Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

López-Andreu, Francisco Javier; Erena, Manuel; Domínguez-Gómez, José Antonio; López-Morales, Juan Antonio

doi:10.3390/agronomy11040621

Cited by 21 publications

(15 citation statements)

References 48 publications

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“…This work has aimed to propose a methodology to automatically monitor the aid line for the protection of steppe birds promoted by the CAP, using Sentinel 1 and 2 missions and derived products that improve the decision-making process on granting aid. As part of the methodology, a traffic light map was developed to indicate which applications comply with the aid requirements (green) and which do not (red) [24].…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…The ARD is then fundamental to the EO data cube, which integrates this data into one logical array multi-temporal thematic analysis. The DC input on machine learning [24,25] allows for generating crop modeling [26].…”

Section: Introductionmentioning

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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“…Data acquisition is generally feasible and affordable, but transforming data into usable information requires technical knowledge not often available for all farmers. The past limitations linked to the direct use of multispectral satellite remote sensing data, such as insufficient spatial resolution, inadequate temporal resolution, and complex data access and processing, were significantly overcome since the launch in mid-2015 of the EU Copernicus Program Sentinel-2 mission combined with the development of appropriate desktop and cloud-based data processing platforms (e.g., Google Earth Engine: https://earthengine.google.com/ (accessed on 16 June 2021) [118]; Sen2-Agri: http://www.esa-sen2agri.org/ (accessed on 16 June 2021) [119]; and Sen4CAP: http://esa-sen4cap.org/ (accessed on 16 June 2021) [120]). As for models based on computer vision and image processing, correspondent operational solutions are not yet available for growers as needed.…”

Section: Discussionmentioning

confidence: 99%

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

2021

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Purpose—knowing in advance vineyard yield is a critical success factor so growers and winemakers can achieve the best balance between vegetative and reproductive growth. It is also essential for planning and regulatory purposes at the regional level. Estimation errors are mainly due to the high inter-annual and spatial variability and inadequate or poor performance sampling methods; therefore, improved applied methodologies are needed at different spatial scales. This paper aims to identify the alternatives to traditional estimation methods. Design/methodology/approach—this study consists of a systematic literature review of academic articles indexed on four databases collected based on multiple query strings conducted on title, abstract, and keywords. The articles were reviewed based on the research topic, methodology, data requirements, practical application, and scale using PRISMA as a guideline. Findings—the methodological approaches for yield estimation based on indirect methods are primarily applicable at a small scale and can provide better estimates than the traditional manual sampling. Nevertheless, most of these approaches are still in the research domain and lack practical applicability in real vineyards by the actual farmers. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Research limitations—this work is based on academic articles published before June 2021. Therefore, scientific outputs published after this date are not included. Originality/value—this study contributes to perceiving the approaches for estimating vineyard yield and identifying research gaps for future developments, and supporting a future research agenda on this topic. To the best of the authors’ knowledge, it is the first systematic literature review fully dedicated to vineyard yield estimation, prediction, and forecasting methods.

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“…Based on the work published by López-Andreu et al [10], which proposes a methodology to identify rice cultivation by ML, using S2 data, in this work, the methodology developed in that article is improved by confronting the results obtained in the extraction of biophysical index signals, the random forest (RF) classification of a multi-element time series, and the RF classification of a single S2 image. In addition, a detailed treatment of the resulting RF classification rasters is added to discover whether 100% of the area declared in the aid application (toward the end of the crop's vegetative development) is cultivated.…”

Section: Introductionmentioning

confidence: 99%

Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing

et al. 2022

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The European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications continuously for an agricultural year. This paper presents a generic methodology developed for implementing monitoring controls. To achieve this, the dataset provided by the Sentinel-2 time series is transformed into information through the combination of classifications with machine learning using random forest and remote sensing-based biophysical indices. This work focuses on monitoring the helpline associated with rice cultivation, using 13 Sentinel-2 images whose grouping and characteristics change depending on the event or landmark being sought. Moreover, the functionality to check, before harvesting the crop, that the area declared is equal to the area cultivated is added. The 2020 results are around 96% for most of the metrics analysed, demonstrating the potential of Sentinel-2 for controlling subsidies, particularly for rice. After the quality assessment, the hit rate is 98%. The methodology is transformed into a tool for regular use to improve decision making by determining which declarants comply with the crop-specific aid obligations, contributing to optimising the administrations’ resources and a fairer distribution of funds.

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Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

Cited by 21 publications

References 48 publications

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

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

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing

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