operational Land Imager（OLI），在四个季节的白天（10:30 am）和夜间（10:30 pm）的热岛（SUHI）。本研究以空间异质性较高的杭州地区（25×25 km）为研究区域。R ²引入了RMSE和RMSE来评估转换精度。最后，我们与类似检索的LST进行了比较，以验证该方法的可行性。结果表明如下。（1）RF模型最适合用于降级MODIS / LST。MLR模型和TsHARP模型不适用于高度异质区域的缩减研究。（2）从时间维度看，夏，冬季的预报精度明显高于春，秋季，晚上的预报精度普遍高于白天。（3）夜间的SUHI范围小于白天，主要集中在市中心。该研究区的SUHI在秋季最强，冬季最弱。