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

Schiavon, Emma; Taramelli, Andrea; Tornato, Antonella; Pierangeli, Fabio

doi:10.1016/j.jenvman.2021.113121

Cited by 21 publications

(18 citation statements)

References 72 publications

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“…The "PRecursore IperSpettrale della Missione Applicativa" (PRISMA), put in orbit from 2019 by the Italian Space Agency (ASI), is a technology demonstrator satellite mission finalized to develop user-driven [45] pre-operational products, taking advantage of the combination of a hyperspectral sensor and a panchromatic imagery. PRISMA mission is fully funded by ASI, while the payload was developed by the Italian Leonardo company.…”

Section: Prisma Hyperspectral Datamentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Prisma Hyperspectral Datamentioning

confidence: 99%

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

et al. 2022

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“…Intensive agriculture, as shown by Garske et al (2021) [12], is responsible for about a quarter of the carbon footprint and three quarters of biodiversity loss, and if the reporting includes the entire food sector, the percentage increases. Schiavon et al (2021) [13] note that there is a vicious circle between agricultural production and climate change in which agriculture is a trigger and a victim, which is why climate and energy targets provide for a 40% reduction in emissions by 2030 for all sectors of the economy, which implies changes including in agriculture as well. Jehlićka et al (2020) [14] note that the technologically and economically sophisticated industrial agri-food system, adopted almost globally and a landmark for less developed countries, contributed to soil degradation, biodiversity loss and climate change, thus calling sustainability into question.…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of Agricultural Chains on the Carbon Footprint in the Context of European Green Pact and Crises

Haller

2022

Agriculture

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Agriculture and related activities generate a significant amount of greenhouse gas emissions with environmental and biodiversity implications. Based on the European objectives proposed by the Green Pact, this paper assesses the carbon footprint of agricultural chains for European OECD member countries. The period analysed is 2000–2019, and the method applied is panel data, specific to OLS models. We opted for three research directions: one is general, one is geographical and one is in temporary dynamics. The general analysis shows that in the European region, the organically cultivated area and economic growth reduce the carbon footprint, while fertilisers, aquaculture production, investments in road infrastructure and agricultural area determine its increase. The geographical analysis outlines the existence of two clusters, one consisting of Member States where agricultural chains reduce their carbon footprint and one consisting of a larger number of Member States, including the major European agricultural powers, where agricultural chains increase the carbon footprint. The temporary analysis reveals that the EU has changed its paradigm since 2008–2009. Economic growth has been slowly decoupling from the carbon footprint since 2016, and the rest of the factors analysed have become more environmental since the late 2010s. The EU has positioned itself towards achieving the objectives set by the Green Pact at a slow pace, justified by the heterogeneity of members’ national characteristics, in addition to its purpose not to harm the food security of the population. In order to achieve the objectives proposed by the Green Pact, it is necessary to focus on more extensive organic farming and traditional production methods, more extensive efforts to reduce nitrogen surplus in fertilizer content, to support short agri-food chains and to identify new production techniques, including the use of nanotechnology and high-performance technologies. Local agricultural chains are crystallizing into a possible solution to the insecurity generated by energy and food crises, political conflicts, pandemics, under the observation that organic products should be excluded from the category of luxury commodity.

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“…The confusion created by satellite images to determine the plant's height is due to the slope of the surface or the inclination of the plant caused by the wind. This problem is typical in radar images and S1 images too [59][60][61] . Therefore the methodology finally proposed as a solution to the problem was BirdsML.…”

Section: Discussionmentioning

confidence: 99%

Protecting Steppe Birds by Monitoring with Sentinel Data and Machine Learning under the Common Agricultural Policy

López-Andreu¹,

Hernández-Guillen²,

Domínguez-Gómez³

et al. 2022

Agronomy

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This paper shows the work carried out to obtain a methodology capable of monitoring the Common Agricultural Policy (CAP) aid line for the protection of steppe birds, which aims to improve the feeding and breeding conditions of these species and contribute to the improvement of their overall biodiversity population. Two methodologies were initially defined, one based on remote sensing (BirdsEO) and the other on Machine Learning (BirdsML). Both use Sentinel-1 and Sentinel-2 data as a basis. BirdsEO encountered certain impediments caused by the land’s slope and the crop’s height. Finally, the methodology based on Machine Learning offered the best results. It evaluated the performance of up to 7 different Machine Learning classifiers, the most optimal being RandomForest. Fourteen different datasets were generated, and the results they offered were evaluated, the most optimal being the one with more than 150 features, including a time series of 8 elements with Sentinel-1, Sentinel-2 data and derived products, among others. The generated model provided values higher than 97% in metrics such as accuracy, recall and Area under the ROC Curve, and 95% in precision and recall. The methodology is transformed into a tool that continuously monitors 100% of the area requesting aid, continuously over time, which contributes positively to optimizing the use of administrative resources and a fairer distribution of CAP funds.

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

Cited by 21 publications

References 72 publications

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

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

Influence of Agricultural Chains on the Carbon Footprint in the Context of European Green Pact and Crises

Protecting Steppe Birds by Monitoring with Sentinel Data and Machine Learning under the Common Agricultural Policy

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