A Multi-platform Comparison of Phenology for Semi-automated Classification of Crops

  • Sarah Kanee

Student thesis: Master's Thesis

Abstract

Remote sensing has enabled unprecedented earth observation from space and has proven to be an invaluable tool for agricultural applications and crop management practices. Here we detect seasonal metrics indicating the start of the season (SOS), the end of the season (EOS) and maximum greenness (MAX) based on vegetation spectral signatures and the normalized difference vegetation index (NDVI) for a time series of Landsat-8, Sentinel-2 and PlanetScope imagery of potato, wheat, watermelon, olive and peach/apricot fields. Seasonal metrics were extracted from NDVI curves and the effect of different spatial and temporal resolutions was assessed. It was found that Landsat-8 overestimated SOS and EOS and underestimated MAX due to its low temporal resolution, while Sentinel-2 offered the most reliable results overall and was used to classify the fields in Aljawf. Planet data reported the most precise SOS and EOS, but proved challenging for the framework because it is not a radiometrically normalized product, contained clouds in its imagery, and was difficult to process because of its large volume. The results demonstrate that a balance between the spatial and temporal resolution of a satellite is important for crop monitoring and classification and that ultimately, monitoring vegetation dynamics via remote sensing enables efficient and data-driven management of agricultural system
Date of AwardJul 2021
Original languageEnglish (US)
SupervisorMatthew McCabe (Supervisor)

Keywords

  • Earth observation, remote sensing, google earth engine, phenology, crop monitoring, crop classification

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