Improving Crop Yield Estimation through SWAP Model Using Satellite Data

Document Type : Research Paper


1 Master science of irrigation and drainage , Department of Irrigation and Reclamation Eng., College of Agriculture and Natural Resources, University of Tehran

2 Assistance professor, Department of Irrigation and Reclamation Eng., College of Agriculture and Natural Resources, University of Tehran

3 Assistance professor, Water Engineering Department, University of Guilan


Updating the satellite data in crop growth models is a useful technique to estimate the crop yield on some large scale farms. Throughout the present study, the improvement of SWAP model simulation was investigated using this technique for crop yield estimation. The study was conducted during 2012 growing season at some three center- pivot farms, covered by fodder maize and sugar beet as the main crops in Qazvin Irrigation Network. SWAP model was run by two methods, namely through either non-updated or updated satellite Leaf Area Index (LAI) data. Results indicated that sugar beet and fodder maize yields estimated through updated SWAP model were improved by about 13.7 and 14.5 (%) in absolute percent error while they amounted to 3.321 and 1.621 (ton/ha) in RMSE.
The obtained results indicated that the LAI assimilation technique (using satellite data) can greatly reduce errors related to model input parameters. It can reduce the uncertainty as well as estimate fairly accurately the crop yield in a large area and on any individual farm.


Main Subjects

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