JWSS - Journal of Water and Soil Science

Soil quality is the permanent soil ability to function as a live system within ecosystem under different land uses. Investigating the impact of land use type on soil quality indicators could help to distinguish sustainable managements and therefore, to inhibit soil degradation. In order to evaluate the effect of different land uses on soil quality indicators, a research based on a randomized complete design in Rabor region, Kerman Province, Iran, was conducted. A total of 104 samples were taken from the soil surface (0-15 cm) of four land uses including: pasture (28 samples), forest (25 samples), agronomy (27 samples) and garden land use (24 samples). Soil quality indicators were measured as: soil organic matter, particulate organic matter, and bulk density, plant available water capacity, S index, cation exchange capacity (CEC), electrical conductivity (EC), soil pH, and phosphatase enzyme. According to the results, land use types had a significant effect on all indicators except S index at 1% probability level. The maximum amount of soil pH, bulk density and phosphatase enzyme was obtained from forest land use. On the other hand, the maximum amount of the other indicators was attained from the garden land use. Totally, garden land use, due to having high organic matter, could improve the soil quality. However, the pasture land use had the worst soil quality due to the weak cover and the low organic matter.

In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R², MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.