A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: a machine learning approach

Htitiou, Abdelaziz; Boudhar, Abdelghani; Lebrini, Youssef; Hadria, Rachid; Lionboui, Hayat; Benabdelouahab, Tarik

doi:10.1080/10106049.2020.1768593

Cited by 32 publications

(11 citation statements)

References 97 publications

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“…This implies that apart from the crop traits presented here, not only other vegetation models (e.g., related to non-photosynthetic vegetation, Amin et al, 2021 ), but also those targeting other land cover types, such as models dedicated to the quantification of water variables (e.g., Ruescas et al, 2018 ) or soil properties (e.g., Vaudour et al, 2019 ) can be provided. Furthermore, the presented workflow can serve as a foundation for the computation of higher-level products, e.g., time series processing for the calculation of phenology metrics (e.g., Misra et al, 2020 ; Htitiou et al, 2020 ; Salinero-Delgado et al, 2021 ), fusion or assimilation of multiple products (e.g., Pipia et al, 2019 ; Schreier et al, 2021 ; Sadeh et al, 2021 ). At the same time, although this work focused on the processing of S2 TOA data, it must be emphasized that essentially the EBD-GPR retrieval models can be developed for any optical sensor data with the ALG-ARTMO software framework.…”

Section: Discussionmentioning

confidence: 99%

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Estévez

Salinero-Delgado

Berger

et al. 2022

Remote Sensing of Environment

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Section: Discussionmentioning

confidence: 99%

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Estévez

Salinero-Delgado

Berger

et al. 2022

Remote Sensing of Environment

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“… Ref. Approach Datasets Outcomes [ 28 ] CNN, ANN, SVM and RF Multi-source dataset (Spectroscopy, RGB and HS imageries) Robotic weed control system [ 29 ] RF Sentinel-2A time series OA (88%), kappa (0.84%) [ 30 ] SVM, DT, K-NN and ML Sentinel-2 images OA (77.2%) with SVM and RF. [ 31 ] DT Multi-source dataset (Multi-polarized SAR, Radarsat-2, and Sentinel-2) OA (66%) using single date Sentinel-2 with 2 date Sentinel-1, OA (89.5%) by incorporating Radarsat-2 data.…”

Section: Related Workmentioning

confidence: 99%

Investigation on the use of ensemble learning and big data in crop identification

Ahmed¹,

Mahmoud²,

Farg³

et al. 2023

Heliyon

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“…Data from remote sensing satellites provide large and continuous observations that characterize the changes occurring on the earth [11,12]. Indeed, time-series of satellite data are suitable to monitor the spatiotemporal behavior of plant phenology [13][14][15]. These issues motivate studying changes in farming systems to characterize the spatiotemporal variability that has occurred over long periods related to different drivers, i.e., short-and long-term weather events and public policies.…”

Section: Introductionmentioning

confidence: 99%

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

et al. 2021

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Changing land use patterns is of great importance in environmental studies and critical for land use management decision making over farming systems in arid and semi-arid regions. Unfortunately, ground data scarcity or inadequacy in many regions can cause large uncertainties in the characterization of phenological changes in arid and semi-arid regions, which can hamper tailored decision making towards best agricultural management practices. Alternatively, state-of-the-art methods for phenological metrics’ extraction and long time-series analysis techniques of multispectral remote sensing imagery provide a viable solution. In this context, this study aims to characterize the changes over farming systems through trend analysis. To this end, four farming systems (fallow, rainfed, irrigated annual, and irrigated perennial) in arid areas of Morocco were studied based on four phenological metrics (PhM) (i.e., great integral, start, end, and length of the season). These were derived from large Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series using both a machine learning algorithm and a pixel-based change analysis method. Results showed that during the last twenty-year period (i.e., 2000–2019), a significant dynamism of the plant cover was linked to the behavior of farmers who tend to cultivate intensively and to invest in high-income crops. More specifically, a relevant variability in fallow and rainfed areas, closely linked to the weather conditions, was found. In addition, significant lag trends of the start (−6 days) and end (+3 days) were found, which indicate that the length of the season was related to the spatiotemporal variability of rainfall. This study has also highlighted the potential of multitemporal moderate spatial resolution data to accurately monitor agriculture and better manage land resources. In the meantime, for operationally implementing the use of such work in the field, we believe that it is essential consider the perceptions, opinions, and mutual benefits of farmers and stakeholders to improve strategies and synergies whilst ensuring food, welfare, and sustainability.

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A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: a machine learning approach

Cited by 32 publications

References 97 publications

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Investigation on the use of ensemble learning and big data in crop identification

Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data

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