• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.309-324
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• 2008

In this study the retrieval algorithms have been developed to retrieve total precipitable water (TPW) from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) infrared measurements using a physical iterative retrieval method and a split-window technique over East Asia. Retrieved results from these algorithms were validated against Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) over ocean and radiosonde observation over land and were analyzed for investigating the key factors affecting the accuracy of results and physical processes of retrieval methods. Atmospheric profiles from Regional Data Assimilation and Prediction System (RDAPS), which produces analysis and prediction field of atmospheric variables over East Asia, were used as first-guess profiles for the physical retrieval algorithm. We used RTTOV-7 radiative transfer model to calculate the upwelling radiance at the top of the atmosphere. For the split-window technique, regression coefficients were obtained by relating the calculated brightness temperature to the paired radiosonde-estimated TPW. Physically retrieved TPWs were validated against SSM/I and radiosonde observations for 14 cases in August and December 2004 and results showed that the physical method improves the accuracy of TPW with smaller bias in comparison to TPWs of RDAPS data, MODIS products, and TPWs from split-window technique. Although physical iterative retrieval can reduce the bias of first-guess profiles and bring in more accurate TPWs, the retrieved results show the dependency upon initial guess fields. It is thought that the dependency is due to the fact that the water vapor absorption channels used in this study may not reflect moisture features in particular near surface.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.213-222
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• 2008

Although hyperspectral sensing data have shown great potential to derive various surface information that is not usually available from conventional multispectral image, the acquisition of proper hyperspectral image data are often limited. To use ground-based hyperspectral camera image for remote sensing studies, radiometric calibration should be prerequisite. The objective of this study is to develop radiometric calibration procedure to convert image digital number (DN) value to surface reflectance for the 120 bands ground-based hyperspectral camera. Hyperspectral image and spectral measurements were simultaneously obtained from the experimental target that includes 22 different surface materials of diverse spectral characteristics at wavelength range between 400 to 900 nm. Calibration coefficients to convert image DN value to at-sensor radiance were initially derived from the regression equations between the sample image and spectral measurements using ASD spectroradiometer. Assuming that there is no atmospheric effects when the image acquisition and spectral measurements were made at very close distance in ground, we were also able to derive calibration coefficients that directly transform DN value to surface reflectance. However, these coefficients for deriving reflectance values should not be applied when the camera is used for aerial image that contains significant effect from atmosphere and further atmospheric correction procedure is required in such case.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.537-546
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• 2007

In hyperspectral remote sensing, linear spectral mixture model is a common procedure decomposing into the components of a mixed pixel and estimating the fraction of each end-member. Although linear spectral mixture model is frequently used in geology and mineral mapping because this model is simple and easy to apply, this model is not always valid in forest and urban area having rather complex structure. This study aims to analyze possible error for applying linear spectral mixture model. For the study, we measured laboratory spectra of mixture sample, having various materials, fractions, distributions. The accuracy of linear mixture model is low with the mixture sample having similar fraction because the multi-scattering between components is maximum. Additionally, this multi-scattering is related to the types, fraction, and distribution of components. Further analysis is necessary to quantify errors from linear spectral mixture model.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.553-563
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• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.469-483
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• 2006

An algorithm was developed to estimate stability indices (SI) over the Korean peninsula using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) infrared brightness temperatures (TBs). The SI is defined as the stability of the atmosphere in the hydrostatic equilibrium with respect to the vertical displacements and is used as an index for the potential severe storm development. Using atmosphere temperature and moisture profiles from Regional Data Assimilation and Prediction System (RDAPS) as initial guess data for a nonlinear physical relaxation method, K index (KI), KO Index (KO), lifted index (LI), and maximum buoyancy (MB) were estimated. A fast radiative transfer model, RTTOV-7, is utilized for reducing the computational burden related to the physical relaxation method. The estimated TBs from the radiative transfer simulation are in good agreement with observed MODIS TBs. To test usefulness for the short-term forecast of severe storms, the algorithm is applied to the rapidly developed convective storms. Compared with the SIs from the RDAPS forecasts and NASA products, the MODIS SI obtained in this research predicts the instability better over the pre-convection areas. Thus, it is expected that the nowcasting and short-term forecast can be improved by utilizing the algorithms developed in this study.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.925-939
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• 2018

Absorbed shortwave radiation at the surface is an important component of energy analysis among the atmosphere, land, and ocean. In this study, the absorbed shortwave radiation was calculated using a radiation model and surface broadband albedo data for application to Geostationary Earth Orbit Korea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A). And the results (GWNU algorithm) were compared with CERES data and calculation results using pyranometer and MODIS (Moderate Resolution Imaging Spectroradiometer) data to be selected as the reference absorbed shortwave radiation. This GWNU algorithm was also compared with the physical and statistical algorithms of GOSE-R ABI and two algorithms (Li et al., 1993; Kim and Jeong, 2016) using regression equation. As a result, the absorbed shortwave radiation calculated by GWNU algorithm was more accurate than the values calculated by the other algorithms. However, if the problem about computing time and accuracy of albedo data arise when absorbed shortwave radiation is calculated by GWNU algorithm, then the empirical algorithms explained above should be used with GWNU algorithm.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.212-226
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• 2019

This study derived spectral Lambertian Equivalent Reflectance (LER) over East Asia from the observations of Ozone Monitoring Instrument (OMI) onboard polar-orbit satellite Aura. The climatological (October 2004-September 2007) LER values were compared with the surface reflectance products of OMI or MODerate resolution Imaging Spectroradiometer (MODIS) in terms of the atmosphere-environment variables as follows: wavelength (UV, visible), surface properties (land, ocean), and cloud filtering. Four kinds of LER outputs in the UV and visible region (328-500 nm) were retrieved based on the averages of lowest (1, 5, and 10%) surface reflectance values as well as the minimum reflectance. The average of the lowest 10% among them was in best agreement with the OMI product: correlation coefficient (0.88), RMSE (1.0%) and mean bias (-0.3%). The 10% average and OMI LER values over ocean were 2% larger in UV than in visible, while the values over land were 1% smaller. The LER variability on the wavelength and surface property was highest (~3%) in the condition of both land and visible, particularly in the ice-cap and desert regions. The minimum reflectance values over the oceanic and inland sample areas overestimated the MODIS product by 1.4%. This high-resolution MODIS observations were effective in removing cloud contamination. The relative errors of the 10% average to MODIS were smaller (-0.6%) over ocean but larger (1.5%) over land than those of the OMI product to MODIS. The reduced relative error in the OMI product over land may result from additional cloud filtering using the Landsat data. This study will be useful when retrieveing the surface reflectance from geostationary-orbit environmental satellite (e.g., Geostationary Environment Monitoring Spectrometer; GEMS).

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5B
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• pp.459-469
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• 2010

This study is to develop a grid-based daily runoff model considering seasonal vegetation canopy condition. The model simulates the temporal and spatial variation of runoff components (surface, interflow, and baseflow), evapotranspiration (ET) and soil moisture contents of each grid element. The model is composed of three main modules of runoff, ET, and soil moisture. The total runoff was simulated by using soil water storage capacity of the day, and was allocated by introducing recession curves of each runoff component. The ET was calculated by Penman-Monteith method considering MODIS leaf area index (LAI). The daily soil moisture was routed by soil water balance equation. The model was evaluated for 930 $km^2$ Yongdam watershed. The model uses 1 km spatial data on landuse, soil, boundary, MODIS LAI. The daily weather data was built using IDW method (2000-2008). Model calibration was carried out to compare with the observed streamflow at the watershed outlet. The Nash-Sutcliffe model efficiency was 0.78~0.93. The watershed soil moisture was sensitive to precipitation and soil texture, consequently affected the streamflow, and the evapotranspiration responded to landuse type.

• Journal of the Korean earth science society
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• v.36 no.2
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• pp.139-157
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• 2015

We investigated the relative errors of satellite-observed Surface Skin Temperature (SST) data caused by sea ice in the northern hemispheric ocean ($30-90^{\circ}N$) during April 16-24, 2003-2014 by intercomparing MODerate Resolution Imaging Spectroradiometer (MODIS) Ice Surface Temperature (IST) data with two types of Atmospheric Infrared Sounder (AIRS) SST data including one with the AIRS/Advanced Microwave Sounding Unit-A (AMSU) and the other with 'AIRS only'. The MODIS temperatures, compared to the AIRS/AMSU, were systematically up to ~1.6 K high near the sea ice boundaries but up to ~2 K low in the sea ice regions. The main reason of the difference of skin temperatures is that the MODIS algorithm used infrared channels for the sea ice detection (i.e., surface classification), while microwave channels were additionally utilized in the AIRS/AMSU. The 'AIRS only' algorithm has been developed from NASA's Goddard Space Flight Center (NASA/GSFC) to prepare for the degradation of AMSU-A by revising part of the AIRS/AMSU algorithm. The SST of 'AIRS only' compared to AIRS/AMSU showed a bias of 0.13 K with RMSE of 0.55 K over the $30-90^{\circ}N$ region. The difference between AIRS/AMSU and 'AIRS only' was larger over the sea ice boundary than in other regions because the 'AIRS only' algorithm utilized the GCM temperature product (NOAA Global Forecast System) over seasonally-varying frozen oceans instead of the AMSU microwave data. Three kinds of the skin temperatures consistently showed significant warming trends ($0.23-0.28Kyr^{-1}$) in the latitude band of $70-80^{\circ}N$. The systematic disagreement among the skin temperatures could affect the discrepancies of their trends in the same direction of either warming or cooling.