Work

Advancing CyanoHAB monitoring with hyperspectral data from NASA PACE: First results and validation

Year: 2026

Type: article

Abstract: This study presents the first assessment of NASA PACE’s hyperspectral Ocean Color Imager (OCI) for cyanobacterial harmful algal bloom (cyanoHAB) monitoring, by a direct comparison with Sentinel-3’s multispectral Ocean and Land Color Instrument (OLCI), and its Cyanobacteria Assessment Network (CyAN) operational product. We analyzed OCI-OLCI coincident imagery from summer 2024 blooms in Lake Erie, Green Bay, and Clear Lake, evaluating the performance of both sensors using the established Cyanobacteria Index (CI_cyano) and corresponding cyanobacterial cell density (CCD) to estimate bloom biomass. PACE OCI successfully captured bloom patterns comparable to Sentinel-3 OLCI, including core bloom areas. When benchmarked against the CyAN product, OCI-derived CCD showed strong agreement (R² = 0.84, NRMSE = 8.95%), though a negative bias (β ≈ –11%) was observed for extreme bloom pixels. Validation with in situ measurements indicated that OCI significantly improved chlorophyll-a biomass retrievals compared to CyAN/OLCI (NRMSE = 21.57% for OCI vs 38.67% for CyAN/OLCI), emphasizing the value of hyperspectral observations for optically complex inland waters. This assessment provides a first step in establishing continuity between the long-running CyAN products and new PACE OCI data, further supporting water quality decision making. Beyond quantifying bloom magnitude, OCI’s hyperspectral data enables the identification of distinct cyanobacterial spectral profiles, showing potential for improved cyanobacteria pigment and biomass estimates as well as taxonomic and genus-level discrimination. Our results indicate PACE OCI offers significant promise for advancing cyanoHAB monitoring and water resource management, including the potential for delineating phytoplankton functional types and the presence of toxin-producing cyanobacteria.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.105032