Monitoring environmental and climate goals for European agriculture: User perspectives on the optimization of the Copernicus evolution offer

Energy productivity and GHG emission in the european agriculture: The club convergence approach

The agricultural production plays an important role in food provision and income generation for the rural population. To mitigate the climate change and ensure food security, the agricultural sector has faced multiple initiatives, including the European Green Deal. Developing effective frameworks for measures under such initiatives requires identifying reasonable benchmarks. Accordingly, it is important to assess the patterns of input use and productivity in the agricultural sector. This paper focuses on the agricultural energy productivity in the European Union (EU) Member States during 2005-2019. Indeed, the EU allocates substantial support to improve resource efficiency and reduce climate pressures in the agriculture. To the best of our knowledge, this is the first paper to apply the club convergence approach for the energy productivity the EU agriculture. This particular approach allows identifying the homogenous groups of the EU countries and, subsequently, assess the dynamics of the agricultural energy productivity within these groups. The results indicate that the agricultural energy productivity still requires attention in the EU countries, as only partial convergence was observed during 2015-2019. The EU countries were grouped into five clusters with different levels of the agricultural energy productivity. The results imply that the differences among resulting clusters remained rather stable over time. Therefore, relevant policies addressing energy efficiency issues can be developed for these relatively homogeneous groups to ensure further cohesion. The results suggest that countries with high energy productivity may show high levels of greenhouse gas intensity (and low levels of, e.g., labour productivity). Therefore, energy conservation and introduction of clean energy is a complex task that can be guided by the proposed framework and adjustment in the Common Agricultural Policy measures.

Sentinel-2 Data and Unmanned Aerial System Products to Support Crop and Bare Soil Monitoring: Methodology Based on a Statistical Comparison between Remote Sensing Data with Identical Spectral Bands

The growing need for sustainable management approaches of crops and bare soils requires measurements at a multiple scale (space and time) field system level, which have become increasingly accurate. In this context, proximal and satellite remote sensing data cooperation seems good practice for the present and future. The primary purpose of this work is the development of a sound protocol based on a statistical comparison between Copernicus Sentinel-2 MIS satellite data and a multispectral sensor mounted on an Unmanned Aerial Vehicle (UAV), featuring spectral deployment identical to Sentinel-2. The experimental dataset, based on simultaneously acquired proximal and Sentinel-2 data, concerns an agricultural field in Pisa (Tuscany), cultivated with corn. To understand how the two systems, comparable but quite different in terms of spatial resolution and atmosphere impacts, can effectively cooperate to create a value-added product, statistical tests were applied on bands and the derived Vegetation and Soil index. Overall, as expected, due to the mentioned impacts, the outcomes show a heterogeneous behavior with a difference between the coincident bands as well for the derived indices, modulated in the same manner by the phenological status (e.g., during the canopy developments) or by vegetation absence. Instead, similar behavior between two sensors occurred during the maturity phase of crop plants.

Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ ± SE values of 2.32 ± 0.07 for clay, 3.85 ± 0.19 for silt, and 3.51 ± 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.