بررسي پتانسيل استفاده از اطلاعات تبخير و تعرق در تشخيص باغات به كمك تصاوير ماهواره‌اي

Abstract Discriminating orchards from other vegetation covers in satellite imagery is challenging due to spectral similarities with some agricultural lan‎ds, seasonal variations in vegetation conditions, the small an‎d irregular shape of orchard parcels, an‎d the occurrence of mixed pixels. Most conventional orchard-mapping methods rely primarily on the spectral characteristics of optical imagery, whereas evapotranspiration (ET), in addition to vegetation conditions, is influenced by surface moisture conditions an‎d surface energy exchanges an‎d may therefore provide complementary information on orchard behavior. Accordingly, this study aimed to investigate the potential of evapotranspiration information, both independently an‎d in combination with optical imagery, an‎d to eva‎luate the role of its temporal variability in orchard identification. The study area was Tiran an‎d Karvan County, located in western Isfahan Province, Iran, which is characterized by an arid to semi-arid climate an‎d contains extensive agricultural an‎d orchard lan‎ds. In this study, evapotranspiration data derived from the SEBAL an‎d METRIC algorithms an‎d the ready-to-use PML-V2 an‎d MOD16 products were eva‎luated alongside Lan‎dsat 8 optical imagery corresponding to the Iranian year 1398. Reference data were prepared for two classes, orchard an‎d non-orchard, comprising a total of 27,960 sample points, with 13,980 samples for each class. Classification was performed using the Ran‎dom Forest algorithm, with 80% of the samples used for model training an‎d the remaining 20% used for model eva‎luation. The eva‎luated scenarios included independent monthly evapotranspiration, annual multi-temporal evapotranspiration, selec‎ted multi-temporalevapotranspiration, a combination of the three best-performing evapotranspiration layers, independent monthly Lan‎dsat 8 optical imagery, an‎d monthly combinations of Lan‎dsat 8 imagery with each evapotranspiration source. The performance of the scenarios was compared using Overall Accuracy an‎d the F1-score of the orchard class. Keywords: Evapotranspiration, Orchard Identification, Satellite Imagery, Remote Sensing, Ran‎dom Forest, Multi-temporal Data, Classification

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171990