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Article

An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data

1
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
3
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
4
Environmental Futures Research Institute, School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2613; https://doi.org/10.3390/rs12162613
Received: 23 June 2020 / Revised: 5 August 2020 / Accepted: 12 August 2020 / Published: 13 August 2020
An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites. View Full-Text
Keywords: Himawari-8 AHI; operational split-window algorithm; land surface temperature; emissivity; validation Himawari-8 AHI; operational split-window algorithm; land surface temperature; emissivity; validation
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MDPI and ACS Style

Li, R.; Li, H.; Sun, L.; Yang, Y.; Hu, T.; Bian, Z.; Cao, B.; Du, Y.; Liu, Q. An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data. Remote Sens. 2020, 12, 2613. https://doi.org/10.3390/rs12162613

AMA Style

Li R, Li H, Sun L, Yang Y, Hu T, Bian Z, Cao B, Du Y, Liu Q. An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data. Remote Sensing. 2020; 12(16):2613. https://doi.org/10.3390/rs12162613

Chicago/Turabian Style

Li, Ruibo, Hua Li, Lin Sun, Yikun Yang, Tian Hu, Zunjian Bian, Biao Cao, Yongming Du, and Qinhuo Liu. 2020. "An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data" Remote Sensing 12, no. 16: 2613. https://doi.org/10.3390/rs12162613

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