Optimizing Sentinel-2 feature space for improved crop biophysical and biochemical variables retrieval using the novel spectral triad feature selection algorithm

Abstract

This study presents a novel Spectral Triad feature selection (STfs) technique based on music theory and compares it to the entire Sentinel-2 feature space and Random Forest-Recursive Feature Elimination (RF-RFE). The optimal subsets were evaluated with Random Forest for retrieving Leaf Area Index (LAI), Leaf Chlorophyll Content (LC_ab), and Canopy Chlorophyll Content (CCC) in a semi-arid agricultural landscape. The results indicated that the proposed STfs algorithm obtained equivalent or better (i.e. by 1 – 3%) retrieval accuracies for LAI (R²_cv of 66%, root mean squared error of cross-validation [RMSE_cv] of 0.53 m² m⁻²), LC_ab (R²_cv: 74%, RMSE_cv: 7.09 µg cm⁻²) and CCC (R²_cv: 77%, RMSE_cv: 33.69 µg cm⁻²), using only 5, 7 and 7 variables, respectively, when compared to RF-RFE and entire Sentinel-2 feature space. Overall, the proposed STfs algorithm has great potential to optimize the spectral feature space of quasi-hyperspectral sensors for rapid crop biophysical and biochemical parameter retrieval.

Keywords:

• Remote sensing
  
• feature selection
• chlorophyll content
• leaf area index
• sentinel-2
• random forest
• biophysical and biochemical variables
  

Acknowledgement

The authors would like to acknowledge the European Space Agency (ESA) and Copernicus Programme for providing Sentinel-2 data free of charge. The data was accessed through Sentinel Hub Cloud API for Satellite Imagery provided by ESA Network of Resources (NoR) sponsorship. We also appreciate the EU-AfriCultuReS Project (GA: 774652) and the South African National Space Agency (SANSA) for providing field data for this study. Mahlatse Kganyago received University Research Committee (URC) Research Grant (Grant No. 2023URC00563) from University of Johannesburg Faculty of Science.

Disclosure statement

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