• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.787-799
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• 2018

Accurate and effective mapping is critical step to monitor the spatial distribution and change of flood inundated area in large scale flood event. In this study, we try to suggest methods to use low spatial resolution satellite optical imagery for flood mapping, which has high temporal resolution to cover wide geographical area several times per a day. We selected the Sebou watershed flood in Morocco that was occurred in early 2010, in which several hundred $km^2$ area of the Gharb lowland plain was inundated. MODIS daily surface reflectance product was used to detect the flooded area. The study area showed several distinct spectral patterns within the flooded area, which included pure turbid water and turbid water with vegetation. The flooded area was extracted by thresholding on selected band reflectance and water-related spectral indices. Accuracy of these flooding detection methods were assessed by the reference map obtained from Landsat-5 TM image and qualitative interpretation of the flood map derived. Over 90% of accuracies were obtained for three methods except for the NDWI threshold. Two spectral bands of SWIR and red were essential to detect the flooded area and the simple thresholding on these bands was effective to detect the flooded area. NIR band did not play important role to detect the flooded area while it was useful to separate the water-vegetation mixed flooded classes from the purely water surface.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.149-158
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• 2017

Fine temporal and spatial resolution of image data are necessary to monitor the phenology of vegetation. However, there is no single sensor provides fine temporal and spatial resolution. For solve this limitation, researches on spatiotemporal data fusion methods are being conducted. Among them, FSDAF (Flexible spatiotemporal data fusion) can fuse each band in high accuracy.In thisstudy, we applied MODIS NDVI and Landsat NDVI to enhance time resolution of NDVI based on FSDAF algorithm. Then we proposed the possibility of utilization in vegetation phenology monitoring. As a result of FSDAF method, the predicted NDVI from January to December well reflect the seasonal characteristics of broadleaf forest, evergreen forest and farmland. The RMSE values between predicted NDVI and actual NDVI (Landsat NDVI) of August and October were 0.049 and 0.085, and the correlation coefficients were 0.765 and 0.642 respectively. Spatiotemporal data fusion method is a pixel-based fusion technique that can be applied to variousspatial resolution images, and expected to be applied to various vegetation-related studies.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.121-131
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• 2018

Climate change has caused changes in environmental factors that have a direct impact on agriculture such as temperature and precipitation. The meteorological disaster that has the greatest impact on agriculture is drought, and its forecasts are closely related to agricultural production and water supply. In the case of terrestrial data, the accuracy of the spatial map obtained by interpolating the each point data is lowered because it is based on the point observation. Therefore, acquisition of various meteorological data through satellite imagery can complement this terrestrial based drought monitoring. In this study, Evaporative Stress Index (ESI) was used as satellite data for drought determination. The ESI was developed by NASA and USDA, and is calculated through thermal observations of GOES satellites, MODIS, Landsat 5, 7 and 8. We will identify the difference between ESI and other satellite-based drought assessment indices (Vegetation Health Index, VHI, Leaf Area Index, LAI, Enhanced Vegetation Index, EVI), and use it to analyze the drought in South Korea, and examines the applicability of ESI as a new indicator of agricultural drought monitoring.

• Proceedings of the KSRS Conference
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• v.1
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• pp.90-93
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• 2006

The first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meteorological Satellite (COMS) is scheduled for launch in 2008. GOCI includes the eight visible-to-near-infrared (NIR) bands, 0.5km pixel resolution, and a coverage region of 2500 ${\times}$ 2500km centered at 36N and 130E. GOCI has had the scope of its objectives broadened to understand the role of the oceans and ocean productivity in the climate system, biogeochemical variables, geological and biological response to physical dynamics and to detect and monitor toxic algal blooms of notable extension through observations of ocean color. The special feature with GOCI is that like MODIS, MERIS and GLI, it will include the band triplets 660-680-745 for the measurements of sun-induced chlorophyll-a fluorescence signal from the ocean. The GOCI will provide SeaWiFS quality observations with frequencies of image acquisition 8 times during daytime and 2 times during nighttime. With all the above features, GOCI is considered to be a remote sensing tool with great potential to contribute to better understanding of coastal oceanic ecosystem dynamics and processes by addressing environmental features in a multidisciplinary way. To achieve the objectives of the GOCI mission, we develop the GOCI Data Processing System (GDPS) which integrates all necessary basic and advanced techniques to process the GOCI data and deliver the desired biological and geophysical products to its user community. Several useful ocean parameters estimated by in-water and other optical algorithms included in the GDPS will be used for monitoring the ocean environment of Korea and neighbouring countries and input into the models for climate change prediction.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.654-655
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• 2003

Korea has experienced 10 a Cochlodinium polykrikoides red tide outbreaks during the last 10 years (1993-2002). The monitoring activities at National Fisheries Research and Development Institute (NFRDI) in Korea have been extended to all the coastal waters after the worst of fish killing by C. polykrikoides blooms in 1995. NFRDI is looking forward to finding out the feasibility of red tide detection around Korean waters using satellite remote sensing of NOAA/AVHRR, Orbview-2/SeaWiFS, IRS-P4/OCM and Terra/MODIS on real time base. In this study, we used several alternative methods including climatological analysis, spectral and optical methods which may offer a potential detection of the major species of red tide in Korean waters. The relationship between the distribution of SST and C. polykrikoides bloom areas was studied. In climatological analysis, NOAA, SeaWiFS, OCM satellite data in 20th and 26th August 2001 were chosen using the known C. polykrikoides red tide bloom area mapped by helicopter reconnaissance and ground observation. The 26th August, 2001 SeaWiFS chlorophyll a anomaly imageries against the imageries of non-occurring red tide for August 20, 2001 showed the areas C. polykrikoides occurred. The anomalies of chlorophyll a concentration from satellite data between before and after red tide outbreaks showed the similar distribution of C. polykrikoides red tide in 26th August, 2001. The distribution of the difference in SST between daytime and nighttime also showed the possibility of red tide detection. We used corrected vegetation index (CVI) to detect floating vegetation and submerged vegetation containing algal blooms. The simple result of optical absorption from C. polykrikoides showed that if we use the optical characteristics of each red tide we will be able to get the feasibility of the red tide detection.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.77-89
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• 2004

The spatial and temporal variations of aerosol optical depth (AOD) over Northeast Asia regions have special importance in the aerosol research for estimation of aerosol radiative forcing parameters and climate change. Aerosol optical and physical properties (AOD and ${\AA}$ngstrom parameter) have been investigated by using Moderate Resolution Imaging Spectroradiometer (MODIS) and Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) to estimate aerosol characteristics over the study region during 2001. Additionally, aerosol characteristics over the Korean peninsular during Aerosol Characteristic Experiment in Asia (ACE-Asia) Intensive Observation Period (IOP) have been investigated by using satellite observations. The results showed that the daily-observed aerosol data indicate seasonal variations with relatively higher aerosol loading in the spring and very low during the winter. The typical Asian dust case showed higher AOD (>0.7) with lower Angstrom exponent (<0.5) and higher AI (>0.5) that is mainly due to the composition of coarse particles in the springtime. Mean AOD for 2001 at 4 different places showed 0.65$\pm$0.37 at Beijing, 0.31$\pm$0.19 at Gosan, 0.54$\pm$0.26 at Seoul, and 0.38$\pm$0.19 at Kwangju, respectively. An interesting result was found in the present study that polluted aerosol events with small size dominated-aerosol loading around the Korean peninsular are sometimes observed. The origin of these polluted aerosols was thought to East China. Aerosol distribution from satellite images and trajectory results shows the proof of aerosol transport. Therefore, aerosol monitoring using satellite data is very useful.

• 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.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1407-1419
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• 2020

Long time-series gridded data is crucial for the analyses of Earth environmental changes. Climate reanalysis and satellite images are now used as global-scale periodical and quantitative information for the atmosphere and land surface. This paper examines the feasibility of DCT-PLS (penalized least square regression based on discrete cosine transform) for the spatial gap filling of gridded data through the experiments for multiple variables. Because gap-free data is required for an objective comparison of original with gap-filled data, we used LDAPS (Local Data Assimilation and Prediction System) daily data and MODIS (Moderate Resolution Imaging Spectroradiometer) monthly products. In the experiments for relative humidity, wind speed, LST (land surface temperature), and NDVI (normalized difference vegetation index), we made sure that randomly generated gaps were retrieved very similar to the original data. The correlation coefficients were over 0.95 for the four variables. Because the DCT-PLS method does not require ancillary data and can refer to both spatial and temporal information with a fast computation, it can be applied to operative systems for satellite data processing.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.1-9
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• 2015

The development of drought index that provides detailed-spatial-resolution drought information is essential for improving drought planning and preparedness. The objective of this study was to develop the concept of using satellite-based hybrid drought index called the Vegetation Drought Response Index in South Korea (VegDRI-SKorea) that could improve spatial resolution for monitoring local and regional drought. The VegDRI-SKorea was developed using the Classification And Regression Trees (CART) algorithm based on remote sensing data such as Normalized Difference Vegetation Index (NDVI) from MODIS satellite images, climate drought indices such as Self Calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Index (SPI), and the biophysical data such as land cover, eco region, and soil available water capacity. A case study has been done for the 2012 drought to evaluate the VegDRI-SKorea model for South Korea. The VegDRI-SKorea represented the drought areas from the end of May and to the severe drought at the end of June. Results show that the integration of satellite imageries and various associated data allows us to get improved both spatially and temporally drought information using a data mining technique and get better understanding of drought condition. In addition, VegDRI-SKorea is expected to contribute to monitor the current drought condition for evaluating local and regional drought risk assessment and assisting drought-related decision making.