• Proceedings of the KSRS Conference
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• v.1
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• pp.224-227
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• 2006

For estimation of three inherent optical properties (IOPs), the absorption coefficients for phytoplankton ($a_{ph}$) and suspended solid particle ($a_{ss}$) and dissolved organic matter ($a_{dom}$), from ocean reflectance, we used inversion of remote sensing reflectance model (Ahn et al., 2001) at this study. The IOP inversion model assumes that (1) the relationship between remote sensing reflectance ($R_{rs}$) and absorption (a) and backscattering ($b_{b}$) is well known, (2) the optical coefficients for pure water ($a_{w}$, $b_{bw}$) are known, (3) the spectral shapes of the specific absorption coefficients for phytoplankton ($a^*_{ph}$) and suspended solid particle ($a^*_{ss}$) and the specific backscattering coefficients for phytoplankton ($b_b^*_{ph}$) and suspended solid particle ($b_b^*_{ss}$) are known. The input data of IOP inversion model is used in-situ ocean optical data at the seawater around the Korea Peninsula for 5 years (2001-2005). We compared the output data of the IOP inversion model and the in-situ observation for seawater around the Korea Peninsula.

• Korean Journal of Remote Sensing
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• v.16 no.3
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• pp.243-260
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• 2000

Ocean remote sensing reflectance of just above water level was modeled using inherent optical properties of seawater contents, total absorption (a) and backscattering(bb) coefficients ($R_{rs}$=0.046 $b_b$/(a+$b_b$). This modeling was based on the specific absorption and backscattering coefficients of 5 optically active seawater components； phytoplankton pigments, non-chlorophyllous suspended particles, dissolved organic matters, heterotrophic microorganisms, and the other unknown particle components. Simulated remote sensing reflectance($R_{rs}$) and water leaving radiance(Lw) spectra were well agreed with in-situ measurements obtained using a bi-directional fields remote spectrometer in coastal waters and open ocean. $R_{rs}$ values in SeaWiFS bands from the model were analyzed to develop 2-band ratio ocean color chlorophyll with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The model algorithms were examined and compared with those observed insitu. Also, chlorophyll algorithm based on remote reflectance developed in this study fell in those obtained by a SeaBAM working group. The remote reflectance model will be very helpful to understand the variation of water leaving radiances caused by the various components in the seawater, and to develop new ocean color algorithm for CASE-II water using neural network method or other analytical method, and in the model of fine atmospheric signal correction.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1-3
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• 2003

In this study, spectroradiometer data were coupled with fluorometer data to find out the best suited bands ratio to monitor the chlorophyll a concentration for inland water. Remote sensing reflectance measurements were used to evaluate the performance of several default ocean color chlorophyll algorithms for SeaWiFS data. This study shows that the chlorophyll a concentration from fluorometer and reflectance from spectroradiometer lies in exploiting the signal provided by the chlorophyll a red absorption peak near 670nm. Two-band ratio based on a ratio of reflectance 670 and 700nm provided a good correlation for a linear model, compare with blue-green two band ratio.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.371-380
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• 2003

The primary objective of this study is to develop a suitable methodology to generate forest leaf area index (LAI) map at regional and local scales. To build empirical models, we collected the LAI values at 30 sample plots over the forest within the kyongan watershed area by the field measurements using an optical instrument. Landsat-7 ETM＋ multispectral data obtained at the same growing season with the field LAI measurement were used. Three datasets of remote sensing signal were prepared for analyzing the relationship with the field measured LAI value and they include raw DN, atmospherically corrected reflectance, and topographically corrected reflectance. From the correlation analysis and regression model development, we found that the radiometric correction of topographic effects was very critical step to increase the sensitivity of the multispectral reflectance to LAI. In addition, the empirical model to generate forest LAI map should be separately developed for each of coniferous and deciduous forest.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.558-560
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• 2003

This paper examines the capability of remote sensing technique for detecting and quantifying the foliar nutrients of oil palm crop. Study has been carried out in the Malaysian Palm Oil Board (MPOB) Research Station in Kluang Johore, Malaysia. Result of the study shows a strong relationship between measured foliar nutrient and the spectral reflectance measured using spectroradiometer. Model that has been developed can be used to estimate the nutrient concentration in the oil palm plantation at micro level and also at macro -level using appropriate satellite data.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.285-295
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• 2001

We developed a CASE-II water model that will enable the simulation of remote sensing reflectance($R_{rs}$) at the coastal waters for the retrieval of suspended sediments (SS) concentrations from satellite imagery. The model has six components which are: water, chlorophyll, dissolved organic matter (DOM), non-chlorophyllous particles (NC), heterotrophic microorganisms and an unknown component, possibly represented by bubbles or other particulates unrelated to the five first components. We measured $R_{rs}$, concentration of SS and chlorophyll, and absorption of DOM during our field campaigns in Korea. In addition, we generated $R_{rs}$ from different concentrations of SS and chlorophyll, and various absorptions of DOM by random number functions to create a large database to test the model. We assimilated both the computer generated parameters as well as the in-situ measurements in order to reconstruct the reflectance spectra. We validated the model by comparing model-reconstructed spectra with observed spectra. The estimated $R_{rs}$ spectra were used to (1) evaluate the performance of four wavelengths and wavelengths ratios for accurate retrieval of SS. 2) identify the optimum band for SS retrieval, and 3) assess the influence of the SS on the chlorophyll algorithm. The results indicate that single bands at longer wavelengths in visible better results than commonly used channel ratios. The wavelength of 625nm is suggested as a new and optimal wavelength for SS retrieval. Because this wavelength is not available from SeaWiFS, 555nm is offered as an alternative. The presence of SS in coastal areas can lead to overestimation chlorophyll concentrations greater than 20-500%.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.127-139
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• 2018

The GOCI atmospheric correction overland surfaces is essential for the time-series analysis of terrestrial environments with the very high temporal resolution. We develop an operational GOCI atmospheric correction method over land surfaces, which is rather different from the one developed for ocean surface. The GOCI atmospheric correction method basically reduces gases absorption and Rayleigh and aerosol scatterings and to derive surface reflectance from at-sensor radiance. We use the 6S radiative transfer model that requires several input parameters to calculate surface reflectance. In the sensitivity analysis, aerosol optical thickness was the most influential element among other input parameters including atmospheric model, terrain elevation, and aerosol type. To account for the highly variable nature of aerosol within the GOCI target area in northeast Asia, we generate the spatio-temporal aerosol maps using AERONET data for the aerosol correction. For a fast processing, the GOCI atmospheric correction method uses the pre-calculated look up table that directly converts at-sensor radiance to surface reflectance. The atmospheric correction method was validated by comparing with in-situ spectral measurements and MODIS reflectance products. The GOCI surface reflectance showed very similar magnitude and temporal patterns with the in-situ measurements and the MODIS reflectance. The GOCI surface reflectance was slightly higher than the in-situ measurement and MODIS reflectance by 0.01 to 0.06, which might be due to the different viewing angles. Anisotropic effect in the GOCI hourly reflectance needs to be further normalized during the following cloud-free compositing.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.67-71
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• 2016

The surface reflectance is essential to retrieval various indicators related land properties such as vegetation index, albedo and etc. In this study, we estimated surface reflectance using Himawari-8 / Advanced Himawari Imager (AHI) channel data. In order to estimate surface reflectance from Top of Atmosphere (TOA) reflectance, the atmospheric correction is necessary because all of the TOA reflectance from optical sensor is affected by gas molecules and aerosol in the atmosphere. We used Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) Radiative Transfer Model (RTM) to correct atmospheric effect, and Look-Up Table (LUT) to shorten the calculation time. We verified through comparison Himawri-8 / AHI surface reflectance and Proba-V S1 products. As a result, bias and Root Mean Square Error (RMSE) are calculated about -0.02 and 0.05.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.487-499
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• 2009

Atmospheric correction of satellite measurements is a major step to estimate accurate surface reflectance of solar spectrum channels. In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model used to retrieve surface reflectance from MODIS (MODerate resolution Imaging Spectrometer) top of atmosphere (TOA) reflectance. It is fast and simple atmospheric correction method, so it uses for work site operation in various satellite. This study attempts a test of accuracy of SMAC through a sensitivity test to detected error sources and to improve accuracy of surface reflectance using SMAC. The results of SMAC as compared with MODIS surface reflectance (MOD09) was represented that low accuracy ($R^2\;=\;0.6196$, Root Means Square Error (RMSE) = 0.00031, bias = - 0.0859). Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Among the input parameters, Aerosol Optical Depth (AOD) is the most influence input parameter. In order to modify AOD term in SMAC code, Stepwise multiple regression was performed with testing and remove variable in three stages with independent variables of AOD at 550nm, solar zenith angle, viewing zenith angle. Surface reflectance estimation by using Newly proposed AOD term in the study showed that improve accuracy ($R^2\;=\;0.827$, RMSE = 0.00672, bias = - 0.000762).

• Korean Journal of Remote Sensing
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• v.14 no.3
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• pp.262-276
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• 1998

The paper first describes the atmospheric correction algorithm for the Ocean Color and Temperature Scanner (OCTS) visible band data used at Earth Observation Center (EOC) of National Space Development Agenrr of japan (NASDA). It uses 10 candidate aerosol models including "Asian dust model" introduced in consideration of the unique feature of aerosols over the east Asian waters. Based on the observations at 670 and 865 nm bands where the reflectance of the water body can be discarded, the algorithm selects a pair of aerosol models that accounts best for the observed spectral reflectances to synthesize the aerosol reflectance in other bands. The paper also evaluates the performance of the algorithm by comparing the satellite estimates of water-leaving radiance and chlorophyll-a concentration with selected buoy- and ship-measured data. In comparison with the old CZCS-type atmospheric correction algorithm where the aerosol reflectance is assumed to be spectrally independent, the OCTS algorithm records factor 2-3 less error in estimating the normalized water-leaving radiances. In terms of chlorophyll-a concentration estimation, however, the accuracy stays very similar compared to that of the CZCS-type algorithm. This is considered to be due to the nature of in-water algorithm which relies on spectral ratio of water-leaving radiances.