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Open AccessArticle

Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th Floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
3
Department of Geography, Ruhr-University Bochum, 44807 Bochum, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1398; https://doi.org/10.3390/rs12091398
Received: 9 March 2020 / Revised: 23 April 2020 / Accepted: 26 April 2020 / Published: 28 April 2020
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST))
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 °C compared to an average RMSE of 3.8 °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 R2 of 0.82 and 0.74 and with RMSE of 2.5 °C and 1.4 °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. View Full-Text
Keywords: MODIS; AMSR2; annual cycle parameters; random forest; cloudy sky LST MODIS; AMSR2; annual cycle parameters; random forest; cloudy sky LST
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MDPI and ACS Style

Yoo, C.; Im, J.; Cho, D.; Yokoya, N.; Xia, J.; Bechtel, B. Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days. Remote Sens. 2020, 12, 1398.

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