RESUMEN
Precise prediction of soil salinity using visible, and near-infrared (vis-NIR) spectroscopy is crucial for ensuring food security and effective environmental management. This paper focuses on the precise prediction of soil salinity utilizing visible and near-infrared (vis-NIR) spectroscopy, a critical factor for food security and effective environmental management. The objective is to utilize vis-NIR spectra alongside a multiple regression model (MLR) and a random forest (RF) modeling approach to predict soil salinity across various land use types, such as farmlands, bare lands, and rangelands accurately. To this end, we selected 150 sampling points representatives of these diverse land uses. At each point, we collected soil samples to measure the soil salinity (ECe) and employed a portable spectrometer to capture the spectral reflectance across the full wavelength range of 400 to 2400 nm. The methodology involved using both individual spectral reflectance values and combinations of reflectance values from different wavelengths as input variables for developing the MLR and RF models. The results indicated that the RF model (RMSE = 4.85 dS m-1, R2 = 0.87, and RPD = 3.15), utilizing combined factors as input variables, outperformed others. Furthermore, our analysis across different land uses revealed that models incorporating combined input variables yielded significantly better results, particularly for farmlands and rangelands. This study underscores the potential of combining vis-NIR spectroscopy with advanced modeling techniques to enhance the accuracy of soil salinity predictions, thereby supporting more informed agricultural and environmental management decisions.
Asunto(s)
Salinidad , Suelo , Espectroscopía Infrarroja Corta , Suelo/química , Espectroscopía Infrarroja Corta/métodos , Análisis de Regresión , Agricultura/métodos , Monitoreo del Ambiente/métodos , Análisis Espectral/métodos , Bosques AleatoriosRESUMEN
Modeling agriculture land suitability at a regional scale plays an important role in designing the best sustainable management systems. The aim of this study was to derive a land suitability map for wheat farming by combining the Geostatistics and analytic hierarchy (AHP)-Fuzzy algorithm in geographic information system (GIS) in calcareous and saline-sodic soils, southern Iran. The local expert's opinions were used to make a decision on the weighting of climate, terrain, and soil data by applying an AHP method. The input data were transformed to a fuzzy-set data. The Spherical and Gaussian semi-variogram models had the best performance for fitting the soil parameters. The results revealed that soil texture (w = 0.207), pH (w = 0.121), slope (w = 0.120), electrical conductivity (w = 0.113), and exchangeable sodium percentage (w = 0.111) had the highest specific weighting for wheat production, respectively. The land suitability map indicated that 25.65% (48306.6 ha) of the studied area was for highly suitable, 38.2% (71939.7 ha) was moderately suitable, and 27.63% (52017.2 ha) was marginally suitable. Only 8.52% (16042.4 ha) of the studied area was not suitable for wheat farming. In conclusion, a combination of AHP, Fuzzy, and GIS could be a potential approach for site-specific soil management, land-use planning, and protection of the environment.