A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Abstract

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

Exploring Climate Change Effects on Vegetation Phenology by MOD13Q1 Data: The Piemonte Region Case Study in the Period 2001–2019

“…The Global Climate Observing System (GCOS) has reported 26 of the 50 key climate variables (ECVs) as significantly dependent on satellite observations [33]. Remote sensing data are also largely used to develop prevention, mitigation, and adaptation measures to cope with the impact of climate change [34], to take out insurance policies based on vegetation trends [35][36][37][38], to support Common Agricultural Policy control in agriculture [39], to support wildlife diseases assessment [40], to manage the risk of falling trees and heat islands monitoring [41][42][43]. Studies on climate change, in fact, need continuous calibration and validation data and appropriate temporal and spatial sampling over a long period of time [44].…”

Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

“…This information is crucial to calibrate adaptive strategies to better face climate change effects in alpine ecosystems. It is worth remembering that mountains are among the most sensitive ecosystems to climate change, especially rising temperature, where effects observed more quickly than in other terrestrial habitats [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114] and this study could be considered another one concerning the short-term climate change effect on rangelands' and broad-lived forests' phenology in Aosta Valley. In particular, EO data from public and free archives of ready-to-use products such as MOD13Q1 and MOD16A2, proved to support well this type of analysis in spite of their reduced geometric resolution.…”

Mapping Ecological Focus Areas within the EU CAP Controls Framework by Copernicus Sentinel-2 Data

“…Specifically, S2 images can be used to derive various spectral indices (e.g., the Normalized Difference Vegetation Index, NDVI), which is useful for providing valuable vegetationrelated information [18,19]. S2 data are well known in the literature to be useful for: monitoring vegetation physiology [20,21], estimating crop production [22][23][24], characterising ecosystems [25,26], estimating crop damage from hazardous events for insurance purposes [27,28], assessing tree stability [29], managing private and public forests [30], supporting precision agriculture [31,32] and land cover/use mapping [33][34][35][36], monitoring disaster events [37,38], as well as, recently, for supporting CAP controls in agriculture [39][40][41] (see EU Regulation No. 809/2014 Article 40 bis amended by EU Regulation No.…”