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A Phenology-Based Evaluation of the Optimal Proxy for Cropland Suitability Based on Crop Yield Correlations from Sentinel-2 Image Time-Series

Author

Listed:
  • Dorijan Radočaj

    (Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia)

  • Mladen Jurišić

    (Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia)

Abstract

Cropland suitability calculations quantify natural suitability according to abiotic conditions, thus making them crucial for sustainable land management. However, since ground-truth yield data are extremely scarce, there is a need to improve knowledge on the optimal proxy metric from satellite imagery, which represents cropland suitability and enables global applicability. This study evaluated four frequently used vegetation indices from Sentinel-2 image time-series (normalized difference vegetation index, enhanced vegetation index, enhanced vegetation index 2, and wide dynamic range vegetation index) with three phenology metrics for correlation analysis with maize and soybean yield. Four years (2019–2022) in two study areas (Iowa and Illinois) were utilized in this research, and 1000 ground-truth crop yield samples were created for each combination of study year and area. The combination of wide dynamic range vegetation index (WDRVI) and maximum vegetation index phenology metric (MAX) was an optimal proxy for maize yield prediction, while enhanced vegetation index 2 (EVI2) and MAX produced the highest correlation for soybean, producing Pearson’s correlation coefficient means of 0.506 and 0.519, respectively. This study improved our knowledge of the optimal proxy metric for cropland suitability by combining multiple large ground-truth crop yield datasets with 30 m spatial resolution satellite imagery, which can be further improved with the use of novel vegetation indices with improved resistance to a saturation effect.

Suggested Citation

  • Dorijan Radočaj & Mladen Jurišić, 2025. "A Phenology-Based Evaluation of the Optimal Proxy for Cropland Suitability Based on Crop Yield Correlations from Sentinel-2 Image Time-Series," Agriculture, MDPI, vol. 15(8), pages 1-14, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:8:p:859-:d:1635288
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    References listed on IDEAS

    as
    1. Dorijan Radočaj & Ante Šiljeg & Rajko Marinović & Mladen Jurišić, 2023. "State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review," Agriculture, MDPI, vol. 13(3), pages 1-16, March.
    2. Huizhu Wu & Bing Liu & Bingxue Zhu & Zhijun Zhen & Kaishan Song & Jingquan Ren, 2024. "Combining Vegetation Indices to Identify the Maize Phenological Information Based on the Shape Model," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
    3. Sergio Vélez & Raquel Martínez-Peña & David Castrillo, 2023. "Beyond Vegetation: A Review Unveiling Additional Insights into Agriculture and Forestry through the Application of Vegetation Indices," J, MDPI, vol. 6(3), pages 1-16, July.
    4. Pradosh Kumar Parida & Eagan Somasundaram & Ramanujam Krishnan & Sengodan Radhamani & Uthandi Sivakumar & Ettiyagounder Parameswari & Rajagounder Raja & Silambiah Ramasamy Shri Rangasami & Sundapalaya, 2024. "Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize," Agriculture, MDPI, vol. 14(7), pages 1-20, July.
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