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Showing 2 results for Wetness Index

M. Naderi Khorasgani, A. Karimi,
Volume 11, Issue 42 (1-2008)
Abstract

  This research was carried out to study the impacts of geomorphologic characteristics of claypan on land use and land degradation. Databank of the study area was constructed and digital terrain model of claypan was prepared. By using GIS techniques spatial distributions of the subsurface drainage network, sediment transportation index and wetness index were calculated. The results indicate that the depth to the claypan is between 0 (where the pan is exposed at the surface) to 605 cm. There are several depressions in the claypan which are filled by new sediments. Each depression has a catchment which is charged by the drainage water of its attributed lands. While a depression drains naturally or synthetically, the attributed soils over the depression are in non saline or moderate salinity condition otherwise, a marshland, a waterlogging area or a salt crust zone develops over there. The results also indicated that soil surface salinity is a function of depth to claypan and drainage condition of area. The trends of salinity extension are different for closed and open catchments and the depth to the claypan could be estimated using electrical conductivity. The results also show that analysis of microtopography of soil surface and soil stratification should be considered for designing irrigation and drainage networks.


V. Habibi Arbatani, M. Akbari, Z. Moghaddam, A.m. Bayat,
Volume 26, Issue 4 (3-2023)
Abstract

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.


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