scholarly journals BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

2016 ◽  
Vol III-7 ◽  
pp. 3-8 ◽  
Author(s):  
M. Matsuoka ◽  
M. Takagi ◽  
S. Akatsuka ◽  
R. Honda ◽  
A. Nonomura ◽  
...  
Keyword(s):  
Surface Properties ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Near Infrared ◽  
Temporal Frequency ◽  
Atmospheric Correction ◽  
Time Of Day ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

scholarly journals BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

2016 ◽  
Vol III-7 ◽  
pp. 3-8
Author(s):  
M. Matsuoka ◽  
M. Takagi ◽  
S. Akatsuka ◽  
R. Honda ◽  
A. Nonomura ◽  
...  
Keyword(s):  
Surface Properties ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Near Infrared ◽  
Temporal Frequency ◽  
Atmospheric Correction ◽  
Time Of Day ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

scholarly journals Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2463
Author(s):  
Qing Dong ◽  
Qianqian Xu ◽  
Jiandong Wu ◽  
Beijiu Cheng ◽  
Haiyang Jiang
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Near Infrared Reflectance ◽  
Single Grain ◽  
Single Seeds

Near infrared reflectance spectroscopy (NIRS) and reference data were used to determine the amylose contents of single maize seeds to enable rapid, effective selection of individual seeds with desired traits. To predict the amylose contents of a single seed, a total of 1069 (865 as calibration set, 204 as validation set) single seeds representing 120 maize varieties were analyzed using chemical methods and performed calibration and external validation of the 150 single seeds set in parallel. Compared to various spectral pretreatments, the regression of partial least squares (PLS) with mathematical treatment of Harmonization showed the final optimization. The single-seed amylose contents showed the root mean square error of calibration (RMSEC) of 2.899, coefficient of determination for calibration (R2) of 0.902, and root mean square error of validation (RMSEV) of 2.948. In external validations, the coefficient of determination in cross-validation (r2), root mean square error of the prediction (RMSEP) and ratio of the standard deviation to SEP (RPD) were 0.892, 2.975 and 3.086 in the range of 20–30%, respectively. Therefore, NIRS will be helpful to breeders for determining the amylose contents of single-grain maize.

Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid and Non-Destructive Tool for Fish Fraud Control: Monitoring Source, Condition and Nutritional Value of Five Common Whitefish Species

2020 ◽  
Author(s):  
Diogo B Gonçalves ◽  
Carla S P Santos ◽  
Teresa Pinho ◽  
Rafael Queirós ◽  
Pedro D Vaz ◽  
...  
Keyword(s):  
Near Infrared ◽  
Atlantic Cod ◽  
Cost Effective ◽  
Reflectance Spectroscopy ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Abstract Fish fraud is a problematic issue for the industry that to be properly addressed requires the use of accurate, rapid and cost-effective tools. In this work, near infrared reflectance spectroscopy (NIRS) was used to predict nutritional values (protein, lipids and moisture) as well as to discriminate between source (farmed vs. wild fish) and condition (fresh, defrosted or frozen fish). Five whitefish species consisting of Alaskan pollock (Gadus chalcogrammu), Atlantic cod (Gadus morhua), European plaice (Pleuronectes platessa), Common sole (Solea solea) and Turbot (Psetta maxima), including farmed, wild, fresh and frozen ones, were scanned by a low-cost handheld near infrared reflectance spectrometer with a spectral range between 900 nm and 1700 nm. Several machine learning algorithms were explored for both regression and classification tasks, achieving precisions and coefficient of determination higher than 88% and 0.78, respectively. Principal component analysis (PCA) was used to cluster samples according to classes where good linear discriminations were denoted. Loadings from PCA reveal bands at 1150, 1200 and 1400 nm as the most discriminative spectral regions regarding classification of both source and condition, suggesting the absorbance of OH, CH, CH2 and CH3 groups as the most important ones. This study shows the use of NIRS and both linear and non-linear learners as a suitable strategy to address the fish fraud problematic and fish quality control.

Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy

2006 ◽  
Vol 82 (1) ◽  
pp. 111-116 ◽  
Author(s):  
N. Barlocco ◽  
A. Vadell ◽  
F. Ballesteros ◽  
G. Galietta ◽  
D. Cozzolino
Keyword(s):  
Shear Force ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Intramuscular Fat ◽  
Coefficient Of Determination ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Calibration Models ◽  
Intact Muscle

AbstractPartial least-squares (PLS) models based on visible (Vis) and near infrared reflectance (NIR) spectroscopy data were explored to predict intramuscular fat (IMF), moisture and Warner Bratzler shear force (WBSF) in pork muscles (m. longissimus thoracis) using two sample presentations, namely intact and homogenized. Samples were scanned using a NIR monochromator instrument (NIRSystems 6500, 400 to 2500 nm). Due to the limited number of samples available, calibration models were developed and evaluated using full cross validation. The PLS calibration models developed using homogenized samples and raw spectra yielded a coefficient of determination in calibration (R2) and standard error of cross validation (SECV) for IMF (R2=0·87; SECV=1·8 g/kg), for moisture (R2=0·90; SECV=1·1 g/kg) and for WBSF (R2=0·38; SECV=9·0 N/cm). Intact muscle presentation gave poorer PLS calibration models for IMF and moisture (R2<0·70), however moderate good correlation was found for WBSF (R2=0·64; SECV=8·5 N/cm). Although few samples were used, the results showed the potential of Vis-NIR to predict moisture and IMF using homogenized pork muscles and WBSF in intact samples.

Application of near Infrared Reflectance Spectroscopy for the Analysis of Organic C, Total N and pH in Soils of Uruguay

2002 ◽  
Vol 10 (3) ◽  
pp. 215-221 ◽  
Author(s):  
A. Morón ◽  
D. Cozzolino
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Calibration Coefficient ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Total N ◽  
Organic C ◽  
Silver Spring ◽  
Validation Set

Near infrared (NIR) reflectance spectroscopy was used to analyse samples ( n = 332) from different soils from Uruguay (South America) for organic carbon (OC), total nitrogen (N) and pH. One set ( n = 200) of samples randomly selected was used to develop the NIR calibrations while the remaining ( n = 132) samples were used as the validation set. The samples were scanned in a small circular cup in reflectance mode (400–2500 nm), using a Foss NIRSystems 6500 (Silver Spring, MD, USA). Modified partial least squares (MPLS) was used to produce the calibration models and cross-validation was used to avoid collinearity effects among variables. Three mathematical treatments and four scatter corrections were also applied. The calibration coefficient of determination ( R2CAL) and the standard error in cross-validation ( SECV) were 0.94 ( SECV: 1.9) for OC; 0.91 ( SECV: 0.19) for total N in g kg−1 and 0.93 ( SECV: 0.18) for pH, respectively. The simple correlation coefficient of validation ( rVAL) and the standard errors of prediction ( SEP) were 0.74 and 5; 0.73 and 0.4; 0.84 and 0.28 for OC, total N and pH, respectively.

Determination of Optical Parameters and Moisture Content of Wood with Visible–Near Infrared Spectroscopy

2016 ◽  
Vol 24 (6) ◽  
pp. 571-585 ◽  
Author(s):  
Ataollah Haddadi ◽  
Guillaume Hans ◽  
Brigitte Leblon ◽  
Zarin Pirouz ◽  
Satoru Tsuchikawa ◽  
...  
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Black Spruce ◽  
Scattering Coefficient ◽  
Coefficient Of Determination ◽  
Optical Parameters ◽  
Quaking Aspen ◽  
Near Infrared Reflectance ◽  
Scattering Coefficients

We used the Kubelka-Munk theory equations for calculating the absorption coefficient (Kλ), the scattering coefficient ( Sλ), the transport absorption (σλa), the reduced scattering coefficient [σλs(1 – g)] and the penetration depth (δλ) from visible-near infrared reflectance spectra acquired over thin samples of quaking aspen and black spruce conditioned at three different moisture levels. The computed absorption and scattering coefficients varied from 0.1 mm−1 to 4.0 mm−1 and from 5.5 mm−1 to 10.0 mm−1, respectively. The absorption coefficients varied according to the absorption band, but the scattering coefficients decreased slowly towards high wavelengths. The sample moisture content was then estimated using the partial least squares (PLS) regression method from the Kλ and/or Sλ spectra, and the resulting PLS models were compared to those obtained with raw and transformed [multiplicative scatter corrected (MSC), first and second derivative] absorption spectra. The best PLS models for black spruce, quaking aspen and both species were obtained when only the 800–1800 nm range was used with the raw or MSC spectra. They led to a root mean square error of cross validation ( RMSECV) of 1.40%, 1.09% and 1.23%, respectively, and to a coefficient of determination ( R2CV) higher than 0.94. We also found that the Kλ spectra between 800 nm and 1800 nm can provide PLS models having an acceptable accuracy for moisture content estimation ( R2CV = 0.83 and RMSECV = 2.32%), regardless of the species.

scholarly journals Burn Severity and Post-Fire Land Surface Albedo Relationship in Mediterranean Forest Ecosystems

2019 ◽  
Vol 11 (19) ◽  
pp. 2309 ◽  
Author(s):  
Carmen Quintano ◽  
Alfonso Fernandez-Manso ◽  
Elena Marcos ◽  
Leonor Calvo
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Near Infrared ◽  
Statistical Significance ◽  
Remotely Sensed ◽  
Burn Severity ◽  
Coefficient Of Determination ◽  
Future Research ◽  
Severity Levels ◽  
Fire Scene

Our study explores the relationship between land surface albedo (LSA) changes and burn severity, checking whether the LSA is an indicator of burn severity, in a large forest fire (117.75 km2, Spain). The LSA was obtained from Landsat data. In particular, we used an immediately-after-fire scene, a year-after-fire scene and a pre-fire one. The burn severity (three levels) was assessed in 111 field plots by using the Composite Burn Index (CBI). The potentiality of remotely sensed LSA as an indicator for the burn severity was tested by a one-way analysis of variance, correlation analysis and regression models. Specifically, we considered the total shortwave, visible, and near-infrared LSA. Immediately after the fire, we observed a decrease in the LSA for all burn severity levels (up to 0.631). A small increase in the LSA was found (up to 0.0292) a year after the fire. The maximum adjusted coefficient of determination (R2adj) of the linear regression model between the immediately post-fire LSA image and the CBI values was approximately 67%. Fisher’s least significance difference test showed that two burn severity levels could be discriminated by the immediately post-fire LSA image. Our results demonstrate that the magnitude of the changes in the LSA is related to the burn severity with a statistical significance, suggesting the potentiality of immediately-after-fire remotely sensed LSA for estimating the burn severity as an alternative to other satellite-based methods. However, the persistency of these changes in time should be evaluated in future research.

New approach to improve the calibration of main fatty acids by near-infrared reflectance spectroscopy in ruminant meat

2014 ◽  
Vol 54 (10) ◽  
pp. 1848 ◽  
Author(s):  
B. P. Mourot ◽  
D. Gruffat ◽  
D. Durand ◽  
G. Chesneau ◽  
S. Prache ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Linoleic Acid ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Calibration Model ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

This study aims to investigate alternative near-infrared reflectance spectroscopy (NIRS) strategies for predicting beef polyunsaturated fatty acids (PUFA) composition, which have a great nutritional interest, and are actually poorly predicted by NIRS. We compared the results of NIRS models for predicting fatty acids (FA) of beef meat by using two databases: a beef database including 143 beef samples, and a ruminant database including 76 lamb and 143 beef samples. For all the FA, particularly for PUFA, the coefficient of determination of cross-validation (R2CV) and the residual predictive deviation (RPD) of models increased when the ruminant muscle samples database was used instead of the beef muscle database. The R2CV values for the linoleic acid, total conjugated linoleic acid and total PUFA increased from 0.44, 0.79 and 0.59 to 0.68, 0.9, 0.8, respectively, and RPD values for these FA increased from 1.33, 2.14, 1.54 to 1.76, 3.11 and 2.24, respectively. RPD above 2.5 indicates calibration model is considered as acceptable for analytical purposes. The use of a universal equation for ruminant meats to predict FA composition seems to be an encouraging strategy.

Nondestructive NIR Reflectance Spectroscopic Method for Rapid Fatty Acid Analysis of Peanut Seeds

2011 ◽  
Vol 38 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Jaya Sundaram ◽  
Chari V. Kandala ◽  
Christopher L. Butts ◽  
Charles Y. Chen ◽  
Victor Sobolev
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Methyl Esters ◽  
Spectroscopic Method ◽  
Nondestructive Method ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance

ABSTRACT Near Infrared Reflectance Spectroscopy (NIRS) was used to rapidly and nondestructively analyze the fatty acid concentration present in peanut seeds samples. Absorbance spectra were collected in the wavelength range from 400 nm to 2500 nm using NIRS. The oleic, linoleic and palmitic fatty acids were converted to their corresponding methyl esters and their concentrations were measured using a gas chromatograph (GC). Partial least square (PLS) analysis was performed on a calibration set, and models were developed for prediction of fatty acid concentrations. The best model was selected based on the coefficient of determination (R2), Root Mean Square Error of Prediction, residual percent deviation (RPD) and correlation coefficient percentage between the gas chromatography measured values and the NIR predicted values. The NIR reflectance model developed yielded RPD values of three and above for prediction of the three fatty acids, indicating that this nondestructive method would be suitable for fatty acid predictions in peanut seeds.

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