Land Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region

Ji Zhou,Wenfeng Zhan,Xu Zhang,Huailan Zhang

doi:10.3390/rs6065344

Abstract

The land surface temperature (LST) is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW) algorithms, which can be applied to satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm, require auxiliary atmospheric parameters (e.g., water vapor content). In this research, the Heihe River basin, which is one of the most arid regions in China, is selected as the study area. The Moderate-resolution Imaging Spectroradiometer (MODIS) is selected as a test case. The Global Data Assimilation System (GDAS) atmospheric profiles of the study area are used to generate the training dataset through radiative transfer simulation. Significant correlations between the atmospheric upwelling radiance in MODIS channel 31 and the other three atmospheric parameters, including the transmittance in channel 31 and the transmittance and upwelling radiance in channel 32, are trained based on the simulation dataset and formulated with three regression models. Next, the genetic algorithm is used to estimate the LST. Validations of the RM-GA method are based on the simulation dataset generated from in situ measured radiosonde profiles and GDAS atmospheric profiles, the in situ measured LSTs, and a pair of daytime and nighttime MOD11A1 products in the study area. The results demonstrate that RM-GA has a good ability to estimate the LSTs directly from the MODIS data without any auxiliary atmospheric parameters. Although this research is for local application in the Heihe River basin, the findings and proposed method can easily be extended to other satellite sensors and regions with arid climates and high elevations.

Highlights

As one of the most important parameters of surface-atmosphere interactions, land surface temperature (LST) plays a crucial role in modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface [1,2]
With the simulation dataset generated based on Global Data Assimilation System (GDAS) atmospheric profiles in the study area, we find that the regression models for the previously mentioned sensors have the following general form:
Many split-window (SW) algorithms require auxiliary atmospheric parameters to calculate land surface temperatures (LSTs) from satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm

Summary

Introduction

As one of the most important parameters of surface-atmosphere interactions, land surface temperature (LST) plays a crucial role in modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface [1,2]. The thermal signal acquired by a remote sensor at the top of the atmosphere (TOA) is influenced by surface parameters, e.g., temperature and land surface emissivity (LSE). The derivation of LST would be straightforward if the atmospheric profile and LSEs were collected while acquiring the remote sensing image. In this case, the atmospheric profile, which can be derived from a radiosonde, provides detailed vertical distributions of the temperature, humidity, pressure, and atmospheric contents in aerosols and molecular gases. The LST can be calculated from the surface radiances by taking the inverse of Planck’s function with the LSE and the effective wavelength of the corresponding thermal channel

Methods

Findings

Discussion

Conclusion