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Gewählte Publikation:

Longo-Minnolo, G; Consoli, S; Vanella, D; Ramirez-Cuesta, JM; Greimeister-Pfeil, I; Neuwirth, M; Vuolo, F.
(2022): A stand-alone remote sensing approach based on the use of the optical trapezoid model for detecting the irrigated areas
AGR WATER MANAGE. 2022; 274, 107975 FullText FullText_BOKU

Under the current water scarcity scenario, the promotion of water saving strategies is essential for improving the sustainability of the irrigated agriculture. In particular, high resolution irrigated area maps are required for better understanding water uses and supporting water management authorities. The main purpose of this study was to provide a stand-alone remote sensing (RS) methodology for mapping irrigated areas. Specifically, an unsupervised classification approach on Normalized Difference Vegetation Index (NDVI) data was coupled with the OPtical TRApezoid Model (OPTRAM) for detecting actual irrigated areas without the use of any reference data. The proposed methodology was firstly applied and validated at the Marchfeld Cropland region (Austria) during the irrigation season 2021, showing a good agreement with an overall accuracy of 70%. Secondly, it was applied at the irrigation district Quota 102,50 (Italy) for the irrigation seasons 2019-2020. The results of the latter were instead compared with the data declared by the Reclamation Consortium, finding an overestimation of irrigated areas of 21%. In conclusion, this study suggests an easy-to-use approach, eventually independent of reference data such as agricultural statistical surveys or records and replicable under different agricultural set-tings in continental or Mediterranean climates to support stakeholders for regular estimation of irrigated areas in different growing years or detecting eventual unauthorized water uses. However, some uncertainties should be considered, needing further analyses for improving the accuracy of the proposed approach.
Autor*innen der BOKU Wien:
Vuolo Francesco

Find related publications in this database (Keywords)
Satellite images
Unsupervised classification
Water content

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