An Ocean Color Algorithm Based on Power Functions to Retrieve Inherent Optical Properties From Remotely Sensed Data Off the Baja California Peninsula, Mexico

Ocean color is determined by the different components of seawater. Satellite ocean color data are commonly used to infer the inherent optical properties (IOPs) of seawater and concentration of chlorophyll $a$ (Chl- $a$ ). Many of the algorithms that relate satellite data to the components of seawater use global data to determine their parameters. Regionalizing the algorithms is recommended to improve the retrieval of IOPs from satellite data. In this article, we use the in situ and satellite data from the southern part of the California Current System (CCS) off the Baja California Peninsula during 1999 to test the Garver and Siegel IOPs’ inversion model (GSM). The strong seasonal variability observed in the data was not reproduced by this algorithm. Thus, we propose a new regional algorithm whose order and coefficient depend on seasonal variability. This algorithm includes the linear form of the GSM model but allows changes in two parameters according to the season. These two parameters are the magnitude and spectral shape of the relationship of the remote sensing reflectance with the ratio of the backscattering coefficient and the sum of backscattering and absorption coefficients. In this area, both parameters increase gradually from January to October. The new algorithm improves the retrieval of the IOPs’ ratio from an ${r}^{2}$ below 0.5 with the GSM model to an ${r}^{2}$ that ranges from 0.87 in April and October to 0.93 and 0.94 in January and August, respectively. Furthermore, Chl- $a$ data were used as a proxy to evaluate the new algorithm. Absorption data retrieved from remotely sensed data for 1999, 2008, and 2011 relate well to the in situ Chl- $a$ data.