A comprehensive spectral-biogeochemical database was developed for the Wabash River and the Tippecanoe River in Indiana, United States. This database includes spectral measurements of river water, coincident in situ measurements of water quality parameters (chlorophyll (chl), non-algal particles (NAP), and colored dissolved organic matter (CDOM)), nutrients (total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC)), water-column inherent optical properties (IOPs), water depths, substrate types, and bottom reflectance spectra collected in summer 2014. With this dataset, the temporal variability of water quality observations was first analyzed and studied. Second, radiative transfer models were inverted to retrieve water quality parameters using a look-up table (LUT) based spectrum matching methodology. Results found that the temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions. Meanwhile, there were no significant correlations found between these parameters and streamflow for the Tippecanoe River, due to the two upstream reservoirs, which increase the settling of sediment and uptake of nutrients. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflow (CSO)), water temperature, and nutrients were important factors controlling instream concentrations of phytoplankton. The LUT retrieved NAP concentrations were in good agreement with field measurements with slope close to 1.0 and the average estimation error was 4.1% of independently obtained lab measurements. The error for chl estimation was larger (37.7%), which is attributed to the fact that the specific absorption spectrum of chl was not well represented in this study. The LUT retrievals for CDOM experienced large variability, probably due to the small data range collected in this study and the insensitivity of R_rs to CDOM change. It is concluded that the success of the LUT method requires accurate spectral measurements and enough a priori information of the environment to construct a representative database for water quality retrieval. Therefore, future work will focus on continuing data collection in other seasons of the year and better characterization of the study area.

A hand-held spectrometer was used to collect above-water spectral measurements for measuring optically active water-quality characteristics of the Wabash River and its tributaries in Indiana. Water sampling was undertaken concurrent with spectral measurements to estimate concentrations of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and Sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using the corrected field spectra and in situ chl and TSS data. A subset of the field measurements was used for model development and the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chl absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination (R²) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R² = 0.75). In addition, we also found that reflectance within the near-infrared (NIR) wavelengths (700–890 nm) all showed a strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Results suggest that hyperspectral information provided by field spectrometer can be used to distinguish and quantify water-quality parameters under complex IOP conditions.

The vertical diffuse attenuation coefficient $K_d$ ($\mathrm{PAR}$) is used for calculating the euphotic zone, the first optical depth that is important for primary productivity models. Currently, $K_d \left(\mathrm{PAR}\right)$ can be estimated using an irradiometer or a Secchi disk ($\mathrm{SD}$). The main objective of this work is to define a model that can be applied to a wide range of optical marine conditions to estimate $K_d \left(\mathrm{PAR}\right)$ by $\mathrm{SD}$. We used irradiance profiles and $\mathrm{SD}$ depth ($Z_{SD}$) from 679 stations in various marine regions. Three parametric models were developed, and their statistical performance was evaluated in view of previous approaches reported and remote sensing data. The best results were obtained with an adaptive model representing three cases: clear-water, turbid-water, and a transition zone (R² = 0.965, MAE = 0.083, RMSD = 0.239, BIAS = 0.01, and MPI = 0.854). Previous models considering a single optical depth figure at which the $SD$ disappears did not capture the marine optical complexity. Our classification of 113 stations with spectral absorption data into Jerlov water types indicated that no unique correspondence existed between estimated $K_d$ (PAR) and water type, making it ambiguous to associate compatible inherent optical properties and chlorophyll with $Z_{SD}$. Although obtaining $K_d$ ($\mathrm{PAR}$) from $Z_{SD}$ is simple/low-cost, care should be taken in the methodology used to measure $Z_{SD}$ to ensure consistent results across different optical marine conditions.

The seasonal and inter‐annual variability of photosynthetically available radiation (PAR) over the tropical oceans is examined using satellite imagery acquired from 1997 to 2017. Spatial and temporal biases between monthly PAR estimates from different instruments are determined and corrected, resulting in a consistent time series over the 20‐year record. Uncertainty is evaluated in comparisons with in situ measurements at various sites. Empirical orthogonal function (EOF) analysis is performed with both seasonal and non‐seasonal PAR signals, and linear trends are quantified. Seasonal cycles dominate PAR variability, with the first three seasonal EOF modes explaining 84.7% of the total variance. The seasonal patterns are related to solar position and monsoon. Canonical El Niño Southern Oscillation (ENSO) and Modoki ENSO are related to the two leading non‐seasonal EOF modes, with a correlation coefficient of 0.84 between the first mode and the multivariate ENSO Index and of 0.48 between the second mode and the El Niño Modoki Index. Trend analysis reveals that PAR tends to decrease by 0.2 %year^‐1 in the central Pacific north of the Equator and to increase by 0.2 %year^‐1 in the central Pacific around 5°S. The tendency is also for PAR to increase west of Central and South America. These changes are consistent with patterns of cloud change evidenced in the satellite cloud record and predicted by global climate models. The long‐term satellite PAR dataset, together with information of nutrient availability and temperature, enables further studies to elucidate the causes of phytoplankton variability in the tropical oceans.

Two semi-analytical algorithms, Generalized Inherent Optical Property (GIOP) and Garver-Siegel-Maritorena (GSM), were evaluated in terms of how well they reproduced the absorption coefficient of phytoplankton (a_ph(λ)) and dissolved and detrital organic matter (a_dg(λ)) at three wavelengths (λ of 412, 443, and 488 nm) in a zone with optically complex waters, the Upper Gulf of California (UGC) and the Northern Gulf of California (NGC). In the UGC, detritus determines most of the total light absorption, whereas, in the NGC, chromophoric dissolved organic material (CDOM) and phytoplankton dominate. Upon comparing the results of each model with a database assembled from four cruises done from spring to summer (March through September) between 2011 and 2013, it was found that GIOP is a better estimator for a_ph(λ) than GSM, independently of the region. However, both algorithms underestimate in situ values in the NGC, whereas they overestimate them in the UGC. Errors are associated with the following: (a) the constant a*_ph(λ) value used by GSM and GIOP (0.055 m² mgChla⁻¹) is higher than the most frequent value observed in this study’s data (0.03 m² mgChla⁻¹), and (b) satellite-derived chlorophyll a concentration (Chla) is biased high compared with in situ Chla. GIOP gave also better results for the a_dg(λ) estimation than GSM, especially in the NGC. The spectral slope S_dg was identified as an important parameter for estimating a_dg(λ), and this study’s results indicated that the use of a fixed input value in models was not adequate. The evaluation confirms the lack of generality of algorithms like GIOP and GSM, whose reflectance model is too simplified to capture expected variability. Finally, a greater monitoring effort is suggested in the study area regarding the collection of in situ reflectance data, which would allow explaining the effects that detritus and CDOM may have on the semi-analytical reflectance inversions, as well as isolating the possible influence of the atmosphere on the satellite-derived water reflectance and Chla.

The oceanic waters of the Southwest Tropical Pacific occupy a vast region including multiple Pacific Island Countries. The state of these waters is determinant for fisheries and the blue economy. Ocean color remote sensing is the main tool to survey the variability and long-term evolution of these large areas that are important for economic development but are affected by climate change. Unlike vast oligotrophic gyres, tropical waters are characterized by numerous archipelagos and islands, with deep and shallow lagoons subjected to the large impacts of the land. Strikingly large dendritic phytoplankton (Trichodesmium) blooms with high levels of chlorophyll, developing within archipelagos, as well as coastal enrichments from various origins may be observed. Algorithms to detect the presence of Trichodesmium have been developed or adapted, as well as algorithms to estimate the chlorophyll concentration ([Chl-a)]. Adapting existing [Chl-a] algorithms does not always yield high, i.e., sufficient, accuracy. A review of published regional bio-optical algorithms developed taking into account the specific phytoplankton composition and minimizing the adverse impacts of particles and the seabed bottom on [Chl-a] determination is presented, as well the bio-optical database that allowed their development. The interest of such algorithms for a variety of applications and scientific accomplishments is highlighted, with a view to further addressing the main biology and biogeochemistry questions, e.g., to determine the true impact of diazotrophs and assess lagoon [Chl-a] variability with the highest confidence. This work anticipates the use of future coarse and high-spatial-resolution and multi- and hyper-spectral satellite imagery in the Pacific.