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
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/670218 |
| Date | 07 1900 |
| Creators | Kanee, Sarah |
| Contributors | McCabe, Matthew, Biological and Environmental Science and Engineering (BESE) Division, Johansen, Kasper, Blilou, Ikram |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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