• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.667-677
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• 2020

Crop water stress can be detected based on soil moisture content, crop physiological characteristics and remote-sensing technology. The detection of crop water stress is an important issue for the accurate assessment of yield decline. The crop water stress index (CWSI) has been introduced based on the difference between leaf and air temperature. In this paper, drone-based thermal infrared image was used to map of crop water stress in water control plot (WCP) and water deficit plot (WDP) over spring chinese cabbage fields. The spatial distribution map of CWSI was in strong agreement with the abnormal growth response factors (plant height, plant diameter, and measured value by chlorophyll meter). From these results, CWSI can be used as a good method for evaluation of crop abnormal growth monitoring.

• Proceedings of the KSRS Conference
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• v.1
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• pp.236-238
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.

• Proceedings of the KSRS Conference
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• v.1
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• pp.301-304
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• 2006

For the purpose of investigating the contributions of various gases to climate change, the thermal radiation associated with greenhouse gases are extracted from AIRS (Atmospheric Infrared Sounder) infrared radiances over the tropical pacific region. AIRS instrument which was launched on the EOS-Aqua satellite in May 2002 covers the spectral range from 650 cm-1 to 2700 cm-1 with a spectral resolution of between 0.4 cm-1 and 1 cm-1. In order to extract the thermal radiation absorbed by individual gases, the interfering background radiances at the top of the atmosphere are simulated using the radiative transfer code MODTRAN (MODerate spectral resolution atmospheric TRANsmittance). The simulations incorporated the temperature and water vapor profiles taken from NCEP (National Centers for Environmental Prediction) reanalyses. The differences between the simulated background radiance and AIRS-measured radiance result in the absorption of upward longwave radiation by atmospheric gases (i.e. greenhouse effect). The extracted absorption bands of individual gases will allow us to quantify the radiative forcing of individual greenhouse gases and thus those data will be useful for climate change studies and for the validation of radiative transfer codes used in general circulation models.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.149-159
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• 2005

Simultaneous observations of MODIS (Moderate-resolution Imaging Spectroradiometer) onboard the Aqua and Terra satellites and weather station at ground near the Inchon International Airport (37.2-37.7 N, 125.7-127.2 E) during the period from December 2002 to September 2004 have been utilized in order to analyze the characteristics of satellite-observed infrared (IR) and visible data under fog and clear-sky conditions, respectively. The differences $(T_{3.7-11})$ in brightness temperature between $3.75{\mu}m\;and\;11.0{\mu}m$ were used as threshold values for remote-sensing fog (or low clouds) from satellite during day and night. The $T_{3.7-11}$ value during daytime was greater by about 21 K when it was foggy than that when it was clear, but during nighttime fog it was less by 1.5 K than during nighttime clear-sky. The value was changed due to different values of emission of fog particles at the wavelength. Since the near-IR channel at $3.7{\mu}m$ was affected by solar and IR radiations in the daytime, both IR and visible channels (or reflectance) have been used to detect fog. The reflectance during fog was higher by 0.05-0.6 than that during clear-sky, and varied seasonally. In this study, the threshold values included uncertainties when clouds existed above a layer of fog.

• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.99-112
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• 2015

Observation of land surface temperature in industrial areas is problematic, as it is not possible to construct a network of weather stations with sufficiently high density and continuous operation in such zones. Multiphase remote sensing data that cover a wide area and take a short time to process can enable the user to precisely and continuously measure the current and changing land surface temperatures in a certain region. Jeollanam-Do in South Korea is undergoing rapid industrialization, with the establishment of a number of industrial complexes, such as the Gwangyang Steelworks, Yeosu Industrial Complex, Yulchon Industrial complex, and Daebul Industrial Complex. To look into the properties of industrial complex's temperature, this study uses the thermal band of Landsat 7 ETM+ images acquired under thermal infrared wavelengths in order to calculate and compare the surface temperatures of the four above-named industrial complexes. From this, it is possible to obtain the basic information about industrial complex for environmental and natural resource management, which will aid industrial complex planners in developing methods of addressing environmental problems.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.653-662
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• 2011

Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 ~ 280K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm.

• Proceedings of the KSRS Conference
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• v.2
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• pp.664-667
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• 2006

Seasonal threshold values for fog detection over the ten airport areas in the Korean Peninsula have been derived, using the satellite-observed data of polar-orbit (Aqua/Terra MODIS) and geostationary (GOES-9) during two years. The values are obtained from reflectance at 0.65 ${\mu}m$ $(R_{0.65})$ and the difference in brightness temperature between 3.7 ${\mu}m$ and 11 ${\mu}m$ $(T_{3.7-11})$. In order to examine the discrepancy between the threshold values of two kinds of satellites, the following parameters have been analyzed under the condition of daytime/nighttime and fog/clear-sky, utilizing their simultaneous observations over the Seoul Metropolitan Area. The parameters are the brightness temperature at 3.7 ${\mu}m$ $(T_{3.7})$, the temperature at 11 ${\mu}m$ $(T_{11})$, and $T_{3.7-11}$ for day and night. The $R_{0.65}$ data are additionally included in the daytime. The GOES-9 thresholds over the nine airport areas except the Cheongju airport have revealed the accuracy of 60% in the daytime and 70% in the nighttime, based on statistical verification as follows; FAR, POD and CSI. However, the accuracy decreases in the foggy cases with twilight, precipitation, short persistence, or the higher cloud above fog.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.339-351
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• 2016

For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel ($STD_{IR}$) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

• Korean Journal of Remote Sensing
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• v.3 no.1
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• pp.1-10
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• 1987

The study was performed to recognize the most preferable spectral chennels for discriminating geological materials using the portable radiometer. The portable radiometer covers the visible and short infrared regions from approximately 0.4 to 2.5 microns which are coincided with Landsat TM, and the rock samples used for the study are pyrophylites, andesites, granite, granodiorite and silicified sedimentary rocks which are collected in Yangsan-Dongrae fault area. The analysis of the rock sample provides a preliminary basis for determining the wavelength regions showing diagnostic spectral features and for discriminating hydrothermal altered rocks from the unaltered rocks. The measurement of spectral of spectral reflectance for the rock samples was carried out in the laboratory which environment condition such as temperature, light sources, and humidity are constant. The analysis of the measured data was based on correlation between the reflectance value of the rock samples, and the follow discriptions are output of the study. 1) Pyrophyllite shows absorption at 0.83 $\mu\textrm{m}$ due to the oxidation of pyrite, and absorption at 2.22 $\mu\textrm{m}$ due to OH. 2) The altered rocks have generally higher reflectance than the unaltered rocks. 3) The ratio mesurement of pyrophyllites shows strong absorption at band 5/6 and band 6/4(in Landsat TM 5/7, 7/4). The ratio 1/5(Landsat TM 1/5) may be useful to discriminate andesite from the granite.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.189-194
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• 2008

Middle-infrared (MIR) spectral region between 3.0 and $5.0\;{\mu}m$ in wavelength is useful for observing high temperature events such as volcanic activities and forest fire. However, atmospheric effects and sun irradiance in day time has not been well studied for this MIR spectral band. The objectives of this basic study is to evaluate atmospheric effects and eventually to estimate surface temperature from a single channel MIR image, although a typical approach utilize split-window method using more than two channels. Several parameters are involved for the correction including various atmospheric data and sun-irradiance at the area of interest. To evaluate the effect of sun irradiance, MODIS MIR images acquired in day and night times were used for comparison. Atmospheric parameters were modeled by MODTRAN, and applied to a radiative transfer model for estimating the sea surface temperature. MODIS Sea Surface Temperature algorithm based upon multi-channel observation was performed in comparison with results from the radiative transfer model from a single channel. Temperature difference of the two methods was $0.89{\pm}0.54^{\circ}C$ and $1.25{\pm}0.41^{\circ}C$ from the day-time and night-time images, respectively. It is also shown that the emissivity effect has by more largely influenced on the estimated temperature than atmospheric effects. Although the test results encourage using a single channel MR observation, it must be noted that the results were obtained from water body not from land surface. Because emissivity greatly varies on land, it is very difficult to retrieval land surface temperature from a single channel MIR data.