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307 results on '"MERONI, MICHELE"'

1. Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal

Author

Sabo, Filip, Claverie, Martin, Meroni, Michele, and Essenfelder, Arthur Hrast

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

This paper investigated the potential of a multivariate Transformer model to forecast the temporal trajectory of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for short (1 month) and long horizon (more than 1 month) periods at the regional level in Europe and North Africa. The input data covers the period from 2002 to 2022 and includes remote sensing and weather data for modelling FAPAR predictions. The model was evaluated using a leave one year out cross-validation and compared with the climatological benchmark. Results show that the transformer model outperforms the benchmark model for one month forecasting horizon, after which the climatological benchmark is better. The RMSE values of the transformer model ranged from 0.02 to 0.04 FAPAR units for the first 2 months of predictions. Overall, the tested Transformer model is a valid method for FAPAR forecasting, especially when combined with weather data and used for short-term predictions.

Published
2024
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3. From parcel to continental scale -- A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations

Author

d'Andrimont, Raphaël, Verhegghen, Astrid, Lemoine, Guido, Kempeneers, Pieter, Meroni, Michele, and van der Velde, Marijn

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Applications
Abstract

Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to monitor agricultural land at a continental scale and in a timely manner. However, so far the potential of S1 has not been explored at such a scale. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, we present the first continental crop type map at 10-m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018. Random forest classification algorithms are tuned to detect 19 different crop types. We assess the accuracy of this EU crop map with three approaches. First, the accuracy is assessed with independent LUCAS core in-situ observations over the continent. Second, an accuracy assessment is done specifically for main crop types from farmers declarations from 6 EU member countries or regions totaling >3M parcels and 8.21 Mha. Finally, the crop areas derived by classification are compared to the subnational (NUTS 2) area statistics reported by Eurostat. The overall accuracy for the map is reported as 80.3% when grouping main crop classes and 76% when considering all 19 crop type classes separately. Highest accuracies are obtained for rape and turnip rape with user and produced accuracies higher than 96%. The correlation between the remotely sensed estimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99 (rape and turnip rape). Finally, we discuss how the framework presented here can underpin the operational delivery of in-season high-resolution based crop mapping., Comment: 19 pages, 11 Figures, 5 Tables (without appendix)

Published
2021
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4. Yield forecasting with machine learning and small data: what gains for grains?

Author

Meroni, Michele, Waldner, François, Seguini, Lorenzo, Kerdiles, Hervé, and Rembold, Felix

Subjects
Statistics - Applications
Abstract

Forecasting crop yields is important for food security, in particular to predict where crop production is likely to drop. Climate records and remotely-sensed data have become instrumental sources of data for crop yield forecasting systems. Similarly, machine learning methods are increasingly used to process big Earth observation data. However, access to data necessary to train such algorithms is often limited in food-insecure countries. Here, we evaluate the performance of machine learning algorithms and small data to forecast yield on a monthly basis between the start and the end of the growing season. To do so, we developed a robust and automated machine-learning pipeline which selects the best features and model for prediction. Taking Algeria as case study, we predicted national yields for barley, soft wheat and durum wheat with an accuracy of 0.16-0.2 t/ha (13-14 % of mean yield) within the season. The best machine-learning models always outperformed simple benchmark models. This was confirmed in low-yielding years, which is particularly relevant for early warning. Nonetheless, the differences in accuracy between machine learning and benchmark models were not always of practical significance. Besides, the benchmark models outperformed up to 60% of the machine learning models that were tested, which stresses the importance of proper model calibration and selection. For crop yield forecasting, like for many application domains, machine learning has delivered significant improvement in predictive power. Nonetheless, superiority over simple benchmarks is often fully achieved after extensive calibration, especially when dealing with small data., Comment: 17 pages, 7 figures

Published
2021

12. Increasing the prospective capacity of global crop and rangeland monitoring with phenology tailored seasonal precipitation forecasts

Author

Barcelona Supercomputing Center, Meroni, Michele, Vojnović, Petar, Zampieri, Matteo, Materia, Stefano, Rembold, Felix, Kipkogei, Oliver, Toreti, Andrea, Barcelona Supercomputing Center, Meroni, Michele, Vojnović, Petar, Zampieri, Matteo, Materia, Stefano, Rembold, Felix, Kipkogei, Oliver, and Toreti, Andrea

Abstract

This work has been supported by the FOCUS-Africa Horizon 2020 project (grant agreement No 869575). The developed code can be obtained through direct request to the JRC authors., Peer Reviewed, Postprint (published version)

Published
2024

13. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

Author

Mohammed, Gina H., Colombo, Roberto, Middleton, Elizabeth M., Rascher, Uwe, van der Tol, Christiaan, Nedbal, Ladislav, Goulas, Yves, Pérez-Priego, Oscar, Damm, Alexander, Meroni, Michele, Joiner, Joanna, Cogliati, Sergio, Verhoef, Wouter, Malenovský, Zbyněk, Gastellu-Etchegorry, Jean-Philippe, Miller, John R., Guanter, Luis, Moreno, Jose, Moya, Ismael, Berry, Joseph A., Frankenberg, Christian, and Zarco-Tejada, Pablo J.

Published
2019
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20. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences

Author

Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Just, Ceia, Ricardo S., Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., França, Filipe M., Gardner, Jonathan P. A., Harris, Bradley P., Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., López-Baucells, Adrià, Major, Heather L., Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A., Martin, Philip A., Mateos-Molina, Daniel, McConnaughey, Robert A., Meroni, Michele, Meyer, Christoph F. J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, C. Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C., Schmitter-Soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagnol, Doriane, Stanley, Thomas R., Stokesbury, Kevin D. E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, and Sutherland, William J.

Published
2020
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21. New Functionalities and Regional/National Use Cases of the Anomaly Hotspots of Agricultural Production (ASAP) Platform

Author

Rembold, Felix, primary, Meroni, Michele, additional, Otieno, Viola, additional, Kipkogei, Oliver, additional, Mwangi, Kenneth, additional, de Sousa Afonso, João Maria, additional, Ihadua, Isidro Metódio Tuleni Johannes, additional, José, Amílcar Ernesto A., additional, Zoungrana, Louis Evence, additional, Taieb, Amjed Hadj, additional, Urbano, Ferdinando, additional, Dimou, Maria, additional, Kerdiles, Hervé, additional, Vojnovic, Petar, additional, Zampieri, Matteo, additional, and Toreti, Andrea, additional

Published
2023
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24. How well do EO-based food security warning systems for food security agree? Comparison of NDVI-based vegetation anomaly maps in West Africa

Author

Bégué, Agnès, Madec, Simon, Lemettais, Louise, Leroux, Louise, Interdonato, Roberto, Becker-Reshef, Inbal, Barker, Brian, Justice, Christina, Kerdiles, Hervé, Meroni, Michele, Bégué, Agnès, Madec, Simon, Lemettais, Louise, Leroux, Louise, Interdonato, Roberto, Becker-Reshef, Inbal, Barker, Brian, Justice, Christina, Kerdiles, Hervé, and Meroni, Michele

Abstract

The GEOGLAM crop monitor for early warning is based on the integration of the crop conditions assessments produced by regional systems. Discrepancies between these assessments can occur and are generally attributed to the interpretation of the vegetation and climate data. The premise of this article is that other sources of discrepancy related to the data themselves must also be considered. We conducted a comparative experiment of the growth vegetation anomalies routinely produced by four operational crop monitoring systems in West Africa [FEWSNET, GIEWS, ASAP, VAM] for the 2010–2020 period. We collected a set of normalized differences vegetation index-based indicators (% mean, % median, and Z -score) and proposed original methods to analyze and compare the spatio-temporal variations of these indices using Hovmöller representation, statistics, and spatial analysis. To facilitate systems comparison, a classification scheme based on the percentile rank values of anomaly indicators was applied to produce 3-class alarm maps (negative, absence, and positive anomalies). Results show that, on an annual basis, the per-pixel similarity is relatively low between the four systems [24.5%–34.1%], and that VAM and ASAP are the most similar (70%). The reasons of the products discrepancies come mainly from different preprocessing methods, especially the choice of the reference period used to calculate the anomaly. The negative alarm agreement classes show no eco-climatic zoning influence, but negative alarms hot-spots were locally observed. The negative alarm agreement maps can be a useful tool for early warning as they synthesize the information provided by the different systems, with a confidence level.

Published
2023

25. The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing

Author

Widlowski, Jean-Luc, Mio, Corrado, Disney, Mathias, Adams, Jennifer, Andredakis, Ioannis, Atzberger, Clement, Brennan, James, Busetto, Lorenzo, Chelle, Michaël, Ceccherini, Guido, Colombo, Roberto, Côté, Jean-Francois, Eenmäe, Alo, Essery, Richard, Gastellu-Etchegorry, Jean-Philippe, Gobron, Nadine, Grau, Eloi, Haverd, Vanessa, Homolová, Lucie, Huang, Huaguo, Hunt, Linda, Kobayashi, Hideki, Koetz, Benjamin, Kuusk, Andres, Kuusk, Joel, Lang, Mait, Lewis, Philip E., Lovell, Jennifer L., Malenovský, Zbyněk, Meroni, Michele, Morsdorf, Felix, Mõttus, Matti, Ni-Meister, Wenge, Pinty, Bernard, Rautiainen, Miina, Schlerf, Martin, Somers, Ben, Stuckens, Jan, Verstraete, Michel M., Yang, Wenze, Zhao, Feng, and Zenone, Terenzio

Published
2015
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34. Dynamic Maize Yield Predictions Using Machine Learning on Multi-Source Data

Author

Croci, Michele, Impollonia, Giorgio, Meroni, Michele, Amaducci, Stefano, Michele Croci (ORCID:0000-0001-7356-2774), Giorgio Impollonia (ORCID:0000-0002-9878-6595), Stefano Amaducci (ORCID:0000-0002-6184-9257), Croci, Michele, Impollonia, Giorgio, Meroni, Michele, Amaducci, Stefano, Michele Croci (ORCID:0000-0001-7356-2774), Giorgio Impollonia (ORCID:0000-0002-9878-6595), and Stefano Amaducci (ORCID:0000-0002-6184-9257)

Abstract

Timely yield prediction is crucial for the agri-food supply chain as a whole. However, different stakeholders in the agri-food sector require different levels of accuracy and lead times in which a yield prediction should be available. For the producers, predictions during the growing season are essential to ensure that information is available early enough for the timely implementation of agronomic decisions, while industries can wait until later in the season to optimize their production process and increase their production traceability. In this study, we used machine learning algorithms, dynamic and static predictors, and a phenology approach to determine the time for issuing the yield prediction. In addition, the effect of data reduction was evaluated by comparing results obtained with and without principal component analysis (PCA). Gaussian process regression (GPR) was the best for predicting maize yield. Its best performance (nRMSE of 13.31%) was obtained late in the season and with the full set of predictors (vegetation indices, meteorological and soil predictors). In contrast, neural network (NNET) and support vector machines linear basis function (SVMl) achieved their best accuracy with only vegetation indices and at the tasseling phenological stage. Only slight differences in performance were observed between the algorithms considered, highlighting that the main factors influencing performance are the timing of the yield prediction and the predictors with which the machine learning algorithms are fed. Interestingly, PCA was instrumental in increasing the performances of NNET after this stage. An additional benefit of the application of PCA was the overall reduction between 12 and 30.20% in the standard deviation of the maize yield prediction performance from the leave one-year outer-loop cross-validation, depending on the feature set.

Published
2022

46. Dynamic Maize Yield Predictions Using Machine Learning on Multi-Source Data.

Author

Croci, Michele, Impollonia, Giorgio, Meroni, Michele, and Amaducci, Stefano

Subjects
MACHINE learning, KRIGING, SUPPORT vector machines, PRINCIPAL components analysis, FORECASTING
Abstract

Timely yield prediction is crucial for the agri-food supply chain as a whole. However, different stakeholders in the agri-food sector require different levels of accuracy and lead times in which a yield prediction should be available. For the producers, predictions during the growing season are essential to ensure that information is available early enough for the timely implementation of agronomic decisions, while industries can wait until later in the season to optimize their production process and increase their production traceability. In this study, we used machine learning algorithms, dynamic and static predictors, and a phenology approach to determine the time for issuing the yield prediction. In addition, the effect of data reduction was evaluated by comparing results obtained with and without principal component analysis (PCA). Gaussian process regression (GPR) was the best for predicting maize yield. Its best performance (nRMSE of 13.31%) was obtained late in the season and with the full set of predictors (vegetation indices, meteorological and soil predictors). In contrast, neural network (NNET) and support vector machines linear basis function (SVMl) achieved their best accuracy with only vegetation indices and at the tasseling phenological stage. Only slight differences in performance were observed between the algorithms considered, highlighting that the main factors influencing performance are the timing of the yield prediction and the predictors with which the machine learning algorithms are fed. Interestingly, PCA was instrumental in increasing the performances of NNET after this stage. An additional benefit of the application of PCA was the overall reduction between 12 and 30.20% in the standard deviation of the maize yield prediction performance from the leave one-year outer-loop cross-validation, depending on the feature set. [ABSTRACT FROM AUTHOR]

Published
2023
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47. SPIRITS OF LIFE AND PERCEPTION. GREEK AND ARABIC LEGACIES IN ALBERT THE GREAT'S EARLY THEOLOGY AND NATURAL PHILOSOPHY

Author

MERONI, MICHELE

Abstract

Among his contemporaries, Albert the Great (1200-1280) stands out for his extensive knowledge of philosophical and medical sources from the Islamic Word. The dissertation investigates the role of Albert's Arabic sources in his doctrine of spirit, a key concept straddling medicine and natural philosophy, in the broader context of his psychophysiology. The main purpose of the research is to provide a study of the reception of sources such as Avicenna (De anima, De animalibus, Canon), Qusta ibn Luqa (De differentia spiritus et animae) and other Arabic authors (Averroes, al-Ghazali and al-Magusi), shedding light on their relevance for Albert's own understanding of spiritus. Prefaced by a methodological introduction and a first chapter devoted to the relationship between philosophy and medicine, the dissertation provides a comparison between Albert's position on the role of spirit in his early theological works with his mature works (Aristotelian paraphrases), focusing especially on the Parva Naturalia. The research revolves around two main areas of interest: life- and perception-related phenomena, highlighting how Albert's understanding of the causal role of spirit changes over the years, thus moving away from his first conception in the early theological works, strongly influenced by his Arabic sources. A typical feature of Albert's psychophysiology of life-related phenomena in the Aristotelian paraphrases is the introduction of the concept of spirit in plants, a unique case among Albert's contemporaries. The second part of the dissertation deals with the role of spirits in sense perception and cognitive activities, with a special focus on dreams, divinatory dreams, and prophecy, where the influence of the Arabic (also, Hebrew) philosophical tradition is crucial.

Published
2021
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50. Characterization of fine resolution field spectrometers using solar Fraunhofer lines and atmospheric absorption features

Author

Meroni, Michele, Busetto, Lorenzo, Guanter, Luis, Cogliati, Sergio, Crosta, Giovanni Franco, Migliavacca, Mirco, Panigada, Cinzia, Rossini, Micol, and Colombo, Roberto

Subjects
Spectrometer -- Usage, Spectrometer -- Properties, Optics -- Research, Optics -- Methods, Astronomy, Physics
Abstract

The accurate spectral characterization of high-resolution spectrometers is required for correctly computing, interpreting, and comparing radiance and reflectance spectra acquired at different times or by different instruments. In this paper, we describe an algorithm for the spectral characterization of field spectrometer data using sharp atmospheric or solar absorption features present in the measured data. The algorithm retrieves systematic shifts in channel position and actual full width at half-maximum (FWHM) of the instrument by comparing data acquired during standard field spectroscopy measurement operations with a reference irradiance spectrum modeled with the MODTRAN4 radiative transfer code. Measurements from four different field spectrometers with spectral resolutions ranging from 0.05 to 3.5 nm are processed and the results validated against laboratory calibration. An accurate retrieval of channel position and FWHM has been achieved, with an average error smaller than the instrument spectral sampling interval. OCIS codes: 120.4640, 120.6200, 280.0280, 300.0300.

Published
2010

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