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Multi-Year Cereal Crop Classification Model in a Semi-Arid Region Using Sentinel-2 and Landsat 7–8 Data

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Listed:
  • Manel Khlif

    (LR17AGR01 InteGRatEd Management of Natural Resources: Remote Sensing, Spatial Analysis and Modeling (GREEN-TEAM), National Agronomic Institute of Tunisia, Carthage University, 43 Avenue Charles Nicolle, Tunis 1082, Tunisia)

  • Maria José Escorihuela

    (isardSAT, Technological Park, Marie Curie, 8-14, 08042 Barcelona, Spain)

  • Aicha Chahbi Bellakanji

    (LR17AGR01 InteGRatEd Management of Natural Resources: Remote Sensing, Spatial Analysis and Modeling (GREEN-TEAM), National Agronomic Institute of Tunisia, Carthage University, 43 Avenue Charles Nicolle, Tunis 1082, Tunisia)

  • Giovanni Paolini

    (isardSAT, Technological Park, Marie Curie, 8-14, 08042 Barcelona, Spain)

  • Zeineb Kassouk

    (LR17AGR01 InteGRatEd Management of Natural Resources: Remote Sensing, Spatial Analysis and Modeling (GREEN-TEAM), National Agronomic Institute of Tunisia, Carthage University, 43 Avenue Charles Nicolle, Tunis 1082, Tunisia)

  • Zohra Lili Chabaane

    (LR17AGR01 InteGRatEd Management of Natural Resources: Remote Sensing, Spatial Analysis and Modeling (GREEN-TEAM), National Agronomic Institute of Tunisia, Carthage University, 43 Avenue Charles Nicolle, Tunis 1082, Tunisia)

Abstract

This study developed a multi-year classification model for winter cereal in a semi-arid region, the Kairouan area (Tunisia). A random forest classification model was constructed using Sentinel 2 (S2) vegetation indices for a reference agricultural season, 2020/2021. This model was then applied using S2 and Landsat (7 and 8) data for previous seasons from 2011 to 2022 and validated using field observation data. The reference classification model achieved an overall accuracy (OA) of 89.3%. Using S2 data resulted in higher overall classification accuracy. Cereal classification exhibited excellent precision ranging from 85.8% to 95.1% when utilizing S2 data, while lower accuracy (41% to 91.8%) was obtained when using only Landsat data. A slight confusion between cereals and cereals growing with olive trees was observed. A second objective was to map cereals as early as possible in the agricultural season. An early cereal classification model demonstrated accurate results in February (four months before harvest), with a precision of 95.2% and an OA of 87.7%. When applied to the entire period, February cereal classification exhibited a precision ranging from 85.1% to 94.2% when utilizing S2 data, while lower accuracy (42.6% to 95.4%) was observed in general with Landsat data. This methodology could be adopted in other cereal regions with similar climates to produce very useful information for the planner, leading to a reduction in fieldwork.

Suggested Citation

  • Manel Khlif & Maria José Escorihuela & Aicha Chahbi Bellakanji & Giovanni Paolini & Zeineb Kassouk & Zohra Lili Chabaane, 2023. "Multi-Year Cereal Crop Classification Model in a Semi-Arid Region Using Sentinel-2 and Landsat 7–8 Data," Agriculture, MDPI, vol. 13(8), pages 1-21, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1633-:d:1220587
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    References listed on IDEAS

    as
    1. Dorte Verner & David Treguer & John Redwood & Jen Christensen & Rachael McDonnell & Christine Elbert & Yasuo Konishi, 2018. "Climate Variability, Drought, and Drought Management in Tunisia's Agricultural Sector," World Bank Publications - Reports 30604, The World Bank Group.
    2. Khouloud Abida & Meriem Barbouchi & Khaoula Boudabbous & Wael Toukabri & Karem Saad & Habib Bousnina & Thouraya Sahli Chahed, 2022. "Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas," Agriculture, MDPI, vol. 12(9), pages 1-13, September.
    3. Saleh Yousefi & Somayeh Mirzaee & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Christopher Gomez & Narges Siamian & Mona Alrasheedi & Hazem Ghassan Abdo, 2022. "Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters," Land, MDPI, vol. 11(7), pages 1-14, June.
    4. Rongchao Yang & Qingbo Zhou & Beilei Fan & Yuting Wang & Zhemin Li, 2023. "Land Cover Classification from Hyperspectral Images via Weighted Spatial–Spectral Joint Kernel Collaborative Representation Classifier," Agriculture, MDPI, vol. 13(2), pages 1-25, January.
    5. Jose Manuel Monsalve-Tellez & Jorge Luis Torres-León & Yeison Alberto Garcés-Gómez, 2022. "Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm," Agriculture, MDPI, vol. 12(7), pages 1-19, July.
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