• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.275-284
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• 2001

This study was carried out to evaluate high resolution satellite imagery of IKONOS for classifying the land cover, especially forest type. The IKONOS imagery of 11km$\times$11km size was taken on April 24, 2000 in Bong-pyoung Myun Pyungchang-Gun, Kangwon Province. Land cover classes were water, coniferous evergreen, Larix leptolepis, broad-leaved tree, bare land, farm land, grassland, sandy soil and asphalted area. Supervised classification method with algorithm of maximum likelihood was applied for classification. The terrestrial survey was also carried out to collect the reference data in this area. The accuracy of the classification was analyzed with the items of overall accuracy, producer's accuracy, user's accuracy and k for test area through the error matrix. In the accuracy analysis of the test area, overall accuracy was 94.3%, producer's accuracy was 77.0-99.9%, user's accuracy was 71.9-100% and k and 0.93. Classes of bare land, sandy soil and farm land were less clear than other classes, whereas classification result of IKONOS in forest area showed higher performance than that of other resolution(5-30m) satellite data.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.361-368
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• 2004

Hydraulic models has a module to calculate SCS-CN values in order to estimate amount of water flow, which can be done with remotely sensed data and GIS data. The choice of the ancillary data tends to determine the range of SCS-CN values. We compare the results of SCS-CN value with satellite data of different spatial resolution and with soil maps of different scale. Mokhyun river basin was chosen,partly because of availbility of water quality and quantity data, partly because of rapid changes in land use and land cover since last ten years. The average CN values were calculated with spatial resolutions of 2.5 meter and 30 meter, We could not find any different result due to spatial resolution of CN resolution but due to both soil maps and to land cover maps. Further studies should be done for more than two kinds of satellite data.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.411-419
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• 2003

An area corresponding to the spatial resolution of optical remote sensor imagery often includes more than one pure surface material. In such case, a pixel value represents a mixture of spectral reflectance of several materials within it. This study attempts to apply the spectral mixture analysis on forest and to evaluate the information content of endmember fractions resulted from the spectral unmixing. Landsat-7 ETM+ image obtained over the study area in the Kwangneung Experimental Forest was initially geo-referenced and radiometrically corrected to reduce the atmospheric and topographic attenuations. Linear mixture model was applied to separate each pixel by the fraction of six endmember: deciduous, coniferous, soil, built-up, shadow, and rice/grass. The fractional values of six endmember could be used to separate forest cover in more detailed spatial scale. In addition, the soil fraction can be further used to extract the information related to the canopy closure. We also found that the shadow effect is more distinctive at coniferous stands.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.6
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• pp.593-601
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• 2015

The NDVI (Normalized difference vegetation index) is used as indicators of crop growth situation in remote sensing. To measure or validate the NDVI, reliable NDVI sensors have been needed. We tested new fixed-field NDVI sensor, "SRS (Spectral Reflectance Sensor)" developed by Decagon Devices, during Kimchi cabbage growing season at the cultivation area located in Gochang, Gangneung and Taebaek in Korea from 2014 to 2015. The diurnal variation of NDVI measured by SRS (SRS NDVI) showed a slight ${\cap}$-profile shape and was affected by water on the sensor surface. This means that SRS NDVI around noontime is resonable, except rainy day. Comparisons were made between the SRS NDVI and NDVI of used widely mobile sensor (Cropcircle NDVI). The comparisons indicate that SRS NDVI are close to Cropcircle NDVI (R=0.99). SRS NDVI time series displayed change of the plant height and leaf width of Kimchi cabbage. An obvious exponential relationship is found between SRS NDVI and the plant height ($R^2{\geq}0.92$) and leaf width ($R^2{\geq}0.92$) of Kimchi cabbage. Thus, SRS NDVI will be used as indicator of crop growth situation and a very powerful tool for evaluation of remote sensing NDVI estimates and associated corrections.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.497-510
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• 2010

In this study, comparisons of the backscattering coefficients and the coherence values which had been extracted from SAR (Synthetic Aperture Radar) images such as JERS-1, ENVISAT and ALOS satellites with surface roughness, surface geometric and soil moisture content were carried out. As the results of analysis using the backscattering coefficient and coherence values from SAR images, the coherence was shown high in the region containing more of mud fraction due to higher viscosity of fine grain-size. A lot of tidal channels were well developed in the Ganghwa tidal flat, affecting the drainage of seawater and subsequent soil moisture content by exposure time of tidal flat. The backscattering coefficient. consequently, appeared to be lower in sand flat and mix flat with decrease of soil moisture. In contrast, most mud flats were distributed at high elevation so that soil moisture was not much influenced by seawater. The backscattering coefficient in mud flat seemed to have a relationship with the density of tidal channel. In addition, lowering backscattering coefficients in the all Ganghwa tidal flat was observed when surface remnant water increased according to the amount of rainfall. The correlation between backscattering coefficient, coherence and sediment environment factors in the Ganghwa tidal flat was investigated. In the future, more quantitative spatial analysis will be helpful to well understand the sedimentary influence of various sediment environment factors.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.721-728
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• 2023

Agricultural drought is triggered by a depletion of moisture content in the soil, which hinders photosynthesis and thus increases carbon dioxide (CO₂) concentrations in the atmosphere. The aim of this study is to analyze the relationship between soil moisture (SM) and vegetation activity toward quantifying CO₂ concentration in the atmosphere. To this end, the MODerate resolution imaging spectroradiometer (MODIS), an optical multispectral sensor, was used to evaluate two regions in South Korea for validation. Vegetation activity was analyzed through MOD13A1 vegetation indices products, and MODIS gross primary productivity (GPP) product was used to calculate the CO₂ flux based on its relationship with respiration. In the case of SM, it was calculated through the method of applying apparent thermal inertia (ATI) in combination with land surface temperature and albedo. To validate the SM and CO₂ flux, flux tower data was used which are the observed measurement values for the extreme drought period of 2014 and 2015 in South Korea. These two variables were analyzed for temporal variation on flux tower data as daily time scale, and the relationship with vegetation index (VI) was synthesized and analyzed on a monthly scale. The highest correlation between SM and VI (correlation coefficient (r) = 0.82) was observed at a time lag of one month, and that between VI and CO₂ (r = 0.81) at half month. This regional study suggests a potential capability of MODIS-based SM, VI, and CO₂ flux, which can be applied to an assessment of the global view of the agricultural drought by using available satellite remote sensing products.

• Journal of Soil and Groundwater Environment
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• v.7 no.2
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• pp.35-44
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• 2002

In order to determine the contamination of the aromatic hydrocarbons in soil, a fiber-optic sensing technique with fluorescence detector has been proposed. Previous researches have shown that the optimal condition for detecting benzene, toluene, ethylbenzene, xylene (BTEX) was 260 nm /290 nm (excitation/emission wavelength). However, broader fluorescence spectra of BTEX-polluted soil sample ranging from 300 nm to 600 nm were observed. Additionally, the intensity of fluorescence increased with increasing BTEX concentration, which was conspicuous in the fine-particle soil, The overall results indicated that the suggested technique could be useful for in-situ monitoring system for subsurface oil-storage tank.

• Journal of Korea Water Resources Association
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• v.32 no.2
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• pp.163-176
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• 1999

Hydrology-based topographical informations generated by GIS techniques could be changed according to the selection of base map, algorithm of extraction, and so on. The purpose of this paper is to investigate the variation of SCS CN extracted by GIS technique and to propose the effective strategy for applying GIS to the rainfall-runoff simulation in ungaged basin. For experimental implementation, GIS spatial data, such as reconnaissance soil map, detailed interpretative soil map, landuse planning map and remotely sensed data(Landsat TM), were collected and generated to calculate the amount of effective rainfall in Pyungchang river basin. In applying SCS Runoff Curve Number to the test basin, the hydrological attribute data were analyzed. In addition, the characteristics of runoff responses according to the selection of GIS spatial data for SCS CN were reviewed. This study shows the applicability of GIS techniques to runoff simulation in ungaged basin by comparing with the measured flood hydrograph. It has been found that the detained interpretative soil map and remote sensing data are appropriate for calculating of SCS CN.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.973-983
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• 2020

Recently, SAR (Synthetic Aperture Radar) is being highlighted as a solution to the coarse spatial resolution of remote sensing data in water resources research field. Spatial resolution up to 10 m of SAR backscattering coefficient has facilitated more elaborate analyses of the spatial distribution of soil moisture, compared to existing satellite-based coarse resolution (>10 km) soil moisture data. It is essential, however, to multilaterally analyze how various hydrological and environmental factors affect the backscattering coefficient, to utilize the data. In this study, soil moisture estimated by WCM (Water Cloud Model) and linear regression is compared with in-situ soil moisture data at 5 soil moisture observatories in the Korean peninsula. WCM shows suitable estimates for observing instant changes in soil moisture. However, it needs to be adjusted in terms of errors. Soil moisture estimated from linear regression shows a stable error range, but it cannot capture instant changes. The result also shows that the effect of soil moisture on backscattering coefficients differs greatly by land cover, distribution of vegetation, and water content of vegetation, hence that there're still limitations to apply preexisting models directly. Therefore, it is crucial to analyze variable effects from different environments and establish suitable soil moisture model, to apply SAR to water resources fields in Korea.