The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin

doi:10.6046/zrzyyg.2021414

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Abstract

Given the land surface types and atmospheric features of the Heihe River basin, this study calculated the surface emissivity of the study area using the ASTER Global Emissivity Database and the vegetation cover method (VCM) and estimated the atmospheric water vapor content using the improved multilayer feed-forward neural network (MFNN). Moreover, by establishing the coefficient lookup table of input parameter groups, this study developed an ASTER data-based split-window algorithm for the remote sensing inversion of land surface temperature. To validate the applicability and accuracy of the split-window algorithm, this study elevated the algorithm using the measured site data on the land surface temperature of the Heihe River basin in 2019 and MODIS instruments. Compared with the site data, the results of the split-window algorithm had root mean square errors of 1.81~3.01 K. In the cross-validation using the MODIS instruments, the split-window algorithm had relatively small errors and deviations, with root mean square errors of 1.11~1.75 K. Overall, the accuracy of the land surface temperature obtained from the inversion using the split-window algorithm can meet the needs of meteorological and climatological studies. Moreover, the development philosophy of the split-window algorithm can be used as a reference for similar thermal infrared sensors.

Keywords ASTER data land surface temperature cross-validation

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TP79

Issue Date: 20 March 2023

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	Junjun MA
	Chunlei WANG
	Xiaohong HUANG

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Junjun MA,Chunlei WANG,Xiaohong HUANG. The remote sensing inversion and validation of land surface temperature based on ASTER data: A case study of the Heihe River basin[J]. Remote Sensing for Natural Resources, 2023, 35(1): 198-204.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021414 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/198

Tab.1 Weather station information table

Fig.1 Flow chart for obtaining the regression coefficients

Tab.2 Root mean square error between estimated value and actual WVC(g/cm²)

状态	$ε ∈ [0.94,1.0], T ∈ [290,310] K$
水汽分组/(g·cm^-2)	$W V C ∈ [0.0,1.5]$	$W V C ∈ [1.0,2.5]$
LST值的范围/K	[290.0,307]	[290.4,307.83]
均值/K	298.5	299.12
两组LST偏差/K	0.36
均值差值/K	0.62

Tab.3 Water vapor value sensitivity analysis table

Tab.4 Comparisons of the estimation value with site data and MODIS product

Fig.2 Scatter diagram of the estimation value and site data

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