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

Abstract

<jats:p>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.</jats:p>

Environmental Drivers of Water Use for Caatinga Woody Plant Species: Combining Remote Sensing Phenology and Sap Flow Measurements

“…This was due to the proximity of trees from different species, sometimes less than two meters, but also due to the spatial autocorrelation of the pixels. Each pixel influences its neighborhood [42], reducing the effective pixel resolution. It is worth mentioning that the spatial miss-registration in the Sentinel-2 images series vary by around 12 m (more than one pixel) but can be greater than 3 pixels according to [43].…”

Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information

“…The proposed methodology is an accurate and efficient alternative to generate land abandonment maps since it allows obtaining maps with high accuracy from a reduced set of training data. In addition, the limitations of Sentinel-2 for areas with high spatial fragmentation was overcome thanks to the use of VHR images [16][17][18]. We created a complete map of abandoned citrus plots in the Oliva municipality for the year 2019, that detected a 31% of erroneously classified plots in the current methodology used by the public administration.…”

“…Previous studies on citrus already showed serious discrepancies between agriculture census data and satellite-derived cropland area using medium-resolution satellite imagery as Landsat [13]. Sentinel-2 images overcome some of these limitations and showed its potential in agricultural applications [14,15], however in areas with high spatial fragmentation they may be not enough [16,17]. The Sentinel-2 satellite images did not show enough accuracy to identify abandoned plots in our study area due to resolution limitations and the small size of the plots, for this reason, the use of higher resolution images is recommended [18].…”

Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring

“…In some cases, it might also mitigate the negative effect of the imperfect geometric accuracy of the image. As was suggested in [44] in the context of a Sentinel-2 analysis, a buffer size of −5 m is chosen in the present study. Another possible source of noise is the fact that the edges of the fields are sometimes managed differently from the central part of the fields (different machines passage orientation or inputs interdiction for example), which causes heterogeneity in the parcel signal.…”