Using Visible and near Infrared Spectroscopy to Estimate Carbonates and Gypsum in Soils in Arid and Subhumid Regions of Isfahan, Iran

Abstract

Soils in arid and semi-arid regions are strongly affected by the accumulation of carbonates, gypsum and other, more soluble, salts.Carbonates and gypsum both have a considerable influence on soil properties, especially the chemical properties of the soil solution. The development of reliable, fast and inexpensive methods to quantify the amounts of carbonates and gypsum in soil is therefore important. Visible and near infrared (vis-NIR) spectroscopy is a non-destructive, rapid and cheap method for measuring several soil properties simultaneously. However, research on vis-NIR spectroscopy in quantifying carbonates and gypsum is limited. Therefore, this study evaluated the efficiency of vis-NIR spectroscopy in quantifying carbonates and gypsum in surface soils using partial least-squares regression (PLSR) compared with standard laboratory methods and compared PLSR with a feature-specific method using continuum removal (CR). Carbonates and gypsum in a total of 251 sieved and air-dried topsoil samples from Isfahan Province in central Iran were measured by standard laboratory methods and vis-NIR spectroscopy (350-2500 nm wavelength range). In parallel, PLSR and the feature-specific method based on CR spectra were used to predict carbonates and gypsum. The PLSR model efficiency (E) for carbonates and gypsum in the validation set was 0.52 and 0.80, respectively. The PLSR model resulted in better predictions than the feature-specific method for both soil properties. Because of the unique absorption features of gypsum, which did not overlap with other soil properties, predictions of gypsum resulted in higher E values and lower errors than predictions of carbonates.

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

“…Predictions for calcium carbonate are less accurate than predictions for gypsum in this area. Similar results were found on gypsiferous soils in Iran [51]. The rest of the compounds considered in this study-quartz, feldspars, etcetera-were even more variable or their concentrations were negligible.…”

“…Changes in reflectance, color, or brightness can be indicators of these changes [27,49,50]. Khayamim et al [51] used VIS/NIR spectroscopy and PLSR to predict soil gypsum content with high accuracy (R 2 = 0.86). More recently, quantitative estimates of gypsum content have been established from hyperspectral remote sensing [52].…”

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

“…Laboratory spectral measurements of this sample after air drying for several days result in reflectance level up to 70% in the VNIR, close to reference halite spectral characteristics. The field spectrum of the second crust type (sample P65, Figure 2b) is characterized by strong absorption features in the SWIR at ~1750 nm and ~2200 nm as well as the unique triplet absorption around 1500 nm, which are characteristic for gypsum spectral properties [101,103,104]. The XRD analysis also confirms the high gypsum content (83%) mixed with some halite.…”

Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data Treated by Fractional Derivative

“…In the aspect of quantitative research on hyperspectral data, partial least squares regression (PLSR) has been proved as a robust and reliable mathematical tool because of its advantage of solving colinearity problems [45][46][47][48][49][50]. Thus, in this research, PLSR was applied for model calibrations and predictions of soil salt content based on the hyperspectral data treated by G-L fractional derivative.…”