Abstract
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.
Highlights
Air temperature (Ta), defined as the temperature at 2 m above the land surface, is one of the most important variables in regional and global weather models of the terrain and its characteristics [1,2]
Verification for optimal model selection was performed by randomly dividing the entire dataset into 70% and 30% portions for use as data for Ta production and verification, respectively
In our Deep Neural Network (DNN) model, Automated Synoptic Observing System (ASOS) Ta was the dependent variable, and Band 10, Band 11, NDVI, and Normalized Difference Water Index (NDWI) were applied as the independent variables
Summary
Air temperature (Ta), defined as the temperature at 2 m above the land surface, is one of the most important variables in regional and global weather models of the terrain and its characteristics [1,2]. Ta affects the rates of biotic processes in the ecosystem, including phonologies, growth, carbon, fixation, insolation, and respiration through vegetation–. Ta is used in many areas of research that monitor climate change, global warming, and abnormal temperature phenomenon. But it only represents a relatively small area because it is measured locally at in-situ stations. Most of retrieved data from satellite data were retrieved using various ways in a large area like atmosphere, land and ocean.
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