Articles | Volume 385
https://doi.org/10.5194/piahs-385-443-2024
https://doi.org/10.5194/piahs-385-443-2024
Post-conference publication
 | 
19 Apr 2024
Post-conference publication |  | 19 Apr 2024

Assessing the Accuracy of Multiple Classification Algorithms Combining Sentinel-1 and Sentinel-2 for the Citrus Crop Classification and spatialization of the Actual Evapotranspiration Obtained from Flux Tower Eddy Covariance: Case Study of Cap Bon, Tunisia

Amal Chakhar, Rim Zitouna-Chebbi, David Hernández-López, Rocío Ballesteros, Imen Mahjoub, and Miguel A. Moreno

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Cited articles

Chakhar, A., Hernández-López, D., Ballesteros, R., and Moreno, M. A.: Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data, Remote Sens., 13, 1–21, https://doi.org/10.3390/rs13020243, 2021. 
Copernicus Open Access Hub: Sentinel-2 archives, https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2/data-products, last access: 12 December 2023. 
Er-Raki, S., Chehbouni, A., Guemouria, N., Duchemin, B., Ezzahar, J., and Hadria, R.: Combining FAO-56 Model and Ground-Based Remote Sensing to Estimate Water Consumptions of Wheat Crops in a Semi-Arid Region, Agr. Water Manage., 87, 41–54, https://doi.org/10.1016/j.agwat.2006.02.004, 2007. 
Glenn, E. P., Christopher, M. U. N., Hunsaker, D. J., and Nagler, P. L.: Vegetation Index-Based Crop Coefficients to Estimate Evapotranspiration by Remote Sensing in Agricultural and Natural Ecosystems, Hydrol. Process., 25, 4050–4062, https://doi.org/10.1002/hyp.8392, 2011. 
Hamouda, M. F. B.: Approche Hydrogéologique et Isotopique Des Systèmes Aquifères Côtiers Du Cap Bon: Cas Des Nappes de La Côte Orientale et d'El Haouaria, Tunisie, PhD, INAT, 2008. 
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Short summary
It is very important to have accurate estimation of the total area of citrus plots. To achieve this objective, we performed crop classification using Sentinel-1 and -2. Additionally, ETa was available, so we tried to find it is potential relation with NDVI. The results were obtained with the classifier SVM using GEE Google Earth Engine and provided a significant contribution to the citrus crop classification also highlighted the potential to extrapolate accurate ET estimation to larger scales.
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