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Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring

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Information and Communication Technologies for Agriculture—Theme I: Sensors

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 182))

Abstract

National and International space agencies are determined to keep their fingers on the pulse of crop monitoring through Earth Observation (EO) satellites, which is typically tackled with optical imagery. In this regard, there has long been a trade-off between repetition time and spatial resolution. Another limitation of optical remotely sensed data is their typical discontinuity in time, caused by cloud cover or adverse atmospheric effects. Enduring clouds over agricultural fields can mask key stages of crop growth, leading to uncertainties in crop monitoring practices such as yield predictions. Gap-filling methods can provide a key solution for accurate crop phenology characterization. This chapter first provides a historical overview of EO missions dedicated to crop monitoring. Then, it addresses the rapidly evolving fields of gap-filling and land surface phenology (LSP) metrics calculation using a new in-house developed toolbox, DATimeS. These techniques have been put into practice for homogeneous and heterogeneous demonstration landscapes over the United States. Time series of Difference Vegetation Index (DVI) were processed from two EO data sources: high spatial resolution Sentinel-2 and, low spatial resolution MODIS data. LSP metrics such as start and end of season were calculated after gap filling processing at 1km resolution. Over the homogeneous area both S2 and MODIS are well able to capture the phenology trends of the dominant crop and LSP metrics were successfully mapped. Conversely, the MODIS dataset presented more difficulties than S2 to capture the phenology trend of winter wheat over heterogeneous landscape.

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Notes

  1. 1.

    http://www.worldstopexports.com/wheat-exports-country/

  2. 2.

    http://www.nass.usda.gov/Quick_Stats/

  3. 3.

    https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table

  4. 4.

    https://www.planet.com/

  5. 5.

    The toolbox can be freely downloaded at https://artmotoolbox.com/

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Acknowledgments

This chapter was funded by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project (grant agreement 755617) and supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu).” Belen Franch and Santiago Belda were also supported by the program Generacio Talent of Generalitat Valenciana (CIDEGENT/2018/009 and SEJIGENT/2021/001, respectively), and Jochem Verrelst by Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities).

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Authors and Affiliations

  1. Institut Cartogràfic i Geològic de Catalunya (ICGC), Parc de Montjüic, Barcelona, Spain

    Luca Pipia

  2. Image Processing Laboratory (IPL), Universitat de València. C/Catedrático José Beltrán, Paterna, Valencia, Spain

    Luca Pipia, Santiago Belda, Belen Franch & Jochem Verrelst

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  1. Luca Pipia
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  2. Santiago Belda
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  3. Belen Franch
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  4. Jochem Verrelst
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Correspondence to Jochem Verrelst .

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Editors and Affiliations

  1. Institute for Bio-economy and Agri-technology (iBO), Maria Laura Delle Monache, Department of Civil and Environmental Engineering, University of California, Berkeley, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Dionysis D. Bochtis

  2. Institute for Bio-economy and Agri-technology (iBO), Maria Laura Delle Monache, Department of Civil and Environmental Engineering, University of California, Berkeley, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Maria Lampridi

  3. Department of Geography, Harokopio University of Athens, Athens, Greece

    George P. Petropoulos

  4. Department of Agricultural and Biological Engineering, University of Florida, Immokalee, FL, USA

    Yiannis Ampatzidis

  5. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos Pardalos

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Pipia, L., Belda, S., Franch, B., Verrelst, J. (2022). Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring. In: Bochtis, D.D., Lampridi, M., Petropoulos, G.P., Ampatzidis, Y., Pardalos, P. (eds) Information and Communication Technologies for Agriculture—Theme I: Sensors. Springer Optimization and Its Applications, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-84144-7_8

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