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Development of a Pre-Automatized Processing Chain for Agricultural Monitoring Using a Multi-Sensor and Multi-Temporal Approach

Author

Listed:
  • Emiliana Valentini

    (Institute of Polar Sciences of the National Research Council of Italy (ISP CNR), Montelibretti, 00015 Rome, Italy)

  • Serena Sapio

    (Institute for Advanced Studies of Pavia (IUSS), 27100 Pavia, Italy)

  • Emma Schiavon

    (Institute for Advanced Studies of Pavia (IUSS), 27100 Pavia, Italy)

  • Margherita Righini

    (Institute for Advanced Studies of Pavia (IUSS), 27100 Pavia, Italy)

  • Beatrice Monteleone

    (Institute for Advanced Studies of Pavia (IUSS), 27100 Pavia, Italy)

  • Andrea Taramelli

    (Institute for Advanced Studies of Pavia (IUSS), 27100 Pavia, Italy
    Institute for Environmental Protection and Research (ISPRA), 00144 Rome, Italy)

Abstract

Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector’s development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services.

Suggested Citation

  • Emiliana Valentini & Serena Sapio & Emma Schiavon & Margherita Righini & Beatrice Monteleone & Andrea Taramelli, 2024. "Development of a Pre-Automatized Processing Chain for Agricultural Monitoring Using a Multi-Sensor and Multi-Temporal Approach," Land, MDPI, vol. 13(1), pages 1-17, January.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:1:p:91-:d:1318188
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    References listed on IDEAS

    as
    1. Canone, D. & Previati, M. & Bevilacqua, I. & Salvai, L. & Ferraris, S., 2015. "Field measurements based model for surface irrigation efficiency assessment," Agricultural Water Management, Elsevier, vol. 156(C), pages 30-42.
    2. Beatrice Monteleone & Iolanda Borzí & Brunella Bonaccorso & Mario Martina, 2023. "Quantifying crop vulnerability to weather-related extreme events and climate change through vulnerability curves," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2761-2796, April.
    Full references (including those not matched with items on IDEAS)

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