We examined the effects of pheophytin a (Phe-a) on absorption properties of phytoplankton obtained from the red tide prone area of Dalian Bay in the North Yellow Sea, China, in June 2007 and July 2011. When the proportion of Phe-a relative to chlorophyll-a (Chl-a) increased, the absorption peak in the blue-violet region shifted from 438nm to 409nm. The relationship between blue-violet absorption maximum and Phe-a concentration is more tightly correlated than the relationship between blue-violet absorption maximum and Chl-a concentration. However, we found the opposite results for the red absorption maximum. These findings suggest that Phe-a mainly effects the blue-violet absorption maximum, while Chl-a primarily controls the red absorption maximum. Our results are determined by specific absorption coefficients of Phe-a and Chl-a in living algae cells. A multivariate linear regression analysis revealed that the specific absorption coefficients of Phe-a accounts for the absorption peaks at 407 nm and 673 nm, and specific absorption coefficient of Chl-a accounts for the absorption peaks at 440 and 673 nm, furthermore, the specific absorption coefficients of Phe-a are much larger than those of Chl-a in the blue-violet region, but they are lower in the red region.

Particulate organic carbon (POC) in the surface ocean contributes to understanding the global ocean carbon cycle system. The surface POC concentration can be effectively detected using satellites. In open oceans, the blue-to-green band ratio (BG) algorithm is often used to obtain global surface ocean POC concentrations. However, POC concentrations are underestimated in waters with complex optical environments. To generate a more accurate global POC mapping in the surface ocean, we developed a new ocean color algorithm using a mixed global-scale in situ POC dataset with the concentration ranging from 11.10 to 4389.28 mg/m³. The new algorithm (a-POC) was established to retrieve the POC concentration using the strong relationship between the absorption coefficient at 490 nm (a(490)) and POC, in which a(490) was from the Ocean Color Climate Change Initiative (OC-CCI) v5.0 suite. Afterward, the a-POC algorithm was applied to OC-CCI v5.0 data for special regions and the global ocean. The performances of the a-POC algorithm and the BG algorithm were compared by combining the match-ups of satellite data and in situ dataset. The results showed that the statistical parameters of the a-POC algorithm were similar to those of the BG algorithm in the Atlantic oligotrophic gyre regions, with a median absolute percentage deviation (MAPD) value of 22.04%. In the eastern coastal waters of the United States and the Chesapeake Bay, the POC concentration retrieved by the a-POC algorithm was highly consistent with the match-ups, and MAPD values were 33.06% and 26.11%. The a-POC algorithm was also applied to the Ocean and Land Color Instrument (OLCI) data pre-processed with different atmospheric correction algorithms to evaluate the universality. The result showed that the a-POC algorithm was robust and less sensitive to atmospheric correction than the BG algorithm.

Unmanned Aerial Vehicles (UAVs) have become powerful tools for high-resolution, quantitative remote sensing in ecological and environmental studies. In this study, we present a novel approach to accurately mapping and estimating the biomass of Suaeda salsa using UAV-based visible-light imagery combined with hue angle inversion modeling. By integrating diffuse reflectance standard plates into the flight protocol, we converted RGB pixel values into reflectance and derived hue angle metrics with enhanced radiometric accuracy. A hue angle cutoff threshold of 249.01° was identified as the optimal cutoff to distinguish Suaeda salsa from the surrounding land cover types with high confidence. To estimate biomass, we developed an exponential inversion model based on hue angle data calibrated through extensive field measurements. The resulting model—Biomass = 3.57639 × 10⁻¹⁵ × e^0.12201×α—achieved exceptional performance (R² = 0.99696; MAPE = 3.616%; RMSE = 0.02183 kg/m²), indicating strong predictive accuracy and robustness. This study highlights a cost-effective, non-destructive, and scalable method for the real-time monitoring of coastal vegetation, offering a significant advancement in remote sensing applications for wetland ecosystem management.

Corrigendum: Absorption-based algorithm for satellite estimating the particulate organic carbon concentration in the global surface ocean