2026-04-14T07:47:36Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.12327/20312025-10-22T11:28:53Zcom_2072_4428com_2072_4427col_2072_487898

00925njm 22002777a 4500 dc Paolini, Giovanni author Escorihuela, Maria Jose author Merlin, Olivier author Pamies-Sans, Magí author Bellvert, Joaquim author 2022-11-17 Maps of irrigation systems are of critical value for a better understanding of the human impact on the water cycle, while they also present a very useful tool at the administrative level to monitor changes and optimize irrigation practices. This study proposes a novel approach for classifying different irrigation systems at field level by using remotely sensed data at subfield scale as inputs of different supervised machine learning (ML) models for time-series classification. The ML models were trained using ground-truth data from more than 300 fields collected during a field campaign in 2020 across an intensely cultivated region in Catalunya, Spain. Two hydrological variables retrieved from satellite data, actual evapotranspiration ( ETa ) and soil moisture ( SM ), showed the best results when used for classification, especially when combined together, retrieving a final accuracy of 90.1±2.7% . All the three ML models employed for the classification showed that they were able to distinguish different irrigation systems, regardless of the different crops present in each field. For all the different tests, the best performances were reached by ResNET, the only deep neural network model among the three tested. The resulting method enables the creation of maps of irrigation systems at field level and for large areas, delivering detailed information on the status and evolution of irrigation practices. Paolini, Giovanni, Maria Jose Escorihuela, Olivier Merlin, Magí Pamies Sans, and Joaquim Bellvert. 2022. "Classification Of Different Irrigation Systems At Field Scale Using Time-Series Of Remote Sensing Data". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 10055-10072. doi:10.1109/JSTARS.2022.3222884 1939-1404 http://hdl.handle.net/20.500.12327/2031 https://doi.org/10.1109/JSTARS.2022.3222884 Classification of Different Irrigation Systems at Field Scale Using Time-Series of Remote Sensing Data