Developing a method for root-zone soil moisture monitoring at the field scale using remote sensing and simulation modeling
Efficient use of water and irrigation management are essential to sustain irrigated agriculture in drylands, where water resources are limited. Because of the high cost and difficulties of operation and maintenance of in situ instrumentation over irrigated fields, fine-scale monitoring of soil moist...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005997 |
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| Summary: | Efficient use of water and irrigation management are essential to sustain irrigated agriculture in drylands, where water resources are limited. Because of the high cost and difficulties of operation and maintenance of in situ instrumentation over irrigated fields, fine-scale monitoring of soil moisture (SM) based on remote sensing and a simulation model may be a practical way to inform irrigation practices. We herein propose a method for integration of low-cost, available, multi-source data, including field data (crop, soil, and weather) and high-resolution satellite data (Sentinel-2 and Landsat-8) into a soil–water–atmosphere–plant (SWAP) model to provide daily, accurate surface- and root-zone SM at the field scale that can inform optimal management of irrigation water. Specifically, effective soil parameters and crop growth in the SWAP model were parameterized and updated using satellite-based surface SM and leaf area index data obtained using inverse modeling and assimilation techniques. We applied and evaluated the developed method for the surface- and root-zone SM estimates using the measured SM over 13 marked locations in six study fields in Iran with two crop types, wheat and maize. The proposed method showed promising results at all marked locations, study fields, study crops, crop growth stages, and monitored soil depths and layers. The root mean square errors (RMSEs) and coefficients of determination (R2 values) were < 0.032 cm3 cm−3 and 0.52–0.95, respectively. The results showed that the type of irrigation system had a direct effect on the SM estimated with the proposed method. The proposed method could improve the spatiotemporal resolution of surface and root-zone SM monitoring via simulation of daily root-zone SM at a spatial resolution of 10 m. This method may enable the development of precise irrigation systems that optimize water allocations and conserve limited water resources at the field scale. |
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| ISSN: | 1873-2283 |