All-weather surface temperature generation method and device based on machine learning

Abstract

The invention discloses an all-weather surface temperature generation method and device based on machine learning. The method comprises: extracting an MODIS data set subjected to remote sensing inversion through an MODIS tool MRT; combining static meteorological satellite data with DEM topographic data of the ALOS satellite, and estimating and obtaining surface incident solar radiation; performingspatial aggregation on data sets with the same spatial scale, and taking the data sets and the MODIS data set as a machine learning training data set; constructing a surface temperature relation model through a random forest model; estimating the real temperature of the earth surface with the cloud coverage pixels; and combining the real earth surface temperature of the cloud-covered pixel with the data set of the cloudless-covered pixel to generate the all-weather earth surface temperature. According to the method, the problems that current thermal infrared remote sensing is easily influenced by cloud and mist, and a large number of blank value-lacking areas exist in surface temperature products are solved, cloud condition surface temperature estimation is achieved, and an important basis is provided for all-weather surface temperature product generation.

Description

technical field [0001] The invention relates to surface temperature monitoring technology, in particular to an all-weather surface temperature generation method and device based on machine learning. Background technique [0002] Land surface temperature (LST), as an important parameter reflecting the interaction between the earth and the atmosphere in the earth's surface system, is a key parameter affecting the processes of surface ecology, hydrology, meteorology, etc. comprehensive results. Therefore, quantitative and accurate acquisition of the temporal and spatial distribution characteristics of surface temperature has important research significance and value for the study of the energy balance of the earth-atmosphere system and ecosystems. Moreover, dynamic monitoring of resources and the environment on a regional and global scale requires comprehensive, complete, and continuous all-weather information on the temporal and spatial distribution of surface temperature, su...

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Problems solved by technology

[0004] However, due to the low accuracy of passive microwave surface temperature retrieval, the large scale difference between passive microwave data and thermal infrared data, the error of passive microwave surface temperature itself and the uncertainty of downscaling, there are many problems in the all-weather surface temperature product generated by data fusion. large uncertainty; moreover, the existing problem of using the existing inversion algorithm is that the estimated surface temperature value is not the real surface temperature under the cloud at the satellite transit time, but the theoretical value of the surface temperature under clear sky conditions at the satellite transit time , it is difficult to meet the needs of practical applications

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