Document Type : Research Paper
Authors
- Zohreh Azizzadeh 1
- Majid Mohammad Esmaili 1
- Ali Sattarian 2
- Seyed Ali Hosseini 3
- Bahareh Bahmanesh 1
1 Department of Rangeland and Watershed, Faculty of Agriculture and Natural Resources, Gonbad Kavos University, Gonbad Kavos, Iran.
2 Department of Biology, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, Iran
3 Research Group, Golestan Province Natural Resources and Agriculture Research Institute, Gorgan, Iran
Abstract
Soil moisture in the upper soil layers plays a vital role in water and soil resource management, directly influencing infiltration, runoff, agricultural productivity, and flood regulation. Its spatial variability is controlled by multiple factors, including climate conditions, topography, vegetation, and soil characteristics. Neglecting these variations often leads to significant errors in hydrological and agricultural modeling. This study investigates the relationship between geomorphometric indices as a proxy of topography and topsoil moisture across five sub-basins of the Simineh and Zarrineh rivers in northwest Iran, using both field observations and satellite data. Soil moisture measurements from 287 points (2015–2017) were compared with Soil Moisture Active Passive (SMAP) satellite estimates to produce high-resolution spatial maps. Several geomorphometric indices were derived, including the Topographic Wetness Index (TWI), Topographic Position Index (TPI), Wind Exposure Index (WEI), flow direction (Flow_D), flow accumulation, and Analytical Hillshading (AH). The Random Forest (RF) model was applied to determine the importance of geomorphometric attributes. Validation results revealed a strong correspondence between SMAP data and field observations, with July showing the highest correlation (r = 0.77, soil moisture = 0.18 cm³·cm⁻³), and May the lowest (r = 0.50). The RF model achieved robust performance (R² > 0.7, RMSE = 0.04%). Among the indices, WEI and TWI exhibited the greatest importance (>16%), followed by AH (13%), while Flow_D had the lowest influence (8.9%). These findings confirm the significant role of topographic and hydrological features in controlling soil moisture distribution. The integration of SMAP data with machine learning and geomorphometric indices provides a reliable framework for soil moisture monitoring, offering valuable insights for agricultural management, hydrological modeling, and environmental planning in similar watersheds.
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