• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.83-94
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• 1998

To have a basic idea on the spectral reflectance signature in paddy rice canopy, we measured spectral reflectance from paddy rice canopy(Ilpumbyeo) using spectroradiometer (GER Inc. SFOV : 0.35~2.50 ${\mu}{\textrm}{m}$) in situ weekly or biweekly from transplanting to ripening stage. Spectral reflectance of the visible range (0.4~0.7 ${\mu}{\textrm}{m}$) was decreased to below 5% and then slightly increased again after heading stage in rice canopy. Meanwhile spectral reflectance of the near-infrared range (0.7~1.1 ${\mu}{\textrm}{m}$) was increased to 40~50% and then decreased a great deal after panicle initiation stage in rice canopy. Landsat TM equivalent band set ($\bar{p}$$_{TMi}$) was created by averaging spectral reflectance values to the real TM bands. Correlation analysis between the rice crop variables (LAI, total dry matter) and TM equivalent band set ($\bar{p}$$_{TMi}$) showed that LAI and total dry matter of rice were highly correlated with visible bands such as $\bar{p}$$_{TM1}$, $\bar{p}$$_{TM2}$, and $\bar{p}$$_{TM3}$. Ratio values ($\bar{p}$$_{TMi}$/$\bar{p}$$_{TMi}$) such as $\bar{p}$$_{TM4}$/$\bar{p}$$_{TM2}$ were also highly correlated with rice crop variables such as LAI and total dry matter.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.38 no.2
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• pp.119-128
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• 2002

This study investigates dorsal aspect target strength with fish size, tilt angle and frequency characteristics for the schlegel's black rockfish(Sebastes achlegeli) and the red seabream (Pagrus major). This study was carried out on free swimming fish in a cage in order to obtain acoustic data of the biomass estimation using the scientific echo sounder. The results obtained from this study are summarized as follows; 1 The coefficients of the schlegel's black rockfish and the red seabream using maximum TS with fish length were expressed -63.7dB and -62.6dB at a frequency of 38kHz, -64.4dB and -65.4dB at 120kHz, and -62.4dB and -65.0dB at 200kHz, respectively. 2. The coefficients of the schlegel\`s black rockfish and the red seabream using averaged TS with fish length were expressed -68.4dB and -67.9dB at a frequency of 38kHz, -73.4dB and -72.7dB at 120kHz, and -70.BdE and -73.4dB at 2001Hs, respectively. 3. The coefficients of the schlegel's black rockfish and the red seabream using maximum TS with body weight were expressed -52.0dB and -50.9dB at a frequency of 38kHz, -52.7dB and -53.7dB at 120kHz, and -50.7dB and -53.3dB at 200kHz, respectively. 4. The coefficients of the schlegel's black rockfish and the red seabream using averaged TS with body weight were expressed -56.7dB and -56.2dB at a frequency of 38kHz, -61.7dB and -61.0dB at 120kHz, and -59.ldE and -61.6dB at 200kHz, respectively. 5. Varying the tiIt angle of the two red seabream from -26$^{\circ}$to +25$^{\circ}$, the variation width of target strength expressed smaller at a frequency of 38kHz than at 120kHz and expressed about 3~6dB higher head up than head down at 120kHz.

• 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.