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Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran)
- Pahlavan-Rad, Mohammad Reza, Akbarimoghaddam, Alireza
- Catena 2018 v.160 pp. 275-281
- case studies, clay, ecosystems, environmental factors, floodplains, hydraulic conductivity, models, normalized difference vegetation index, pH, prediction, rivers, sand, sand fraction, silt, soil sampling, soil structure, soil surveys, soil texture, wind, wind erosion, Iran
- Flood plain ecosystems show significant soil spatial variability. Understanding spatial variations of soil texture fractions and pH in flood plains is necessary for ensuring proper management of these plains, because these properties influence soil structure, fertility, hydraulic conductivity, infiltration, and erosion. In the present study, the distribution of soil texture fractions and pH was investigated in a flood plain with intensive wind erosion for an area of ~41,000ha in Zahak county of Sistan and Baluchestan province in eastern Iran. A random forest technique was used to link environmental variables and the studied properties. 460 soil samples were collected from 0 to 30cm depth across a 750m grid. 361 samples were used for training and 99 for independent validation. Results showed that the distance from the river was the most important environmental variable for predicting soil texture fractions and pH in the study area. Natural channel networks, elevation, valley depth, LS factor, NDSI, vertical distance to channel networks, slope, wind effect, NDVI, and brightness were other important variables. The maps produced indicated a higher sand content near Sistan River. Clay, silt, and pH contents increased with distance from Sistan River. Results showed that clay and pH had a similar distribution in the study area. The values of RMSE for the maps of estimated sand, silt, clay, and pH in validation data were respectively 21.40, 17.45, 6.06 and 0.45. These values of RMSE for sand, silt, clay, and pH were respectively 10.3, 10.7, 15.1, and 13.3% lower than a simple model (mean model). Results indicated that using the distance from the river and channel networks as a variable in digital soil mapping can increase the accuracy of the predictive maps of soil properties in flood plains.