To predict the soil texture fractions, 115 profiles were identified based on the Latin hypercube sampling technique, the horizons were sampled, and the clay, sand, and silt contents (in percentages) of soil samples were measured. Then equal-area quadratic spline depth functions were used to derive clay, sand, and silt contents at five standard soil depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). Auxiliary variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial neural network (ANN), regression tree (RT), and neuro-fuzzy (ANFIS) models were used to make a correlation between soil data (clay, sand, and silt) and auxiliary variables. The results of this study showed that the ANFIS model was more accurate in the prediction of the three parameters of clay, silt, and sand than ANN and RT. Moreover, the ability of ANFIS model to estimate the soil texture fractions in the surface layers was higher than the lower layers. The mean coefficient of determination (R²) values calculated by 10-fold cross validation suggested the higher prediction performance in the upper depth intervals and higher prediction error in the lower depth intervals (e.g., R² = 0.91, concordance correlation coefficient (CCC) = 0.90, RMSE = 4.00 g kg⁻¹ for sand of 0–5 cm depth, and R² = 0.68, CCC = 0.60, RMSE = 8.03 g kg⁻¹ for 60–100 cm depth). The results also showed that the most important auxiliary variables are spectrometric data, multi-resolution, valley-bottom flatness index and wetness index. Overall, it is recommended to use ANFIS models for the digital mapping of soil texture fractions in other arid regions of Iran.

Forest growing stem volume (GSV) is regarded as one of the most important parameters for the quality evaluation and dynamic monitoring of forest resources. The accuracy of mapping forest GSV is highly related to the employed models and involved remote sensing features, and the criteria of feature evaluation severely affect the performance of the employed models. However, due to the linear or nonlinear relationships between remote sensing features and GSV, widely used evaluation criteria inadequately express the complex sensitivity between forest GSV and spectral features, especially the saturation levels of features in a planted forest. In this study, novel feature evaluation criteria were constructed based on the Pearson correlations and optical saturation levels of the alternative remote sensing features extracted from two common optical remote sensing image sets (GF-1 and Sentinel-2). Initially, the spectral saturation level of each feature was quantified using the kriging spherical model and the quadratic model. Then, optimal feature sets were obtained with the proposed criteria and the linear stepwise regression model. Finally, four widely used machine learning models—support vector machine (SVM), multiple linear stepwise regression (MLR), random forest (RF) and K-neighborhood (KNN)—were employed to map forest GSV in a planted Chinese fir forest. The results showed that the proposed feature evaluation criteria could effectively improve the accuracy of estimating forest GSV and that the systematic distribution of errors between the predicted and ground measurements in the range of forest GSV was less than 300 m³/hm². After using the proposed feature evaluation criteria, the highest accuracy of mapping GSV was obtained with the RF model for GF-1 images (R² = 0.49, rRMSE = 28.67%) and the SVM model for Sentinel-2 images (R² = 0.52, rRMSE = 26.65%), and the decreased rRMSE values ranged from 1.1 to 6.2 for GF-1 images (28.67% to 33.08%) and from 2.3 to 6.8 for Sentinel-2 images (26.85% to 33.28%). It was concluded that the sensitivity of the optimal feature set and the accuracy of the estimated GSV could be improved using the proposed evaluation criteria (less than 300 m³/hm²). However, these criteria were barely able to improve mapping accuracy for a forest with a high GSV (larger than 300 m³/hm²).

Wheat is considered the most important crop in Iran; however, not all of the land in Iran is equally suitable for growing wheat. This study aimed to apply a spatial model for land suitability assessment integrated with geographic information system (GIS) techniques for the wheat crop. Climate and Soil parameters were recognized as factors affecting land suitability for wheat crop in the study area. Three indices were used in assessing land suitability, soil fertility, chemical and physical quality indices. The results of the proposed model (LSI) were compared with the square root and Storie methods. The results showed that most of the units fall within the Moderate suitable class (S2) and the Marginally suitable class (S3) which together represent 88.66% of the total area. About 7.08% of the study area was High suitable and About 2.37% of the study area was unsuitable for wheat crops and those areas correspond to the adverse physical and chemical properties of the soil. The comparison of the results of the three approaches used showed that the present model has a Moderate level of agreement with the square root method (0.516) and showed that the present model has a low level of agreement with the Storie method (0.243). Comparing the results showed that the story model has a high agreement with the square root method (0.884). In the current model, the use of different indicators allows for obtaining results that seem to be more consistent with the current conditions of the region.