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Visible-near infrared reflectance spectroscopy for assessment of soil properties in gypseous and calcareous soils | ||
| مهندسی و مدیریت آبخیز | ||
| Article 4, Volume 6, Issue 2, August 2014, Pages 125-138 PDF (1.81 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijwmse.2014.101721 | ||
| Authors | ||
| Akbar Hassani1; Hosseinali Bahrami* 2; Aliakbar Noroozi3; Shahin Oustan4 | ||
| 1PhD Student, Faculty of Agriculture, Tarbiat Modares University, Iran | ||
| 2Associate Professor, Faculty of Agriculture, Tarbiat Modares University, Iran | ||
| 3Assistant Professor, Soil Conservation and Watershed Management Research Institute, Iran | ||
| 4Associate Professor, Faculty of Humanities and Social Science, Tabriz University, Iran | ||
| Abstract | ||
| Reflectance spectroscopy can be used to study agricultural and environmental aspect of soil that are sensitive to soil organic and inorganic compounds. Despite the extensive studies in the field of visible-near infrared reflectance spectroscopy, there are rare researches in gypseous and calcareous soils. The objective of this study was to obtain a model that can predict chemical properties of gypseous soils via reflectance spectroscopy methods. Soil samples were collected from 102 locations in five different provinces in 0-30 cm of depth. Some chemical properties of soils, such amount of gypsum, equivalent calcite, cation exchange capacity, pH, EC, exchangeable calcium, magnesium, sodium and potassium and amount of silt, clay and sand were measured by standard methods in the laboratory. Air-dried soil samples were scanned at one nm resolution from 350 to 2500 nm, and calibrations between properties and reflectance spectra were developed using cross-validation under Partial Least Squares Regression (PLSR) and Boosted Regression Trees (BRT). Raw reflectance and first derivative reflectance data were used separately and combined for all samples in the data set. Data were additionally divided into two random subsets of 70 and 30 percent of the full data, which were each used for calibration and validation. Strongest correlations were obtained with gypsum, equivalent calcium carbonate, cation exchange capacity, exchangeable Ca and Mg, organic matter, sand and clay contents. Overall, BRT provided better predictions when under cross-validation. However, PLSR and BRT results were comparable in terms of prediction accuracy when using separate data sets for calibration and validation. In conclusion, VNIR spectroscopy was variably successful in estimating soil properties and showed its potential for substituting laboratory analyses or providing inexpensive co-variable data in environmental studies. | ||
| Keywords | ||
| Calibration; BRT; PLSR; Soil reflectance; Validation | ||
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