Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Estimation of Land Surface Temperature from CHINESE ZY1-02E IRS Data

Xianhui Dou
,
Kun Li
ORCID logo ,
Qi Zhang
ORCID logo ,
Chenyang Ma
,
Hongzhao Tang
,
Yonggang Qian
* ,
Jun Chen
,
Jinglun Li
,
Yichao Li
,
Tao Wang
,
Feng Wang
,
Juntao Yang
,
Xining Liu
Version 1 : Received: 5 December 2023 / Approved: 6 December 2023 / Online: 6 December 2023 (03:21:10 CET)

A peer-reviewed article of this Preprint also exists.

Dou, X.; Li, K.; Zhang, Q.; Ma, C.; Tang, H.; Liu, X.; Qian, Y.; Chen, J.; Li, J.; Li, Y.; Wang, T.; Wang, F.; Yang, J. Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data. Remote Sens. 2024, 16, 383. Dou, X.; Li, K.; Zhang, Q.; Ma, C.; Tang, H.; Liu, X.; Qian, Y.; Chen, J.; Li, J.; Li, Y.; Wang, T.; Wang, F.; Yang, J. Estimation of Land Surface Temperature from Chinese ZY1-02E IRS Data. Remote Sens. 2024, 16, 383.

Abstract

The role of land surface temperature (LST) is of utmost importance in multiple academic disciplines such as climatology, hydrology, ecology, and meteorology. Until to now, many methods have been proposed to estimate LST from satellite thermal infrared data. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12μm, affected strongly by both atmospheric water vapor and ozone. Three study areas, namely Baotou, Heihe River Basin, and Yantai-sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted by cross-comparison between the retrieved LST and MODIS LST product. The results revealed that the WVS-based method exhibited an average bias of 0.63K and an RMSE of 1.62K compared to the in-situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77K and RMSE of 2.0K. These findings provide that the WVS-based method is effective to estimate LST from ZY1-02E IRS data.

Keywords

land surface temperature; WVS-based LST method; ZY1-02E IRS

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Comments (2)

Comment 1
Received: 9 December 2023
Commenter:
Yonggang Qian

Commenter's Conflict of Interests: I am one of the author
Comment: Surface temperature retrieval from remote sensing has always been one of the key challenges in thermal infrared remote sensing. The author has provided an operational temperature retrieval algorithms for a new thermal infrared satellite, which not only comprehensively considers various atmospheric factors such as water vapor and ozone content, but also uses multiple land types in the final verification process, and performs cross-validation based on the highest-precision MODIS temperature product available. The work is very detailed and has guiding significance for the production of temperature products for this satellite. I think it fits the theme of this journal and is suitable for publication.
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Comment 2
Received: 10 December 2023
The commenter has declared there is no conflict of interests.
Comment: This manuscript proposed a single-channel approach based on water vapor scaling for deriving LST from ZY1-02E IRS data. The methodology is well described, and the results show that it is efficient. This study is well-prepared. There are some comments as follow:
1. Page 8, how much does the uncertainty of atmospheric reanalysis profile data affect the accuracy of atmospheric parameters and LST retrieval?
2. Page 13, a linear function expression was considered to correct the temporal difference. What is the accuracy since it was not given in the text? How about the DTC model, which is commonly used to model daily temperature changes?
3. The parameters such as atmospheric parameters and LSE used in the model process are the channel-effective values. The wide spectral range of ZY1-02E IRS sensor may cause differences during integration. What is your consideration?
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