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The Potential of Using Radarsat-2 Satellite Image for Modeling and Mapping Wheat Yield in a Semiarid Environment

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  • Meriem Barbouchi

    (Laboratoire Sciences et Techniques Agronomiques (LR16 INRAT 05), INRAT, University of Carthage, Tunis 1004, Tunisia)

  • Rachid Lhissou

    (Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec, QC G1K 9A9, Canada)

  • Riadh Abdelfattah

    (Department of COSIM Lab, Higher School of Communications of Tunis, University of Carthage, Tunis 1004, Tunisia
    Department of ITI, IMT-Atlantique Bretagne-Pays de la Loire, CEDEX 03, 29238 Brest, France)

  • Anas El Alem

    (Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec, QC G1K 9A9, Canada)

  • Karem Chokmani

    (Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec, QC G1K 9A9, Canada)

  • Nadhira Ben Aissa

    (National Agronomic Institute of Tunisia (INAT), University of Carthage, Tunis 1004, Tunisia)

  • Hatem Cheikh M’hamed

    (Laboratoire Sciences et Techniques Agronomiques (LR16 INRAT 05), INRAT, University of Carthage, Tunis 1004, Tunisia)

  • Mohamed Annabi

    (Laboratoire Sciences et Techniques Agronomiques (LR16 INRAT 05), INRAT, University of Carthage, Tunis 1004, Tunisia)

  • Haithem Bahri

    (Laboratoire Sciences et Techniques Agronomiques (LR16 INRAT 05), INRAT, University of Carthage, Tunis 1004, Tunisia)

Abstract

The monitoring of cereal productions, mainly through yield estimations, has played an important role in providing reliable information to decision makers in order to ensure the proper management of agricultural markets. In this context, remote sensing, which allows the coverage of large areas, is an important source of information that complements those obtained by other methods. In this study, we aim to estimate the wheat yield at an early growth stage (spring season) using only one Radarsat-2 (RS-2) polarimetric image. We propose an empirical statistical relationship between the yield measured in situ and polarimetric parameters extracted from the RS-2 image. The RS-2 image was acquired at the flowering stage as it is proved to be the most appropriate moment for yield prediction. We selected the region of Boussalem in the northwest of Tunisia as the study area. For experimental validation, the yield was determined in situ at the end of the wheat season. Results showed that the polarization ratios are more correlated than the polarimetric parameters with the grain yield with a significant correlation of the HH/VV ratio (r = 0.76) and the HV/VV ratio (r = −0.75), while the most correlated polarimetric parameter was Alpha (r = −0.51). Finally, the multiple regression has led to the development of a three-variable model (HH/VV, HV/HH, and alpha) as the best predictor of the wheat grain yields. Validation results revealed a great potential with a determination coefficient (R 2 ) of 0.58 and root mean squared error (RMSE) of 0.89 t/ha.

Suggested Citation

  • Meriem Barbouchi & Rachid Lhissou & Riadh Abdelfattah & Anas El Alem & Karem Chokmani & Nadhira Ben Aissa & Hatem Cheikh M’hamed & Mohamed Annabi & Haithem Bahri, 2022. "The Potential of Using Radarsat-2 Satellite Image for Modeling and Mapping Wheat Yield in a Semiarid Environment," Agriculture, MDPI, vol. 12(3), pages 1-15, February.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:315-:d:755577
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    References listed on IDEAS

    as
    1. Norazlida Jamil & Gert Kootstra & Lammert Kooistra, 2022. "Evaluation of Individual Plant Growth Estimation in an Intercropping Field with UAV Imagery," Agriculture, MDPI, vol. 12(1), pages 1-23, January.
    2. Ali Zamani & Alireza Sharifi & Shilan Felegari & Aqil Tariq & Na Zhao, 2022. "Agro Climatic Zoning of Saffron Culture in Miyaneh City by Using WLC Method and Remote Sensing Data," Agriculture, MDPI, vol. 12(1), pages 1-15, January.
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