Automating band selection for hyperspectral indices: bridging AVIRIS-NG and Sentinel-2 satellite data for earth science applications

Abstract

Hyperspectral data from the Airborne Visible and Infra-Red Imaging Spectrometer – Next-Generation (AVIRIS-NG) offers transformative potential for Earth science research, enabling detailed analysis of land surface processes, resource monitoring, and environmental dynamics. This study presents an automated methodology to optimize the selection of AVIRIS spectral bands, improving the computation of indices critical to Earth science applications. By leveraging multiple hyperspectral bands, the approach enhances the accuracy of indices used to monitor water resources, vegetation health, urban expansion, and built-up areas. The methodology involves calculating indices from all possible AVIRIS band combinations, evaluating their root mean squared error (RMSE) against Sentinel-2 indices, reducing RMSE skewness, and selecting bands with minimal deviation for specific Land Use Land Cover (LULC) categories. The process is automated and employs parallel processing with Python, significantly reducing execution time and enabling scalability for large geospatial datasets. Key indices, including the Normalized Difference Water Index (NDWI), Normalized Difference Red Edge (NDRE), and Normalized Difference Built-up Index (NDBI), Green Normalized Difference Vegetation Index (GNDVI) were validated using the proposed methodology. Results demonstrate the potential of hyperspectral data to outperform traditional single-band approaches, providing more precise and reliable assessments.