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Combined Use of Landsat 8 and Sentinel 2A Imagery for Improved Sugarcane Yield Estimation in Wonji-Shoa, Ethiopia

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Abstract

In this study, a support vector regression (SVR) approach based on a radial basis function was used for estimating sugarcane yield in the Wonji-Shoa sugarcane plantation (Ethiopia) combining Landsat 8 (L8) and sentinel 2A (S2A) data. Vegetation Indices(VIs) involving visible, near-infrared, and shortwave infrared bands were calculated from the L8 and S2A sensor observations, and seasonal cumulative values were computed for the period June to October in the 9th month and June to November in the 10th month of the year for 2016/17 to 2018/19 cropping seasons. Sugarcane yield was predicted using the SVR, Multilayer perceptron neural network (MLPNN), and Multiple linear regression (MLR) methods. Then, a tenfold cross-validation approach was implemented for the performance evaluation. The results showed significant correlations between sugarcane yield and cumulative values of VIs computed during the 10th month in the growing season. The results also revealed that the estimation accuracy of sugarcane was better using the combined L8 and S2A (RMSE = 12.95 t/ha, and MAE = 10.14 t/ha) than using the S2A data alone (RMSE = 14.71 t/ha, and MAE = 12.18 t/ha). Comparing SVR results with MLPNN and MLR disclosed that SVR outperforms the other two models in terms of prediction accuracy. Overall, this study demonstrated the successful application of the SVR in developing a model for Sugarcane yield estimation and it may provide a guideline for improving the estimations of sugarcane in the study area.

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Acknowledgments

This study was conducted with funding provided by the Ethiopian Space Science and Technology Institute (ESSTI). The field-level sugarcane yield data were provided by the Wonji Sugarcane Research and Development Centre. The Copernicus Program of the European Space Agency (ESA) and the United States Geological Survey (USGS) Landsat Program are thanked for their open-source Sentinel-2A MSI and L8 OLI datasets, respectively. Our gratitude is also extended to the R Development Team for the open-source packages for the statistical analysis.

Funding

This study was funded by the Ethiopian Space Science and Technology Institute (ESSTI).

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Authors and Affiliations

  1. Department of Natural Resources Management, Debre Berhan University, Debre Berhan, Ethiopia

    Gebeyehu Abebe

  2. Department of Remote Sensing, Ethiopian Space Science and Technology Institute, P.O.Box: 33679, Addis Ababa, Ethiopia

    Gebeyehu Abebe & Berhan Gessesse

  3. National Drought Mitigation Center, University of Nebraska-Lincoln, Lincoln, USA

    Tsegaye Tadesse

  4. Department of Geography and Environmental Studies, Kotebe Metropolitan University, Addis Ababa, Ethiopia

    Berhan Gessesse

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  1. Gebeyehu Abebe
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Gebeyehu Abebe. The first draft of the manuscript was written by Gebeyehu Abebe and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Abebe, G., Tadesse, T. & Gessesse, B. Combined Use of Landsat 8 and Sentinel 2A Imagery for Improved Sugarcane Yield Estimation in Wonji-Shoa, Ethiopia. J Indian Soc Remote Sens 50, 143–157 (2022). https://doi.org/10.1007/s12524-021-01466-8

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