• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.2
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• pp.154-160
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• 2017

This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible-NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The $R^2$ value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.289-305
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• 2004

Atmospheric correction of Landsat Visible and Near Infrared imagery (VIS/NIR) over aquatic environment is more demanding than over land because the signal from the water column is small and it carries immense information about biogeochemical variables in the ocean. This paper introduces two methods, a modified dark-pixel substraction technique (path--extraction) and our spectral shape matching method (SSMM), for the correction of the atmospheric effects in the Landsat VIS/NIR imagery in relation to the retrieval of meaningful information about the ocean color, especially from Case-2 waters (Morel and Prieur, 1977) around Korean peninsula. The results of these methods are compared with the classical atmospheric correction approaches based on the 6S radiative transfer model and standard SeaWiFS atmospheric algorithm. The atmospheric correction scheme using 6S radiative transfer code assumes a standard atmosphere with constant aerosol loading and a uniform, Lambertian surface, while the path-extraction assumes that the total radiance (L/sub TOA/) of a pixel of the black ocean (referred by Antoine and Morel, 1999) in a given image is considered as the path signal, which remains constant over, at least, the sub scene of Landsat VIS/NIR imagery. The assumption of SSMM is nearly similar, but it extracts the path signal from the L/sub TOA/ by matching-up the in-situ data of water-leaving radiance, for typical clear and turbid waters, and extrapolate it to be the spatially homogeneous contribution of the scattered signal after complex interaction of light with atmospheric aerosols and Raleigh particles, and direct reflection of light on the sea surface. The overall shape and magnitude of radiance or reflectance spectra of the atmospherically corrected Landsat VIS/NIR imagery by SSMM appears to have good agreement with the in-situ spectra collected for clear and turbid waters, while path-extraction over turbid waters though often reproduces in-situ spectra, but yields significant errors for clear waters due to the invalid assumption of zero water-leaving radiance for the black ocean pixels. Because of the standard atmosphere with constant aerosols and models adopted in 6S radiative transfer code, a large error is possible between the retrieved and in-situ spectra. The efficiency of spectral shape matching has also been explored, using SeaWiFS imagery for turbid waters and compared with that of the standard SeaWiFS atmospheric correction algorithm, which falls in highly turbid waters, due to the assumption that values of water-leaving radiance in the two NIR bands are negligible to enable retrieval of aerosol reflectance in the correction of ocean color imagery. Validation suggests that accurate the retrieval of water-leaving radiance is not feasible with the invalid assumption of the classical algorithms, but is feasible with SSMM.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.