• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.482-491
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• 2012

Because the satellite imagery can detect the radiative heat from the surface using the thermal IR (TIR) channel, there have been many efforts to verify the relationship between the land surface temperature (LST) and urban heat island. However, the relationship between geomorphological characteristics like surface aspects and LST is relatively less studied. Therefore, the geomorphological elements, for example, surface aspects and surface slopes, are considered to evaluate their effects on the change of the surface temperature distribution using the Landsat 7 ETM+ TIR channel and the possibility of the image to detect anthropogenic heat from the surface. We found that the surface aspect is ignorable but the surface slope with the sun elevation influences on the surface temperature distribution. Also, the radiative heat from the surface to the atmosphere could not be accurately recorded by the satellite image due to the surface slope but the slope correction process used in this study could correct the surface temperature under slope condition and the slope correction, in fact, was not influenced on the average temperature of the surface. The possibility of the anthropogenic heat detection from the surface from the satellite imagery was verified as well.

• Journal of Space Technology and Applications
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• v.1 no.3
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• pp.356-363
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• 2021

It was common knowledge in textbooks that images acquired using mid-infrared ray were not suitable for measuring temperature near room temperature. But a recent satellite image using a mid-infrared sensor show the possibility that the result measured using the mid-infrared sensor can also measure the temperature near room temperature. In this paper, the possibility and accuracy of extraction room temperature information from satellite images with mid-infrared sensors are reviewed. The mid-infrared satellite image reviewed in this paper showed the temperature of room temperature well, and regarding the reliability as an absolute value of the measured temperature, the effect of the heat transfer amount due to the direct reflection of sunlight on the surface and the effect of the infrared absorption amount absorbed in the atmosphere can be seen as a relatively small or constant value. However, the problem of uncertainty in the radiation coefficient due to physical properties, which is the limit of the non-contact thermometer, remained a problem to be solved.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1951-1963
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• 2021

Remote sensing data using earth observation satellites in agricultural environment monitoring has many advantages over other methods in terms of time, space, and efficiency. Since the sensor mounted on the satellite measures the energy that sunlight is reflected back to the ground, noise is generated in the process of being scattered, absorbed, and reflected by the Earth's atmosphere. Therefore, in order to accurately measure the energy reflected on the ground (radiance), atmospheric correction, which must remove noise caused by the effect of the atmosphere, should be preceded. In this study, atmospheric correction sensitivity analysis, inter-satellite cross-analysis, and comparative analysis with ground observation data were performed to evaluate the application of KOMPSAT-3 satellite's atmospheric correction for agricultural application. As a result, in all cases, the surface reflectance after atmospheric correction showed a higher mutual agreement than the TOA reflectance before atmospheric correction, and it is possible to produce the time series vegetation index of the same standard. However, additional research is needed for quantitative analysis of the sensitivity of atmospheric input parameters and the tilt angle.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.637-654
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• 2023

In recent years, the number of users has been increasing with the rapid development of earth observation satellites. In response, the Committee on Earth Observation Satellites (CEOS) has been striving to provide user-friendly satellite images by introducing the concept of Analysis Ready Data (ARD) and defining its requirements as CEOS ARD for Land (CARD4L). In ARD, a mask called an Unusable Data Mask (UDM), identifying unnecessary pixels for land analysis, should be provided with a satellite image. UDMs include clouds, cloud shadows, terrain shadows, etc. Terrain shadows are generated in mountainous terrain with large terrain relief, and these areas cause errors in analysis due to their low radiation intensity. previous research on terrain shadow detection focused on detecting terrain shadow pixels to correct terrain shadows. However, this should be replaced by the terrain correction method. Therefore, there is a need to expand the purpose of terrain shadow detection. In this study, to utilize CAS500-4 for forest and agriculture analysis, we extended the scope of the terrain shadow detection to shaded areas. This paper aims to analyze the potential for terrain shadow detection to make a terrain shadow mask for South and North Korea. To detect terrain shadows, we used a Hill-shade algorithm that utilizes the position of the sun and a surface's derivatives, such as slope and aspect. Using RapidEye images with a spatial resolution of 5 meters and Sentinel-2 images with a spatial resolution of 10 meters over the Korean Peninsula, the optimal threshold for shadow determination was confirmed by comparing them with the ground truth. The optimal threshold was used to perform terrain shadow detection, and the results were analyzed. As a qualitative result, it was confirmed that the shape was similar to the ground truth as a whole. In addition, it was confirmed that most of the F1 scores were between 0.8 and 0.94 for all images tested. Based on the results of this study, it was confirmed that automatic terrain shadow detection was well performed throughout the Korean Peninsula.