Method for estimating all-weather temperature and spatial and temporal distribution thereof based on MODIS product data

Abstract

The invention provides a method for estimating all-weather temperature and spatial and temporal distribution thereof based on MODIS product data, and the method comprises the steps: obtaining MOD07L2 and MOD06L2 product data, SRTM DEM data and site actual measurement data of a research area, carrying out the preprocessing of the data, guaranteeing the time consistency of the MODIS product data and the site actual measurement data, and enabling the SRTM DEM data and the MODIS product data to have the same spatial resolution; using an atmosphere profile extrapolation method to obtain a sunny day air temperature, and averaging the sunny day air temperature with the surface temperature to obtain air temperature instantaneous data under the sunny day condition; and establishing a regression model of the sunny air temperature and the sunny land surface temperature, substituting the land surface temperature under the cloudy condition into a regression equation to obtain the instantaneous air temperature under the cloudy condition, and further synthesizing the instantaneous air temperature into the air temperature under all weather conditions. According to the invention, continuous estimation of all-weather temperature in instantaneous time is realized; the spatial and temporal distribution information of the air temperature of the research area can be quickly obtained, and continuous and accurate estimation of the all-weather air temperature on different spatial and temporal scales is realized.

Description

technical field [0001] The invention belongs to an all-weather air temperature estimation method, in particular to a method for estimating all-weather air temperature and its time-space distribution based on MODIS product data. Background technique [0002] As one of the important meteorological elements, air temperature (air temperature) participates in many biophysical processes such as surface energy exchange and water cycle, and is a key parameter in hydrological models and climate models. Although the temperature observed by conventional meteorological stations has the advantages of high time resolution and accurate records, the spatial coverage of the station is limited, and it is difficult to reflect the distribution trend of temperature in different time and space, especially when the climate and altitude change greatly. area is particularly evident. Spatial interpolation is a common method to obtain the spatial distribution of temperature. However, due to the spars...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that it needs to remove redundant cloud or water body information in the window, and it needs air temperature observations to calibrate

The surface temperature regression analysis method requires the calibration of air temperature observations, and cannot directly estimate the near-surface instantaneous air temperature from remote sensing data; the atmospheric profile extrapolation method can only estimate the near-surface instantaneous air temperature under sunny conditions

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