Visible Near-Infrared Reflectance Spectrocopy for Geospatial Mapping of Soil Organic Matter

Soil Science ◽  
2009 ◽  
Vol 174 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Sanjay Lamsal
2011 ◽  
Vol 62 (12) ◽  
pp. 1078 ◽  
Author(s):  
X. Li ◽  
R. L. Ison ◽  
R. C. Kellaway ◽  
C. Stimson ◽  
G. Annison ◽  
...  

A range of annual legume genotypes comprising one line of Trifolium subterraneum, four lines of T. michelianum, 11 of T. resupinatum var. resupinatum, and one line of T. resupinatum var. majus were grown in glasshouses under temperature regimes of 10−15°C and 16−21°C. Dry matter (DM) weights of stem, leaf, and flower tissues were measured when plants had six nodes, at first flower appearance, and at senescence. All samples were scanned by near-infrared reflectance spectroscopy (NIRS). One-third of the samples, covering the range of spectral characteristics, were analysed for in vitro digestible organic matter (DOMD), organic matter, crude protein (CP), neutral detergent fibre (NDF), lignin, cellulose, and the hemicellulosic polysaccharide monomers arabinose, xylose, mannose, galactose, and rhamnose. These data were used to develop calibration equations from which the composition of the remaining samples was predicted by NIRS. The higher temperature resulted in plants reaching respective phenological stages earlier, but did not affect either DM yields of total plant, stem, leaf, and petiole tissues or the proportions of each fraction. In vitro DOMD and arabinose and galactose levels decreased, while lignin, cellulose, NDF, xylose, mannose, and rhamnose levels increased with advancing maturity. In vitro DOMD was positively associated with contents of CP, arabinose, galactose, and the arabinose/xylose ratio and was negatively associated with contents of lignin, cellulose, NDF, xylose, mannose, and rhamnose. Lignin contents were highly correlated with levels of both xylose and mannose. Stems were more digestible than leaves in subterranean clover and T. resupinatum var. majus. The study also demonstrated that NIRS can be used routinely as a quick, inexpensive, and reliable laboratory technique to predict feed components of annual Trifolium legumes.


2020 ◽  
pp. 1-12
Author(s):  
Lingyun Peng ◽  
Hao Cheng ◽  
Liang-Jie Wang ◽  
Dianzhen Zhu

Soil organic matter and soil particle composition play extremely important roles in soil fertility, environmental protection, and sustainable agricultural development. Visible – near-infrared reflectance (Vis–NIR) spectroscopy is a rapid, effective, and low-cost analytical method to predict soil properties. In this study, laboratory Vis–NIR spectroscopy data were used to compare the differences among partial least squares regression (PLSR), artificial neural network (ANN) and multivariate adaptive regression splines (MARSplines) based on fuzzy c-means spectral clustering and expert knowledge classification methods for soil prediction. The results showed that (1) the sand content (R2 = 0.69–0.77) had the best prediction, followed by the silt (R2 = 0.56–0.71) and organic matter (R2 = 0.54–0.69) contents, whereas the clay content (R2 = 0.29–0.65) had the poorest prediction, (2) the performance of the models followed the order of PLSR > ANN > MARSplines, and (3) the accuracies of the organic matter and sand contents were higher when applying expert knowledge classification, whereas the prediction of the clay and silt contents was better when applying spectral clustering. However, the overall accuracy of the spectral clustering method was slightly better than that of expert classification. Our findings showed that the spectral cluster-based models produced effective and interpretable prediction results for estimating soil properties. Therefore, this approach should be considered when dealing with large and heterogeneous soil samples.


2020 ◽  
Vol 64 (2) ◽  
pp. 59-69
Author(s):  
Francisco Javier Ancin-Murguzur ◽  
Antony G. Brown ◽  
Charlotte Clarke ◽  
Per Sjøgren ◽  
John Inge Svendsen ◽  
...  

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