IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i8p1633-d1220587.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/8/1633/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/8/1633/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sa’ad Ibrahim, 2022. "Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Lands," Agriculture, MDPI, vol. 13(1), pages 1-22, December.
    2. Aymen Sawassi & Roula Khadra & Brian Crookston, 2024. "Water Banking as a Strategy for the Management and Conservation of a Critical Resource: A Case Study from Tunisia’s Medjerda River Basin (MRB)," Sustainability, MDPI, vol. 16(9), pages 1-20, May.
    3. Wenfei Luan & Ge Li & Bo Zhong & Jianwei Geng & Xin Li & Hui Li & Shi He, 2023. "Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network," Land, MDPI, vol. 12(8), pages 1-20, August.
    4. Pechan, Paul M. & Bohle, Heidi & Obster, Fabian, 2023. "Reducing vulnerability of fruit orchards to climate change," Agricultural Systems, Elsevier, vol. 210(C).
    5. Bright Chisadza & Onalenna Gwate & France Ncube & Nkululeko Mpofu, 2023. "Assessment and characterisation of hydrometeorological droughts in the Upper Mzingwane sub-catchment of Zimbabwe," 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 3275-3299, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1633-:d:1220587. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.