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Automated Processing of Sentinel-2 Products for Time-Series Analysis in Grassland Monitoring

Abstract : Effective grassland management practices require a good understanding of soil and vegetation properties, that can be quantified by farmers’ knowledge and remote sensing techniques. Many systems have been proposed in the past for grassland monitoring, but open-source alternatives are increasingly being preferred. In this paper, a system is proposed to process data in an open-source and automated way. This system made use of Sentinel-2 data to support grassland management at Haus Riswick in the region around Kleve, Germany, retrieved with help of a platform called Sentinelsat that was developed by ESA. Consecutive processing steps consisted of atmospheric correction, cloud masking, clipping the raster data, and calculation of vegetation indices. First results from 2018 resembled the mowing regime of the area with four growing cycles, although outliers were detected due to a lack of data caused by cloud cover. Moreover, that year’s extremely dry summer was visible in the time-series pattern as well. The proposed script is a primary version of a processing chain, which is suitable to be further expanded for more advanced data pre-processing and data analysis in the future.
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https://hal.inria.fr/hal-03361882
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Submitted on : Friday, October 1, 2021 - 3:40:19 PM
Last modification on : Wednesday, November 3, 2021 - 7:05:30 AM
Long-term archiving on: : Sunday, January 2, 2022 - 7:26:42 PM

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Tom Hardy, Marston Domingues Franceschini, Lammert Kooistra, Marcello Novani, Sebastiaan Richter. Automated Processing of Sentinel-2 Products for Time-Series Analysis in Grassland Monitoring. 13th International Symposium on Environmental Software Systems (ISESS), Feb 2020, Wageningen, Netherlands. pp.48-56, ⟨10.1007/978-3-030-39815-6_5⟩. ⟨hal-03361882⟩

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