Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea

Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sungwon Choi, Donghyun Jin, Noh-Hun Seong, Daeseong Jung, Suyoung Sim, Jongho Woo, Uujin Jeon, Yugyeong Byeon, Kyung-soo Han
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	air temperature deep neural network landsat-8 South Korea Science Q
Acceso en línea:	https://doaj.org/article/0a122341df3146c389e966111d15380e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0a122341df3146c389e966111d15380e
record_format	dspace
spelling	oai:doaj.org-article:0a122341df3146c389e966111d15380e2021-11-11T18:54:07ZNear-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea10.3390/rs132143342072-4292https://doaj.org/article/0a122341df3146c389e966111d15380e2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4334https://doaj.org/toc/2072-4292Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over a large area, and LST is highly correlated with Ta. To measure the Ta in a broad area, we studied a Ta retrieval method through Deep Neural Network (DNN) using in-situ data and satellite data of South Korea from 2014 to 2017. To retrieve accurate Ta, we selected proper input variables and conditions of a DNN model. As a result, Normalized Difference Vegetation Index, Normalized Difference Water Index, and 11- and 12-µm band data were applied to the DNN model as input variables. And we also selected proper condition of the DNN model with test various conditions of the model. In validation result in the DNN model, the best accuracy of the retrieved Ta showed an correlation coefficient value of 0.98 and a root mean square error (RMSE) of 2.19 K. And then we additional 3 analysis to validate accuracy which are spatial representativeness, seasonal analysis and time series analysis. We tested the spatial representativeness of the retrieved Ta. Results for window sizes less than 132 × 132 showed high accuracy, with a correlation coefficient of over 0.97 and a RMSE of 1.96 K and a bias of −0.00856 K. And in seasonal analysis, the spring season showed the lowest accuracy, 2.82 K RMSE value, other seasons showed high accuracy under 2K RMSE value. We also analyzed a time series of six the Automated Synoptic Observing System (ASOS) points (i.e., locations) using data obtained from 2018 to 2019; all of the individual correlation coefficient values were over 0.97 and the RMSE values were under 2.41 K. With these analysis, we confirm accuracy of the DNN model was higher than previous studies. And we thought the retrieved Ta can be used in other studies or climate model to conduct urban problems like urban heat islands and to analyze effects of arctic oscillation.Sungwon ChoiDonghyun JinNoh-Hun SeongDaeseong JungSuyoung SimJongho WooUujin JeonYugyeong ByeonKyung-soo HanMDPI AGarticleair temperaturedeep neural networklandsat-8South KoreaScienceQENRemote Sensing, Vol 13, Iss 4334, p 4334 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	air temperature deep neural network landsat-8 South Korea Science Q
spellingShingle	air temperature deep neural network landsat-8 South Korea Science Q Sungwon Choi Donghyun Jin Noh-Hun Seong Daeseong Jung Suyoung Sim Jongho Woo Uujin Jeon Yugyeong Byeon Kyung-soo Han Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea
description	Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over a large area, and LST is highly correlated with Ta. To measure the Ta in a broad area, we studied a Ta retrieval method through Deep Neural Network (DNN) using in-situ data and satellite data of South Korea from 2014 to 2017. To retrieve accurate Ta, we selected proper input variables and conditions of a DNN model. As a result, Normalized Difference Vegetation Index, Normalized Difference Water Index, and 11- and 12-µm band data were applied to the DNN model as input variables. And we also selected proper condition of the DNN model with test various conditions of the model. In validation result in the DNN model, the best accuracy of the retrieved Ta showed an correlation coefficient value of 0.98 and a root mean square error (RMSE) of 2.19 K. And then we additional 3 analysis to validate accuracy which are spatial representativeness, seasonal analysis and time series analysis. We tested the spatial representativeness of the retrieved Ta. Results for window sizes less than 132 × 132 showed high accuracy, with a correlation coefficient of over 0.97 and a RMSE of 1.96 K and a bias of −0.00856 K. And in seasonal analysis, the spring season showed the lowest accuracy, 2.82 K RMSE value, other seasons showed high accuracy under 2K RMSE value. We also analyzed a time series of six the Automated Synoptic Observing System (ASOS) points (i.e., locations) using data obtained from 2018 to 2019; all of the individual correlation coefficient values were over 0.97 and the RMSE values were under 2.41 K. With these analysis, we confirm accuracy of the DNN model was higher than previous studies. And we thought the retrieved Ta can be used in other studies or climate model to conduct urban problems like urban heat islands and to analyze effects of arctic oscillation.
format	article
author	Sungwon Choi Donghyun Jin Noh-Hun Seong Daeseong Jung Suyoung Sim Jongho Woo Uujin Jeon Yugyeong Byeon Kyung-soo Han
author_facet	Sungwon Choi Donghyun Jin Noh-Hun Seong Daeseong Jung Suyoung Sim Jongho Woo Uujin Jeon Yugyeong Byeon Kyung-soo Han
author_sort	Sungwon Choi
title	Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea
title_short	Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea
title_full	Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea
title_fullStr	Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea
title_full_unstemmed	Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea
title_sort	near-surface air temperature retrieval using a deep neural network from satellite observations over south korea
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0a122341df3146c389e966111d15380e
work_keys_str_mv	AT sungwonchoi nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT donghyunjin nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT nohhunseong nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT daeseongjung nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT suyoungsim nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT jonghowoo nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT uujinjeon nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT yugyeongbyeon nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea AT kyungsoohan nearsurfaceairtemperatureretrievalusingadeepneuralnetworkfromsatelliteobservationsoversouthkorea
_version_	1718431626556342272

Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea

Ejemplares similares