期刊
REMOTE SENSING
卷 12, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/rs12091398
关键词
MODIS; AMSR2; annual cycle parameters; random forest; cloudy sky LST
类别
资金
- Korea Meteorological Administration Research and Development Program [KMIPA 2017-7010]
- National Research Foundation of Korea (NRF) [NRF-2017M1A3A3A02015981, NRF-2016M3C4A7952600, NRF-2018K2A9A2A06023758]
- Ministry of Interior and Safety (NOIS), Korea [20009742]
- Ministry of Science and ICT (MSIT), Korea [IITP-2020-2018-0-01424]
- Global PhD Fellowship Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2018H1A2A1062207]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2018-0-01424-003] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Korea Evaluation Institute of Industrial Technology (KEIT) [20009742] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Korea Meteorological Institute (KMI) [KMIPA2017-7010] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [미래선도형특성화연구] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Land surface temperature (LST) is used as a critical indicator for various environmental issues because it links land surface fluxes with the surface atmosphere. Moderate-resolution imaging spectroradiometers (MODIS) 1 km LSTs have been widely utilized but have the serious limitation of not being provided under cloudy weather conditions. In this study, we propose two schemes to estimate all-weather 1 km Aqua MODIS daytime (1:30 p.m.) and nighttime (1:30 a.m.) LSTs in South Korea for humid summer days. Scheme 1 (S1) is a two-step approach that first estimates 10 km LSTs and then conducts the spatial downscaling of LSTs from 10 km to 1 km. Scheme 2 (S2), a one-step algorithm, directly estimates the 1 km all-weather LSTs. Eight advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures, three MODIS-based annual cycle parameters, and six auxiliary variables were used for the LST estimation based on random forest machine learning. To confirm the effectiveness of each scheme, we have performed different validation experiments using clear-sky MODIS LSTs. Moreover, we have validated all-weather LSTs using bias-corrected LSTs from 10 in situ stations. In clear-sky daytime, the performance of S2 was better than S1. However, in cloudy sky daytime, S1 simulated low LSTs better than S2, with an average root mean squared error (RMSE) of 2.6 degrees C compared to an average RMSE of 3.8 degrees C over 10 stations. At nighttime, S1 and S2 demonstrated no significant difference in performance both under clear and cloudy sky conditions. When the two schemes were combined, the proposed all-weather LSTs resulted in an average R-2 of 0.82 and 0.74 and with RMSE of 2.5 degrees C and 1.4 degrees C for daytime and nighttime, respectively, compared to the in situ data. This paper demonstrates the ability of the two different schemes to produce all-weather dynamic LSTs. The strategy proposed in this study can improve the applicability of LSTs in a variety of research and practical fields, particularly for areas that are very frequently covered with clouds.
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