Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 (S1) and Sentinel-2 (S2) data. An application is proposed at two study sites in Europe—in Spain and in Italy—with two climatic contexts (semiarid and humid, respectively), with the objective...

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Bibliographic Details
Published in:Water (Basel) 2022-03, Vol.14 (5), p.804
Main Authors: Elwan, Ehsan, Le Page, Michel, Jarlan, Lionel, Baghdadi, Nicolas, Brocca, Luca, Modanesi, Sara, Dari, Jacopo, Quintana Seguí, Pere, Zribi, Mehrez
Format: Article
Language:English
Subjects:
Agricultural land
Agriculture
Classification
Climate change
Crops
Drought
Environmental Engineering
Environmental Sciences
Irrigated areas
Irrigation
Remote sensing
Spatial data
Support vector machines
Vegetation
Wineries & vineyards
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Summary:This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 (S1) and Sentinel-2 (S2) data. An application is proposed at two study sites in Europe—in Spain and in Italy—with two climatic contexts (semiarid and humid, respectively), with the objective of proving the essential role of multi-site training for a robust application of the proposed methodologies. Several classifiers are proposed to separate irrigated and rainfed areas. They are based on statistical variables from Sentinel-1 and Sentinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5 km surroundings. The support vector machine (SVM) classification approach was tested with different options to evaluate the robustness of the proposed methodologies. The optimal number of metrics found is five. These metrics illustrate the importance of optical/radar synergy and the consideration of multi-scale spatial information. The highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. In addition, the accuracy is consistent at the two study sites. These results confirm the potential of the proposed approaches towards the most general use on sites with different climatic and agricultural contexts.
ISSN:2073-4441
2073-4441
DOI:10.3390/w14050804