Document Type : Original Article


1 Ph.D. Student, Watershed Management, Dep. of Watershed Management, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Gorgan

2 Assistant Prof., Dep. of Geography, Faculty of Geography, University of Amin Police, Tehran

3 Associate Prof., Dep. of Watershed Management, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Gorgan

4 Assistant Prof., Dep. of Desert Management, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Gorgan


Temporal and spatial distribution of soil moisture is a key variable for hydrological planning. In the research area, increasing soil moisture accelerates the formation of runoff. The purpose of this study is to investigate the L-band potential of the Pulsar 2 sensor from the ALOS satellite in estimating soil surface moisture in order to manage water resources and reduce flood hazards. Samples were taken using random sampling. Simultaneously with obtaining SAR data, weight moisture, surface roughness and plant water content, average radar backscattering coefficients and indices angle were measured on the image. The pre-processing, processing and post-processing stages of SAR data were calculated using SNAP software and extraction of plant and moisture indices in ArcGIS 5.10 environment. Surface roughness was obtained using two angled cameras with 20 images with 10 control points (GCP) for each cluster, then using Agisoft Photo Scan software, dense cloud meshes and meshing were performed to produce DTM. Longitudinal profiles of surface roughness for each cluster were extracted from the Analysts 3D tab in ArcGIS. In order to select the appropriate model in the region, three models were considered in estimating soil surface moisture including Dubois-MLR-WCM models. The results of three models in estimating soil surface moisture in HH polarization in Dubois model with R2 = 0.82 and RMSE = 0.027, MLR model with R2 = 0.71 and RMSE = 0.03 and WCM model with R2 = 0.67 and RMSE, respectively = 0.033 was estimated. The results showed that Dubois model is more suitable for research areas and similar conditions in lands with sparse to medium cover.

Keywords: Soil surface moisture, ALOS PALSAR 2, Dubois model, Multivariate linear regression model, Water cloud model


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