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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network.

Mohammad Hajeb¹, Saeid Hamzeh¹, Seyed Kazem Alavipanah¹

¹Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran.

International Journal of Applied Earth Observation and Geoinformation : ITC Journal

|January 16, 2023

Summary

Bayesian Regularized Artificial Neural Networks (BRANN) accurately retrieve multiple sugarcane traits simultaneously from Sentinel-2 data. This method offers faster and more precise vegetation variable quantification for precision agriculture applications.

Keywords:

Bayesian regularization Multi-output ANN Sentinel-2 Sugarcane Vegetation parameter retrieval

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Published on: October 11, 2016

Area of Science:

Agricultural remote sensing
Biophysical and biochemical vegetation analysis
Machine learning applications in agriculture

Background:

Precision agriculture relies on accurate quantification of vegetation variables.
Leaf Area Index (LAI), leaf sheath moisture (LSM), leaf chlorophyll content (LCC), and leaf nitrogen concentration (LNC) are key sugarcane indicators.
Traditional methods for retrieving these variables can be time-consuming and less accurate.

Purpose of the Study:

To develop and evaluate a Bayesian Regularized Artificial Neural Network (BRANN) model for simultaneous retrieval of multiple sugarcane vegetation variables.
To assess the performance of BRANN compared to conventional Artificial Neural Networks (ANNs).
To map vegetation variables across sugarcane fields using Sentinel-2 imagery.

Main Methods:

Utilizing Sentinel-2 spectral data as input for the ANN models.
Employing Bayesian Regularized Artificial Neural Networks (BRANN) to mitigate overfitting and enhance generalizability.
Implementing a simultaneous retrieval approach for LAI, LSM, LCC, and LNC.
Comparing BRANN performance against ANNs trained with the Levenberg-Marquardt algorithm.

Main Results:

Achieved high accuracy with RMSE values of 0.48 (m²/m²) for LAI, 2.36 (% wb) for LSM, 5.85 (microg/cm²) for LCC, and 0.23 (%) for LNC.
Demonstrated that simultaneous retrievals outperformed individual retrievals.
Confirmed the superiority of BRANN over conventional ANNs through statistical analysis.
Generated maps showing reasonable spatial and temporal variations of the retrieved variables.

Conclusions:

BRANN enables accurate and simultaneous quantification of multiple sugarcane vegetation variables from Sentinel-2 data.
The simultaneous retrieval approach with BRANN is more efficient and accurate than individual retrievals and conventional ANNs.
This methodology holds significant potential for advancing precision agriculture through improved vegetation monitoring.