A Spatial Downscaling Method of Multi-factor Remote Sensing Land Surface Temperature Based on Random Forest

Abstract

The invention discloses a random forest-based multi-factor remote sensing land surface temperature space downscaling method. First, a variety of surface parameters that can characterize water bodies, vegetation, buildings, and bare soil are inverted according to the type of land cover, and the surface parameters that have a strong correlation with surface temperature are selected as scale factors through correlation analysis; for deserts, bare soil, etc. For the problem of inaccurate temperature estimation in high-temperature areas, the short-wave infrared band is introduced as a scale factor to improve the downscaling accuracy in high-temperature areas; for the imbalance of randomly selected data in random forests, different regression models are established under different land cover types Using the method of downscaling under different land cover types, high-resolution land surface temperature images are obtained. The present invention has good applicability in large areas or areas with complex surface coverage, and effectively improves the accuracy and efficiency of downscaling.

Description

technical field [0001] The invention belongs to the field of downscaling, and in particular relates to a multi-factor remote sensing surface temperature space downscaling method based on random forest. Background technique [0002] Land Surface Temperature (LST) is an important parameter to characterize surface energy, and an important factor to study and evaluate ecosystems and climate change. Accurate surface temperature products are of great significance for monitoring urban heat islands, ecological environment, agricultural drought, monitoring global climate, estimating soil moisture and other surface processes. The traditional way to obtain surface temperature is through the observation data of surface meteorological stations. The observation station data has high precision and time continuity, but the monitoring coverage area is limited, which is not suitable for large-scale temperature monitoring. At present, the main way to obtain the surface temperature is through ...

AI Technical Summary

Problems solved by technology

Therefore, the traditional method has low accuracy for downscaling of large-scale areas with complex land cover types, which cannot meet the needs of applications.

Images