Estimating Leaf Area Index and biomass of sugarcane based on Gaussian process regression using Landsat 8 and Sentinel 1A observations

ABSTRACT

Accurate estimation of crop parameters, such as Leaf Area Index (LAI) and biomass over large areas using remote sensing techniques, is crucial for monitoring crop growth and yield prediction. In this study, a Gaussian Process Regression (GPR) method was developed to estimate LAI and biomass values of sugarcane during growth season using optical and synthetic-Aperture Radar (SAR) data fusion. Predicting LAI on an independent test data set using the GPR and the combined optical and SAR indices provided better prediction accuracies of LAI; with the GPR based on radial basis function (Root Mean Square Error [RMSE] = 0.34, Mean Absolute Error [MAE] = 0.28 and Mean Absolute Percentage Error [MAPE] = 10.5%) and polynomial function (RMSE = 0.42, MAE = 0.31 and MAPE = 12.58%), respectively. The test results of sugarcane biomass also showed that the GPR (poly) produced the highest statistical results (RMSE = 2.45 kg/m², MAE = 1.72 kg/m², MAPE = 8.1%) using the combined indices. The results suggest that the crop biophysical retrieval based on optical and SAR data fusion and GPR proposed in this study could improve LAI and biomass estimation that could help for effective crop growth monitoring and mapping applications.

KEYWORDS:

• Biomass
• Gaussian process regression
• Landsat 8
• LAI
• relevance vector machine
• Sentinel 1A

Acknowledgments

The authors thank the Ethiopian Space Science and Technology Institute for financially supporting this study. The authors also thank the Alaska Satellite Facility (ASF) and United States Geological Survey (USGS) for providing Sentinel 1A and Landsat 8 OLI data, respectively. The authors appreciate the support of the Wonji sugarcane research centre staffs that helped in the field surveys and data collection. Our gratitude is also extended to the R Development Team for the open-source packages used in the statistical analysis. The authors are grateful to the editor and the anonymous reviewers for their insightful and valuable comments that significantly contributed for improving and clarifying the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors’ contribution

All authors contributed to the study conception and design. GA analyses and interprets the data and wrote the manuscript. TT and BG revised and edited the manuscript. All authors read and approved the final manuscript.

Data availability statement

The data that support the findings of this study are available from the corresponding author [[email protected]] upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

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