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Remote Sensing
Volume 14
Issue 11
10.3390/rs14112716
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Article

Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions

by
Yun Jiang
1,2,
Bo-Hui Tang
1,3,* and
Yanhong Zhao
4
1
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
4
Faculty of Earth Science and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(11), 2716; https://doi.org/10.3390/rs14112716
Submission received: 23 April 2022 / Revised: 31 May 2022 / Accepted: 2 June 2022 / Published: 6 June 2022
(This article belongs to the Special Issue Advance on Net Surface Radiation Inversion Using Remote Sensing Technology)
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Abstract

This work proposes a new method for estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions based on a parameterization method and a genetic algorithm–artificial neural network (GA-ANN) algorithm. The new method establishes a GA-ANN model based on simulated data, and then combines MODIS satellite data and ERA5 reanalysis data to estimate the DSLR. According to the validation results of the field sites, the bias and RMSE are –9.18 and 34.88 W/m2, respectively. Compared with the existing research, the new method can achieve reasonable accuracy. Parameter analysis using independently simulated data shows that the near-surface air temperature (Ta) and cloud base height (CBH) have an important influence on DSLR estimation under cloudy-sky conditions. With an increase in CBH, DSLR gradually decreases; however, with an increase in Ta, DSLR shows a trend of gradual increase. When estimating DSLR under cloudy-sky conditions, under the influence of clouds, except for cirrus, the change in DSLRs with CBH and Ta is greater than 20 W/m2.
Keywords: downwelling surface longwave radiation; GA-ANN; MODIS; ERA5; cloudy sky downwelling surface longwave radiation; GA-ANN; MODIS; ERA5; cloudy sky

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MDPI and ACS Style

Jiang, Y.; Tang, B.-H.; Zhao, Y. Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions. Remote Sens. 2022, 14, 2716. https://doi.org/10.3390/rs14112716

AMA Style

Jiang Y, Tang B-H, Zhao Y. Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions. Remote Sensing. 2022; 14(11):2716. https://doi.org/10.3390/rs14112716

Chicago/Turabian Style

Jiang, Yun, Bo-Hui Tang, and Yanhong Zhao. 2022. "Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions" Remote Sensing 14, no. 11: 2716. https://doi.org/10.3390/rs14112716

APA Style

Jiang, Y., Tang, B.-H., & Zhao, Y. (2022). Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions. Remote Sensing, 14(11), 2716. https://doi.org/10.3390/rs14112716

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