Assessing the Sentinel-2 Capabilities to Identify Abandoned Crops Using Deep Learning

Portalés-Julià, Enrique; Campos-Taberner, Manuel; Garcı́a-Haro, Francisco Javier; Gilabert, María Amparo

doi:10.3390/agronomy11040654

Cited by 18 publications

(12 citation statements)

References 28 publications

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“…In agricultural context, besides having been used for crop classification and identification [74][75][76], DL techniques have played an important role in areas such as detecting diseases [77][78][79][80][81], yield prediction [82][83][84][85][86] and weed detection [14,[87][88][89] and have also shown great potential in detecting agricultural abandonment using remote sensing data [17,34].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the authors utilized self-organizing Kohonen maps (SOMs) to reconstruct missing data due to cloudy holes. [25] 2020 LSTM, MLP, U-net Sentinel-1, Sentinel-2 ( 14) [32] 2021 ANN Sentinel-2 (4) [33] 2021 PSE + LTAE Sentinel-2 (20) [34] 2021 Bi-LSTM, LSTM Sentinel-2 ( 16) [35] 2021 CNN Sentinel-2 (11) [36] 2021 CNN-CRF, CNN Sentinel-1 (9) [37] 2021 MSFCN, CNN, Sentinel-1 ( 14) [38] 2021 LSTM, CNN, GAN Landsat-8 (3) [39] 2021 CNN AgriSAR (6) [40] 2022 CNN Sentinel2-Agri ( 20)…”

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

“…Satellite data has also been used in detecting crop abandonment [46][47][48][49][50]. For instance, the authors of [34] sought to evaluate the capability of Sentinel-2 satellite data in detecting land abandonment in the Valencia province of Spain. They utilized five spectral indices to detect temporal differences between abandoned and active land parcels and trained two DL models-an LSTM network and its bi-directional counterpart (Bi-LSTM)-on the spectral index data.…”

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

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Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

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Section: Discussionmentioning

confidence: 99%

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

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Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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“…Moreover, crops that were abandoned and did not qualify for the CAP basic payment were detected. In the work of Portalés-Julià, E. et al [17], such plots in the province of Valencia (Spain) were determined by evaluating time series of S2-derived spectral indices, together with machine learning and deep learning algorithms, to discriminate abandoned plots.…”

Section: Related Workmentioning

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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“…Convolutional neural networks (CNNs), one of the visual-based deep learning approaches, have been widely used to classify Earth's surface by extracting abstract textural features in imagery [21]. Many successful applications of CNNs have been reported in recent years in crop type classification [22], tree species classification [23], and abandoned cropland detection [24].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data

et al. 2021

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Salinization in arid or semiarid regions with water logging limits cropland yield, threatening food security. The highest level of farmland salinization, that is, abandoned salinized farmland, is a tradeoff between inadequate drainage facilities and sustainable farming. The evolution of abandoned salinized farmlands is closely related to the development of cropping systems. However, detecting abandoned salinized farmland using time-series remote sensing data has not been investigated well by previous studies. In this study, a novel approach was proposed to detect the dynamics of abandoned salinized farmland using time-series multispectral and thermal imagery. Thirty-two years of temporal Landsat imagery (from 1988 to 2019) was used to assess the evolution of salinization in Hetao, a two-thousand-year-old irrigation district in northern China. As intermediate variables of the proposed method, the crop-specific planting area was retrieved via its unique temporal vegetation index (VI) pattern, in which the shape-model-fitting technology and the K-means cluster algorithm were used. The desert area was stripped from the clustered non-vegetative area using its distinct features in the thermal band. Subsequently, the abandoned salinized farmland was distinguished from the urban area by the threshold-based saline index (SI). In addition, a regression model between electrical conductance (EC) and SI was established, and the spatial saline degree was evaluated by the SI map in uncropped and unfrozen seasons. The results show that the cropland has constantly been expanding in recent decades (from 4.7 × 105 ha to 7.1 × 105 ha), while the planting area of maize and sunflower has grown and the area of wheat has decreased. Significant desalinization progress was observed in Hetao, where both the area of salt-affected land (salt-free area increased approximately 4 × 105 ha) and the abandoned salinized farmland decreased (reduced from 0.45 × 105 ha to 0.19 × 105 ha). This could be mainly attributed to three reasons: the popularization of water-saving irrigation technology, the construction of artificial drainage facilities, and a shift in cropping patterns. The decrease in irrigation and the increase in drainage have deepened the groundwater table in Hetao, which weakens the salt collection capacity of the abandoned salinized farmland. The results demonstrate the promising possibility of reutilizing abandoned salinized farmland via a leaching campaign where the groundwater table is sufficiently deep to stop salinization.

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Assessing the Sentinel-2 Capabilities to Identify Abandoned Crops Using Deep Learning

Cited by 18 publications

References 28 publications

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

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

Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data

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