Geodaysit 2023

Precision viticulture aims to enhance quality standards of wine production by improving vineyard management. In this framework, satellite optical remote sensing has already proved to be effective for mapping vegetation behavior in space and time. These maps, properly processed, are useful to optimize agronomic practices improving wine production/quality and mitigating environmental impacts. Nevertheless, vineyards represent a challenge in this context because grapevine canopies are discontinuous, and the observed reflectance signal is affected by background. In fact, satellite imagery ordinarily provides spectral measures with medium-low geometric resolution (≥ 100 m2). Therefore, spectral mixture between grapevine canopies, grass and soils is expected within a satellite-derived reflectance pixel and not considering this problem can deeply affect deductions based on this data. In this work, Sentinel-2 (S2) NDVI maps (10 m resolution) were computed and compared to the ones obtained from DJI P4 multispectral UAV over a vineyard sizing 1.5 ha and located in Piemonte region (NW Italy). The proportion of row and inter-row (α(x,y) and 1-α(x,y)) within S2 pixel was computed and mapped classifying DJI photogrammetry point cloud. Involving α(x,y) and S2 NDVI values, reversing spectral unmixing system was defined solving for two average endmembers NDVI values (row and inter-row) using a moving window (21x21 pixels) least squares approach. Results were compared at S2 pixel-level to the average ones computed from DJI, showing a MAE of 0.15 and 0.10 of row and inter-row NDVI respectively.

Starting from 1962 the Common Agricultural Policy (CAP) has supported through contributions the agricultural sector aiming at preserving the environment and improving crops production. The local Paying Agencies (PA) verify the correctness, completeness and compliance of farmers applications by administrative checks (ACs) and on-the-spot checks (OTSCs). ACs are performed on 100% of applications to automatically detect formal faults through informatics tools. OTSCs are performed on about the 5% of applications testing the compliance with envisaged commitments and obligations, verify eligibility criteria and checking the truthfulness of declared area size. Recently, the article 10 of the recent EU regulation (N. 1173/2022), defined new controls based on remote sensing, specifically by adopting Copernicus Sentinel-2 (S2) imagery, or “other data” at least equivalent value. The adoption of S2 imagery allows to monitor all areas declared by farmers’ applications longing for irregularities detection. Consequently, this type of control can be applied to all CAPs (no longer 5%) applications in each member state. In this framework, the new CAP 2023-2027, requires a gradual implementation of such remote-sensing based tools within member states control systems, becoming compulsory in 2024. Furthermore, the 2023-2027 CAP will introduce some new types of contributions called 'eco-schemes' related to the climate, environment and animal welfare. Nevertheless, a proper review of how remote sensing-based tools can be applied to these new contributions is missing. Therefore, in this work we preliminary explore which marker can be detected by Copernicus S2 data in terms of field surface, agronomic practices and monitor period, possibly related to a specific CAP contribution requirement. Focuses will concern: (a) basic payment; (b) eco-schemes; (c) enhanced conditionality.

Sustainable agriculture is one of the main focus of the 2023 – 2027 Common Agricultural Policy (CAP). For this reason, the new CAP strategic plan presents greater ambitions on climate and environment action in comparison of the previous programming period and stronger incentives that promote climate- and environment-friendly farming practices (i.e. minimizing soil disturbance, organic and carbon farming, maintaining permanent ground cover and adopting combined rotations) are provided. Among the several options, avoiding bare soil conditions and consequently promoting cover crops, or even to cultivate two main crops in a year, can provide excellent benefits. In particular, soil erosion and nitrate percolation are limited and soil structure, fertility, organic carbon sequestration and adaptability to climate change are supported. Consequently, an estimation of how much cultivated area is currently managed in this way should be estimated. Within the farmer CAP application, single (i.e. winter or summer) and a double crop could be included even if more crops can indeed be cultivated afterwards. Accordingly, the scope of this research is to design and validate an approach to classify and map the fields where a crop cover maintenance is promoted rather than the single crop based on Copernicus Sentinel-2 (S2) data. The study area is located in Austria, where a representative sample of the main crop types cultivated in the region was derived from the declarations to the Integrated Administration and Control System (IACS) for the year 2021. The approach relies on the classification of reflectance data from S2 time series including nine vegetation indices that were used to identify single or double crop systems. For this purpose, two supervised classifiers were applied namely One-Class Support Vector Machine (OneClassSVM) and Random Forest (RF). Statistical measures such as Overall Accuracy and Cohen's kappa coefficient were derived from the confusion matrices and the differences between field data and mapping results were analysed. A new map showing single vs double-crop systems was generated for further spatial analysis and interpretation.