Retrieval of Land Surface Temperature From Passive Microwave Observations Using CatBoost-Based Adaptive Feature Selection

Passive microwave (PMW) remote sensing is increasingly employed for generating seamless all-weather land surface temperature (LST) data due to its ability to penetrate cloud cover and capture the actual surface conditions underneath. Existing PMW retrieval methods often utilize large amounts of remote sensing data, overlooking the fact that redundant data can increase computational and time costs, reduce model interpretability, and may negatively impact accuracy. In this article, we proposed a PMW-LST retrieval method that integrates CatBoost-Based adaptive feature selection. First, we categorized the data into six groups based on the underlying surface types and data view time. Next, for each group, we ranked the feature sets according to their importance and employed the recursive feature elimination (RFE) method for feature selection. Finally, the optimized feature sets were used in the CatBoost algorithm to construct the PMW-LST retrieval model. We compared the accuracy of the proposed method with the Holmes, multichannel, and Random Forest algorithms. Results showed that the proposed method had lowest RMSE, with the value of 3.28 K (1.95 K), 2.69 K (1.65 K), and 3.71 K (2.22 K) on grassland, cropland, and barren land at daytime (nighttime), respectively. The verification at sites in Heihe river basin shows that the ubRMSE ranges from 1.73 to 4.48 K at daytime and 2.71 to 3.19 K at nighttime under clear-sky conditions, and from 1.83 to 5.23 K at daytime and 2.77 to 3.93 K at nighttime under cloudy-sky conditions. These results indicate the proposed method achieves higher accuracy in generating seamless all-weather LST data.