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Open AccessArticle

Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

1
Piksel Srl, Via Breda 176, 20126 Milan, Italy
2
Institut wallon de l’évaluation, de la prospective et de la statistique, Route de Louvain-la-Neuve, 2, 5001 Belgrade, Belgium
3
European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, I-21027 Ispra, VA, Italy
4
Arhs Developments, 2b, rue Nicolas Bové, L-1253 Luxembourg, Luxembourg
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2195; https://doi.org/10.3390/rs12142195
Received: 11 May 2020 / Revised: 5 July 2020 / Accepted: 6 July 2020 / Published: 9 July 2020
(This article belongs to the Collection Sentinel-2: Science and Applications)
The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators. View Full-Text
Keywords: NDVI time series; small agriculture parcels; fields; CAP; similarity; spatial limits; Sentinel-2; PlanetScope; checks by monitoring; CbM NDVI time series; small agriculture parcels; fields; CAP; similarity; spatial limits; Sentinel-2; PlanetScope; checks by monitoring; CbM
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MDPI and ACS Style

Vajsová, B.; Fasbender, D.; Wirnhardt, C.; Lemajic, S.; Devos, W. Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring. Remote Sens. 2020, 12, 2195.

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