Spectral band-shifting of multispectral remote-sensing reflectance products: Insights for matchup and cross-mission consistency assessments (Dataset)

HICO Dataset Overview: The dataset archived here includes unique HICO Rrs dataset shown in the paper, “Spectral band-shifting of multispectral remote-sensing reflectance products: Insights for matchup and cross-mission consistency assessments”, published in Remote Sensing of Environment Journal (Salem et al., 2023). The dataset comprise of nearly 6.2 million unique remote-sensing reflectance (Rrs) records in the spectral range of 353–719 nm, along with 9 ancillary products. The unique Rrs of HICO dataset was extracted from 8,893 Hyperspectral Imager for the Coastal Ocean (HICO) images, captured from 2009 to 2014. HICO Dataset and Pre-processing: The HICO sensor, mounted on the International Space Station, collected hyperspectral data specifically for coastal ocean observation. It featured 65 spectral bands between 353 and 719 nm with a 5.7 nm sample interval in the open ocean, coastal waters, estuaries, and shallow regions. The HICO images were processed to filter out invalid or negative Rrs values across the 65 spectral bands of HICO data. An iterative process was employed to refine and extract unique Rrs data, resulting in a novel look-up table (LUT) consisting of nearly 6.2 million unique Rrs records. The LUT's Rrs spectra were further refined using cubic spline interpolation to adjust the sampling interval from 5.7 nm to 1 nm, resulting in a comprehensive LUT of 6,335,988 records spanning 367 bands (353–719 nm). Additionally, the dataset includes 9 ancillary products as follows: 'aot_868': Aerosol optical thickness at 868 nm, 'angstrom': Aerosol Angstrom exponent from 443 to 865 nm, 'chlor_a': Chlorophyll Concentration (mg m^-3), OCI Algorithm, 'chl_ocx': Chlorophyll Concentration (mg m^-3), OC4 Algorithm, 'Kd_490': Diffuse attenuation coefficient at 490 nm (m^-1) using KD2 algorithm, 'pic': Calcite Concentration (mol m^-3) using Balch and Gordon, 'poc': Particulate Organic Carbon (mg m^-3) using D. Stramski, 2007, 'longitude': Geographical longitude, 'latitude': Geographical latitude. Utilization of the Dataset: One of the application of this dataset is in the field of spectral band-shifting, a technique crucial for comparing and integrating Rrs data from different multispectral sensors. These sensors often vary in their spectral bands and response functions, posing challenges for consistent data analysis across different missions. The band-shifting technique, particularly the Spectral Matching Technique with HICO data (SMTH) approach, is extensively described in the paper titled "Spectral band-shifting of multispectral remote-sensing reflectance products: Insights for matchup and cross-mission consistency assessments" from Remote Sensing of Environment (RSE) https://doi.org/10.1016/j.rse.2023.113846. This technique is pivotal in approximating Rrs in bands that are not commonly available across various sensors, enhancing data compatibility and accuracy. Available files: HICO_LookUpTable_Rrs1nm_Ancillary_6M.nc: netCDF file including 6,201,385 unique HICO records with 367 Rrs bands and 9 ancillary products. Read_HICO_Dataset.py: Python script for reading the HICO unique reflectance data. SMTH_Rrs_BandShift.py: Python script to conduct band-shifting using the SMTH approach. Sample_Input.csv: Sample data file for testing the SMTH approach. GitHub: For detailed instructions on setting up the Python environment and running the script, please visit our GitHub repository at SMTH Rrs BandShifting. This link provides comprehensive guidance on environment setup, dependencies, and script execution. Data Use Statement: This work is made available under the terms of the Creative Commons Attribution 4.0 International License [Link]. In cases where the HICO data product is integral to your work, or a significant outcome or conclusion relies on it, we would value the opportunity to discuss these results with you. This ensures the accurate usage and interpretation of the data. Additionally, as we are continually enhancing the Unique HICO data, engaging with us early in your project could (i) contribute to the improvement of our product, and (ii) potentially allow us to offer you a more updated version of the data. Thank you for your collaboration!