Abstract
Based on the observation data of 103 conventional meteorological stations, MODIS data and geographic information data, the BP neural network, Random Forest and support vector machine methods based on genetic algorithm were applied to build the retrieval model of daily maximum temperature in Growing region of Sichuan Province. The results indicated that: (1) Compared to other models, the model combining multispectral information with spatiotemporal information for daily maximum temperature inversion had the highest inversion accuracy. The inversion model using only spectral information for daily maximum temperature had the lowest inversion accuracy. The addition of spatial and temporal information could effectively mitigate the impact of complex atmospheric environments on model inversion. (2) For the inversion model of BP neural network based on genetic algorithm and Random Forest algorithm, the accuracy of the model was significantly improved by the day sequence information, and for the inversion model based on support vector machine, the accuracy of the model was significantly improved by the spatial information. (3) The inversion model based on Random Forest had the highest precision, RMSE = 2.19 ℃. The inversion model based on support vector machine had the second highest accuracy, with RMSE = 2.31 ℃. The inversion model based on genetic algorithm and BP neural network has the lowest accuracy, with RMSE = 2.44 ℃.