• 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.34 no.6_3
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• pp.1369-1382
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• 2018

As the number of multispectral satellites increases, it is expected that it will be possible to acquire and use images for periodically. However, there is a problem of data discrepancy due to different overpass time, period and spatial resolution. In particular, the difference in band bandwidths became different reflectance even for images taken at the same time and affect uncertainty in the analysis of vegetation activity such as vegetation index. The purpose of this study is to estimate the band adjustment factor according to the difference of bandwidth with other multispectral satellites for the application of KOMPSAT-3 satellite in agriculture field. The Spectral band adjustment factor (SBAF) were calculated using the hyperspectral satellite images acquired in the desert area. As a result of applying SBAF to the main crop area, the vegetation index showed a high agreement rate of relative percentage difference within 3% except for the Hapcheon area where the zenith angle was 25. For the estimation of SBAF, this study used only one set of images, which did not consider season and solar zenith angle of SBAF variation. Therefore, long-term analysis is necessary to solve SBAF uncertainty in the future.

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.413-420
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• 2012

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.