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ThinkMind // GEOProcessing 2020, The Twelfth International Conference on Advanced Geographic Information Systems, Applications, and Services // View article geoprocessing_2020_1_80_30063
Using Satellite Imagery and Vegetation Indices to Monitor and Quantify the Performance of Different Varieties of Camelina Sativa
 Authors:
Mar Parra
Lorena Parra
David Mostaza-Colado
Pedro Mauri
Jaime Lloret Keywords: Sentinel-2; rainfed crops; multivariable statistical analysis; NDWI; NDMI; EVI Abstract:
In recent years, the cropping of Camelina sativa has gained popularity among the farmers of rainfed crops. It is an annual and flexible crop, which can grow in different regions. The estimate of the crop yield is essential for farmers. Camelina sativa is a small plant that forms a uniform green tapestry of grass. Hence, satellite imagery can be used for monitoring the crops. In this study, we present the use of Sentinel-2 data to monitor the performance of 6 varieties of Camelina sativa. Crops have been growing from fall to spring, and the harvest occurred in early June. We include satellite imagery from February to June. In this paper, we include a single image per month. Moreover, due to the size of the plots, we only consider the data from bands with a spatial resolution of 10m and 20m. First of all, the differences in spectral signatures of varieties along the time are presented. Then, we detail the possibilities of correlation between different vegetation indices and crop harvest. Finally, a multivariable statistical analysis to correlate bands of Sentinel-2 with harvested seeds is shown. This analysis estimates the yield with high accuracy. Pages: 48 to 53 Copyright: Copyright (c) IARIA, 2020 Publication date: March 22, 2020 Published in: conference ISSN: 2308-393X ISBN: 978-1-61208-762-7 Location: Valencia, Spain Dates: from November 21, 2020 to November 25, 2020
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