Estimating midday near-surface air temperature by weighted consideration of surface and atmospheric moisture conditions using COMS and SPOT satellite data

Ryu, Jin-Hee; Han, Kyung-Soo; Cho, Jaeil; Lee, Chang-Suck; Yoon, Hong-Joo; Yeom, Jong-Min; Ou, Mi-Lim

doi:10.1080/01431161.2015.1065355

Cited by 4 publications

(10 citation statements)

References 35 publications

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“…Generally, Ta values are obtained from meteorological stations 2 m above the ground, although this can be difficult in mountainous terrain. Due to the limited distribution of meteorological stations, we used an algorithm for satellite-based midday air temperature retrieval over northeast Asia [55]. This algorithm uses COMS Level 1B brightness temperature the difference between the IR 1 6 Journal of Sensors …”

Section: Estimation Of Daily Air Temperature (Ta)mentioning

confidence: 99%

COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

Park¹,

Jang²,

Kim³

et al. 2017

Journal of Sensors

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Evapotranspiration (ET) from the land surface is an important hydrometeorological factor in the exchange of energy between the atmosphere and land surface. The accurate quantification for management of water resources and understanding of climate change are crucial, requiring continuous temporal and spatial monitoring. The objective of this study is to apply and estimate daily actual ET using semiempirical method, B-method, which is based on surface energy balance over heterogeneous area, Korea. To estimate daily ET, we used geostationary meteorological satellite data (Communication, Ocean and Meteorological Satellite, COMS) and polarorbiting satellite data (Système Pour l'Observation de la Terre, SPOT). Estimated daily ET using only satellite data was relatively accurate and reflects land surface characteristics. It had high periodicity and spatial resolution over a wide area on clear-sky days. The daily ET was overestimated by about 1 mm/day at the two flux tower measurements sites, but the simulated seasonal variation and pattern were in good agreement with flux tower measurements. In the mixed forest, the root-mean-square error (RMSE) was 0.94 mm/day and the bias was 1.05 mm/day, while, in the rice paddy, RMSE was 1.12 mm/day and bias was 1.21 mm/day.

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Section: Estimation Of Daily Air Temperature (Ta)mentioning

confidence: 99%

COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

Park¹,

Jang²,

Kim³

et al. 2017

Journal of Sensors

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“…Generally, near-surface atmospheric temperature refers to air temperature between 1.25 and 2 m above the ground. Statistical data [ 31 ] showed that the average summer extreme maximum temperature in Nanjing is 310 K (36.85 °C).…”

Section: Thermal Radiation Modelmentioning

confidence: 99%

Cooling Performance and Thermal Radiation Model of Asphalt Mixture with Modified Infrared Powder

et al. 2021

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This research studied a new material named modified infrared powder (MIRP) for decreasing the high temperature of asphalt pavements which can help alleviate the urban heat island effect to some extent. Based on the physical apparent density tests of materials and infrared thermal radiation test, the cooling performance of MIRP was obtained. X-ray diffraction analysis and scanning electron microscopy test (SEM) were conducted to analyze the chemical composition and the microstructure of MIRP, respectively. According to the radiant heat transfer theory, a thermal radiation model of the pavement equilibrium temperature was established by microscopic and chemical analysis to study the influence of thermal radiation asphalt mixture and reveal its cooling performance. The results show that the main components of MIRP are metal oxides and nonmetallic oxides which improve its infrared emissivity. Compared with limestone mineral powder asphalt mortar, the asphalt mortar with MIRP had a more compact structure and uniform distribution, and enhanced the overall structural performance of the mixture. The thermal radiation model reveals that the pavement equilibrium temperature combined with the MIRP in asphalt mixture decreases with the increase of the longwave emissivity, and it diminishes with the decrease of the shortwave absorptivity.

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“…And Surface observation of satellite can be used to extracting information of near-surface environmental conditions such as Ta because satellite observation can represent better spatial coverage [14][15][16][17]. Therefore, many studies proposed Ta retrieval method with LST of satellite data to retrieve Ta in a large area [17][18][19][20]. In many studies and satellite algorithms, LST is calculated by observing the energy emitted from Earth's surface through satellite remote sensing.…”

Section: Introductionmentioning

confidence: 99%

“…Because Ta and LST are highly correlated, it may be possible to retrieve Ta from the readings of the two IR channels (11 and 12 µm) used for LST retrieval method. In [17][18][19][20], Ta has been measured from LST by multiple regression. But there is no official LST data from Landsat-8 therefore we needed to use other data which was validated.…”

Section: Introductionmentioning

confidence: 99%

“…But there is no official LST data from Landsat-8 therefore we needed to use other data which was validated. Moreover, studies in similar study area can be found in [17,20], but as I mentioned these studies used the multiple regression method. Therefore, we investigated the ability to retrieve Ta with IR channels data using a deep neural network (DNN) to try to find another method.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Estimating midday near-surface air temperature by weighted consideration of surface and atmospheric moisture conditions using COMS and SPOT satellite data

Cited by 4 publications

References 35 publications

COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

Cooling Performance and Thermal Radiation Model of Asphalt Mixture with Modified Infrared Powder

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

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