研究目的
To assess qualitative and quantitative methods on soil classification, based on model profiles, by evaluating soils in different environments in the Roraima state, Brazil.
研究成果
Descriptive spectral analysis effectively identified soil attributes and supported classification, while quantitative methods were insufficient. Spectroradiometers and colorimeters provided more objective soil color measurements than human experts. Future studies should integrate descriptive and quantitative approaches in online tools for soil classification.
研究不足
Quantitative analyses (OSACA and cluster analysis) could not correctly group similar soil classes and need improvement to extract full spectral variability. The study is based on a specific case in Roraima, Brazil, and may not be generalizable. Subjectivity in human expert assessments affects color determination. Future work should focus on developing online tools and improving mathematical techniques.
1:Experimental Design and Method Selection:
The study used descriptive and quantitative spectral analyses adapted from the MIRS method, involving interpretation of visible-near infrared spectra (350–2500 nm) for soil classification. Principal component analysis (PCA) and clustering methods (OSACA and hierarchical cluster analysis) were employed for quantitative evaluation.
2:Sample Selection and Data Sources:
16 soil profiles (109 samples) from Roraima state, Brazil, were collected during a fieldtrip. Samples were analyzed for particle size distribution, chemical attributes, and spectral measurements.
3:List of Experimental Equipment and Materials:
Spectroradiometer (FieldSpec Pro 3, Malvern Panalytical), colorimeter (Konica Minolta CR-300), petri dishes, white plate (Spectralon), halogen lamps, sieves (2-mm mesh), and laboratory equipment for chemical analyses (e.g., atomic absorption spectrophotometer, flame photometer).
4:Experimental Procedures and Operational Workflow:
Soil samples were air-dried, sieved, and placed in petri dishes for spectral reflectance measurements. Spectral data were acquired with specific setup (e.g., 50 readings per sample, average spectrum generated). Descriptive analysis involved evaluating spectral curves for intensity, shape, and features. Quantitative analyses included PCA transformation and clustering. Soil color was determined using spectroradiometer, colorimeter, and visual expert assessment.
5:Data Analysis Methods:
Statistical analyses included coefficient of determination (R2) and root mean square error (RMSE) for color comparisons, and clustering algorithms in R software for profile classification.
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