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

Abstract

Comparing or integrating remote-sensing reflectance, Rrsλsr−1, data from multispectral sensors is hindered by differences in their nominal bands and relative spectral-response functions. Similarly, assessing satellite-to-in situ-matchup assessments of Rrs using autonomous ground radiometers, such as the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), faces the same challenges. Therefore, spectral band-shifting techniques are required to approximate Rrs in spectral bands that are mutually unavailable. We present a spectral-matching technique (SMTH) using a novel look-up-table (LUT) generated from Rrs obtained from the Hyperspectral Imager for the Coastal Ocean (HICO) sensor. This LUT consists of nearly 6.3 million unique Rrs spectra, processed from 8893 Level-2 HICO scenes. An extensive in situ hyperspectral dataset (n = 8289) was used to compare the performance of SMTH with three commonly used techniques, namely Melin and Sclep (2015) “MS-QAA” model, linear (LNR) and cubic spline (CS) interpolations. The comprehensive evaluation of eight sensors using in situ dataset revealed that the SMTH has significantly better overall accuracy with respect to spectral band-shifting than the existing approaches. The Bias was improved 7-fold, whereas the root-mean-square difference (RMSD) and mean absolute difference (MAD) were ∼ 15–60% lower. Even so, each model exhibits outstanding performance in certain bands. For instance, the LNR model excels in the 380 nm band, while the MS-QAA emerges as the optimal choice for the 400 and 412 nm bands. SMTH outperforms other models in the green-red region, notably above 600 nm, where most existing ocean-color sensors have a limited number of spectral bands. Despite this variability, all models, showcase satisfactory performance in the 443–560 nm region. Significantly, both SMTH and LNR demonstrated a 100% retrieval rate, the ratio of band-shifted samples to the input samples. In contrast, MS-QAA and CS returned invalid values for 7% and 39% of the input samples, respectively. Further evaluation of regional, global, and composite satellite data suggests that the trophic state significantly influences the model performance. The MS-QAA model exhibited the smallest differences within the oligotrophic waters, characterized by low chlorophyll-a concentrations. In contrast, the SMTH model demonstrates the lowest differences in mesotrophic/eutrophic waters and regions subject to harmful algal blooms. While HICO primarily sampled coastal waters, estuaries, and shallow regions from mid- to low latitudes, there is an opportunity to enhance the representativeness of our LUT by incorporating open-ocean and high-latitude Rrs spectra from the upcoming hyperspectral missions, such as NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE).