Detecting Irrigation Events Over Several Summer Crops Using Sentinel-1 Data - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement
Communication Dans Un Congrès Année : 2023

Detecting Irrigation Events Over Several Summer Crops Using Sentinel-1 Data

Résumé

This study presents the potential of the Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to detect irrigation events over summer crops, including Maize, Soybean, Sorghum and Potato. The potential of the S1 to detect the irrigation events was carried out using the Irrigation Event Detection Model (IEDM) in five study sites in south Europe and the Middle East. The IEDM is a decision tree model initially developed to detect irrigation events using the change detection algorithm applied to the S1 time series data. Results showed generally good overall accuracy for irrigation detection using the S1 data, reaching 67% for all studied sites together. This accuracy varied according to the studied area, with the highest accuracy for semi-arid areas and lowest for temperate areas. In addition, the accuracy of irrigation detection decreases as the vegetation becomes well developed.
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Dates et versions

hal-04795349 , version 1 (21-11-2024)

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Nicolas Baghdadi, Hassan Bazzi, Sami Najem, Hadi Jaafar, Michel Le Page, et al.. Detecting Irrigation Events Over Several Summer Crops Using Sentinel-1 Data. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Jul 2023, Pasadena, United States. pp.2839-2842, ⟨10.1109/IGARSS52108.2023.10282768⟩. ⟨hal-04795349⟩

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