• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1669-1683
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• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.15-33
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• 2021

In this study, the possibility of discriminating Fire blight (FB) infection tested using the hyperspectral imagery. The reflectance of healthy and infected leaves and branches was acquired with 5 nm of full width at high maximum (FWHM) and then it was standardized to 10 nm, 25 nm, 50 nm, and 80 nm of FWHM. The standardized samples were divided into training and test sets at ratios of 7:3, 5:5 and 3:7 to find the optimal bands of FWHM by the decision tree analysis. Classification accuracy was evaluated using overall accuracy (OA) and kappa coefficient (KC). The hyperspectral reflectance of infected leaves and branches was significantly lower than those of healthy green, red-edge (RE) and near infrared (NIR) regions. The bands selected for the first node were generally 750 and 800 nm; these were used to identify the infection of leaves and branches, respectively. The accuracy of the classifier was higher in the 7:3 ratio. Four bands with 50 nm of FWHM (450, 650, 750, and 950 nm) might be reasonable because the difference in the recalculated accuracy between 8 bands with 10 nm of FWHM (440, 580, 640, 660, 680, 710, 730, and 740 nm) and 4 bands was only 1.8% for OA and 4.1% for KC, respectively. Finally, adding two bands (550 nm and 800 nm with 25 nm of FWHM) in four bands with 50 nm of FWHM have been proposed to improve the usability of multispectral image sensors with performing various roles in agriculture as well as detecting FB with other combinations of spectral bands.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.1-16
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• 1996

Korea Aerospace Research Institute(KARI) is developing a Korea Multi-Purpose Satellite I(KOMPSAT-I) which accommodates Electro-Optical Camera(EOC), Ocean Color Imager(OCI), Space Physics Sensor(SPS) for cartography, ocean color monitoring, and space environment monitoring respectively. The satellite has the weight of about 500 kg and is operated on the sun synchronized orbit with the altitude of 685km, the orbit period of 98 minutes, and the orbit revisit time of 28days. The satellite will be launched in the third quarter of 1999 and its lifetime is more than 3 years. EOC has cartography mission to provide images for the production of scale maps, including digital elevation models, of Korea from a remote earth view in the KOMPSAT orbit. EOC collects panchromatic imagery with the ground sample distance(GSD) of 6.6m and the swath width of 15km at nadir through the visible spectral band of 510-730 nm. EOC scans the ground track of 800km per orbit by push-broom and body pointed method. OCI mission is worldwide ocean color monitoring for the study of biological oceanography. OCI is a multispectral imager generating 6 color ocean images with and <1km GSD by whisk-broom scanning method. OCI is designed to provide on-orbit spectral band selectability in the spectral range from 400nm to 900nm. The color images are collected through 6 primary spectral bands centered at 443, 490, 510, 555, 670, 865nm or 6 spectral bands selected in the spectral range via ground commands after launch. SPS consists of High Energy Particle Detector(HEPD) and Ionosphere Measurement Sensor(IMS). HEPD has mission to characterize the low altitude high energy particle environment and to study the effects of radiation environment on microelectronics. IMS measures densities and temperature of electrons in the ionosphere and monitors the ionospheric irregularities in KOMPSAT orbit.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.981-996
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• 2018

In agriculture, remote sensing data using earth observation satellites have many advantages over other methods in terms of time, space, and efficiency. This study analyzed the changes of reflectance and vegetation index according to atmospheric correction of images before using satellite images in agriculture. Top OF Atmosphere (TOA) reflectance and surface reflectance through atmospheric correction were calculated to compare the reflectance of each band and Normalized Vegetation difference Index (NDVI). As a result, the NDVI observed from field measurement sensors and satellites showed a higher agreement and correlation than the TOA reflectance calculated from surface reflectance using atmospheric correction. Comparing NDVI before and after atmospheric correction for multi-temporal images, NDVI increased after atmospheric corrected in all images. garlic and onion cultivation area and forest where the vegetation health was high area NDVI increased more 0.1. Because the NIR images are included in the water vapor band, atmospheric correction is greatly affected. Therefore, atmospheric correction is a very important process for NDVI time-series analysis in applying image to agricultural field.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.301-308
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• 2022

Algae is an indispensable primary producer in the ecosystem by supplying energy to consumers in the aquatic ecosystem, and is largely divided into green algae, blue-green algae, and diatoms. In the case of blue-green algae, the water temperature rises, which occurs in the summer and overgrows, which is the main cause of the algae bloom. Recently, the change in the occurrence time and frequency of the algae bloom is increasing due to climate change. Existing algae survey methods are performed by collecting water and measuring through sensors, and time, cost and manpower are limited. In order to overcome the limitations of these existing monitoring methods, research has been conducted to perform remote monitoring using spectroscopic devices such as multispectral and hyperspectral using satellite image, UAV, etc. In this study, we tried to confirm the possibility of species classification of remote monitoring through laboratory-scale experiments through algal culture and river water collection. In order to acquire hyperspectral images, a hyperspectral sensor capable of analyzing at 400-1000 nm was used. In order to extract the spectral characteristics of the collected river water for classification of algae species, filtration was performed using a GF/C filter to prepare a sample and images were collected. Radiation correction and base removal of the collected images were performed, and spectral information for each sample was extracted and analyzed through the process of extracting spectral information of algae to identify and compare and analyze the spectral characteristics of algae, and remote sensing based on hyperspectral images in rivers and lakes. We tried to review the applicability of monitoring.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1273-1281
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• 2023

Absolute radiometric calibration is a crucial process in converting the electromagnetic signals obtained from satellite sensors into physical quantities. It is performed to enhance the accuracy of satellite data, facilitate comparison and integration with other satellite datasets, and address changes in sensor characteristics over time or due to environmental conditions. In this study, field campaigns were conducted to perform vicarious calibration for the multispectral channels of the CAS500-1. Two valid field observations were obtained under clear-sky conditions, and the top-of-atmosphere (TOA) radiance was simulated using the MODerate resolution atmospheric TRANsmission 6 (MODTRAN 6) radiative transfer model. While a linear relationship was observed between the simulated TOA radiance of tarps and CAS500-1 digital numbers(DN), challenges such as a wide field of view and saturation in CAS500-1 imagery suggest the need for future refinement of the calibration coefficients. Nevertheless, this study represents the first attempt at absolute radiometric calibration for CAS500-1. Despite the challenges, it provides valuable insights for future research aiming to determine reliable coefficients for enhanced accuracy in CAS500-1's absolute radiometric calibration.