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Carbon dioxide (CO2) is the most important greenhouse gas in the atmosphere, playing a crucial role in the greenhouse effect and climate change. Lidar, with its high spatiotemporal resolution and high-precision detection capabilities, has become an essential tool for remote sensing of CO2. However, precise temperature information is required for CO2 retrieval. Studies showed that for both differential absorption lidar (DIAL) and spectroscopic lidar, a CO2 concentration measurement error of 2.0–3.5 ppm would result from each 1 K temperature deviation. Therefore, using nonreal-time and non in situ temperature data can lead to significant CO2 retrieval errors. In this study, a column-averaged CO2 spectroscopy lidar is proposed, which enables simultaneous measurements of CO2 concentration, temperature, and semi-heavy water (HDO, isotopic water vapor). First, a model combining five Lorentzian functions with a binomial background was proposed through spectral decomposition. Second, through theoretical analysis, the fitting parameters were reduced from 18 to 5. Finally, theoretical analysis shows that the model achieves system biases of less than 0.1 ppm for CO2, 0.1 K for temperature, and 0.06 ppm for HDO. Considering Poisson noise, the error distributions of CO2, temperature, and HDO under different optical distances and signal-to-noise ratios (SNRs) were studied. This technology will advance the development of CO2 flux remote sensing and is expected to play a crucial role in ecosystem research, atmospheric environmental monitoring, and greenhouse gas emission reduction policies.

Detection of the effects of climate change on ocean ecosystems is often limited by the short duration of available time series. Here, we use ocean transparency measurements (the Secchi disk depth, ZSD) in the California Current Ecosystem since 1949 and combine them with satellite estimates. Historic in situ measurements of ZSDwere irregular in space and time and are difficult to interpret in time series due to biases introduced by changing locations and timing. We normalize historic ZSDmeasurements with satellite‐derived mean climatology and create a merged in situ—satellite time series of ZSDfor the last ~ 73 yr. Although interannual variability in ZSDis dominated by El Niño Southern Oscillation‐related variability (~ 50% of the total variance in many areas), a secular trend of decreasing transparency that is correlated with increasing productivity is detected 0–300 km from the coast in an area affected by coastal upwelling north of 27°N. In contrast, increasing transparency (correlated with decreasing productivity) is detected offshore (> 1000 km from the coast). In addition to those general trends, transparency is also increasing in coastal area off Baja California south of 27°N.

Primary production (PP) of the South China Sea (SCS) basin area (waters depth deeper than 200 m) is estimated using satellite products, with an overarching goal to reliably characterize the spatial distribution and temporal variation of PP of this important marginal sea. Among the PP models used, the absorption-based model (AbPM) showed better performance ( $R^{2}=0.47$ and $N =39$ ). In comparison, the $R^{2}$ value is 0.26 for a chlorophyll-based model [vertically generalized production model (VGPM)] and 0.15 for the carbon-based model (CbPM). Furthermore, we observed that the PP spatial patterns obtained from these models were similar but disagree on the annual PP magnitude, where VGPM and CbPM, respectively, obtained $\sim $ 50% lower and $\sim $ 40% higher annual PP compared to that obtained by AbPM. In particular, after analysis using empirical orthogonal functions (EOFs), the upwelling-induced high PP off Luzon (winter) and Vietnam coast (summer) was clearly reflected in the first EOF mode of the AbPM results, and its principal component 1 has shown a decreasing trend for the period of 2003–2019 (−15.0% yr $^{-1}$ for winter, $p < 0.05$ ; −14.7% yr $^{-1}$ for summer, $p < 0.05$ ), which reflects the impact of weakening wind and higher sea surface temperature in the SCS. For the results of VGPM and CbPM, however, no strong relationships were found with the main regional oceanographic features. These results suggested that the spatiotemporal variations of SCS PP obtained from AbPM are more reasonable and further highlight the importance of a robust model in reliably capturing large-scale spatiotemporal dynamics of PP in marine environments.

Algal blooms constitute an emerging threat to global inland water quality, yet their spatial and temporal distribution at the global scale remains largely unknown. Here we establish a global bloom database, using 2.91 million Landsat satellite images from 1982 to 2019 to characterize algal blooms in 248,243 freshwater lakes, representing 57.1% of the global lake area. We show that 21,878 lakes (8.8%) spread across six continents have experienced algal blooms. The median bloom occurrence of affected lakes was 4.6%, but this frequency is increasing; we found increased bloom risks in the 2010s, globally (except for Oceania). The most pronounced increases were found in Asia and Africa, mostly in developing countries that remain reliant on agricultural fertilizer. As algal blooms continue to expand in scale and magnitude, this baseline census will be vital towards future risk assessments and mitigation efforts.

For waters with stratified chlorophyll concentration (Chl), numerical simulations were carried out to gain insight into the forward models of subsurface reflectance and empirical algorithms for Chl from the ocean color. It is found that the Gordon and Clark (1980) forward model for reflectance using an equivalent homogeneous water with a weighted average Chl (?Chl?) as the input works well, but depending on the contribution of gelbstoff, the difference in reflectance between stratified and the equivalent homogeneous water can be more than 10%. Further, the attenuation of upward light is better approximated as ∼1.5times that of the diffuse attenuation coefficient of downwelling irradiance. On the other hand, although the forward model for reflectance developed in Zaneveld et al. [Opt. Express13, 9052 (2005)] using equivalent homogeneous water with a weighted average of the backscattering to absorption ratio as the input also works well, this model cannot be used to obtain equivalent ?Chl? for reflectance. Further, for empirical Chl algorithms designed for “Case 1” waters, it has been discovered that, for surface Chl in a range of ∼0.06−22.0mg/m^3, the predictability of surface Chl is basically the same as that of ?Chl? from the blue-green band ratio or the band difference of reflectance. Because ?Chl? is wavelength and weighting-formula dependent, and it is required to have profiles of both Chl and the optical properties, these results emphasize that for empirical Chl algorithms, it is easier, less ambiguous, and certainly more straightforward and simple to use surface Chl for algorithm development and then its evaluation, rather than to use ?Chl?, regardless of whether or not the water is stratified.

Accurate determination of the water-leaving radiance (L_w) is key to correctly interpret in-water optical properties and to validate the atmospheric correction schemes in ocean color studies. Among the various approaches adopted to measure L_w in the field, the skylight-blocked approach (SBA) is the only scheme that can potentially measure L_w directly. However, the apparatus associated with an SBA system will introduce self-shading effects to the measured L_w, which is required to be corrected for an accurate L_w determination. In this study, we experimentally evaluate several factors that could contribute to the self-shading effects of the SBA-measured L_w, including solar zenith angle (∼18^∘−64^∘), water’s optical properties, and cone size (radius of 22 mm and 45 mm). For waters with the total absorption coefficient at 440 nm as high as ∼6.0m^−1, the normalized root-mean-square difference between the SBA-measured L_w after shade correction and the “true” L_w is generally between ∼5% and ∼10% for wavelengths in the range of 400–750 nm. These results suggest that SBA can obtain highly accurate and precise L_w in nearly all natural aquatic environments.

Total and polarized radiances from above the ocean surface are measured by a state-of-the-art snapshot hyperspectral imager. A computer-controlled filter wheel is installed in front of the imager allowing for recording of division-of-time Stokes vector images from the ocean surface. This system, to the best of our knowledge, for the first time provided a capability of hyperspectral polarimetric multi-angular measurements of radiances from above the water surface. Several sets of measurements used in the analysis were acquired from ocean platforms and from shipborne observations. Measurements made by the imager are compared with simulations using a vector radiative transfer (VRT) code showing reasonable agreement. Analysis of pixel-to-pixel variability of the total and polarized above-water radiance for the viewing angles of 20°–60° in different wind conditions enable the estimation of uncertainties in measurements of these radiances in the polarized mode for the spectral range of 450–750 nm, thus setting requirements for the quality of polarized measurements. It is shown that there is a noticeable increase of above-water degree of linear polarization (DoLP) as a function of the viewing angle, which is due both to the larger DoLP of the light from the water body and the light reflected from the ocean surface. Results of measurements and VRT simulations are applied for the multi-angular retrieval of the ratio of beam attenuation coefficient (${c_\textrm{tot}}$c_tot) to absorption coefficient (${a_\textrm{tot}}$a_tot) in addition to the other parameters such as absorption and backscattering coefficients retrieved from traditional unpolarized methods.

Low-power, lightweight, off-the-shelf imaging spectrometers, deployed on above-water fixed platforms or on low-altitude aerial drones, have significant potential for enabling fine-scale assessment of radiometrically derived water quality properties (WQPs) in oceans, lakes, and reservoirs. In such applications, it is essential that the measured water-leaving spectral radiances be corrected for surface-reflected light, i.e., glint. However, noise and spectral characteristics of these imagers, and environmental sources of fine-scale radiometric variability such as capillary waves, complicate the glint correction problem. Despite having a low signal-to-noise ratio, a representative lightweight imaging spectrometer provided accurate radiometric estimates of chlorophyll concentration—an informative WQP—from glint-corrected hyperspectral radiances in a fixed-platform application in a coastal ocean region. Optimal glint correction was provided by a spectral optimization algorithm, which outperformed both a hardware solution utilizing a polarizer and a subtractive algorithm incorporating the reflectance measured in the near infrared. In the same coastal region, this spectral optimization approach also provided the best glint correction for radiometric estimates of backscatter at 650 nm, a WQP indicative of suspended particle load.

Analytical expressions between inherent optical properties (IOPs) retrieved using semi‐analytical algorithms (SAAs) and the vertical distributions of IOPs are derived based on an analytical model for subsurface remote sensing reflectance (rrs). These expressions provide a theoretical and comprehensive understanding of the inverted IOPs for stratified waters and lay the foundation to obtain equivalent products from profiling measurements for apple‐to‐apple comparison of the remotely sensed properties. It is found that the backscattering coefficient (bb) derived from an rrsspectrum via an SAA is governed by rrsin the longer wavelengths, consequently the inverted bbhas less (or negligible) contributions from deeper depths, such as the subsurface chlorophyll maximum. In addition, the inverted bbspectrum does not have the spectral feature of the vertically weighted average bb. The SAA‐derived absorption coefficients, on the other hand, are found related to both the weighting profile at the wavelength of interest and the weighting profile in the longer wavelength(s). As a result, the absorption coefficients in the shorter wavelengths inverted from a perfect SAA are generally lower (can be 40% or more) than its vertically weighted average unless the surface layer dominates the diffuse attenuation of light. Furthermore, unlike the historical perception of remotely sensed chlorophyll concentration (Chl) for stratified waters, SAA‐derived Chl is also sensitive to the vertical profile of bband tends to be lower than its vertically weighted average assuming perfect bio‐optical relationships, unless surface properties dominate the water column.

The Operational Land Imager (OLI) onboard Landsat 8 has potential for mapping the water bio‐optical properties with high spatial resolution. Landsat 8/OLI generates the remote sensing reflectance (Rrs) at four visible bands (λ1‐4= 443, 482, 561, and 655 nm) and is lack of a 412‐nm band commonly included for ocean color sensors. This spectral configuration has limited the use of Landsat 8/OLI reflectance product for analytical derivation of light absorption coefficients of phytoplankton, colored dissolved organic matter (CDOM), and detritus. In this study, we proposed a hybrid approach to fill this gap. First, we developed an algorithm to estimate the reflectance in a virtual band centered at λ0= 412 nm from the OLI reflectance spectra Rrs(λ1‐4). Both the estimated Rrs(λ0) and measured Rrs(λ1‐4) were then used together to retrieve the water component absorption coefficients with existing algorithms including the quasi‐analytical algorithm. We assessed the model performance using in situ measurements from the global waters. It was found that the proposed approach could estimate Rrs(412) with a median absolute percentage difference of ~9%. The subsequent retrievals of the component absorption coefficients were satisfactorily accurate, with median absolute percentage difference roughly equal to 35%, 40%, and 60% for phytoplankton, CDOM, and detritus, respectively. The results suggest the feasibility to generate analytically the component absorption coefficients from the Landsat 8/OLI reflectance. The remote sensing reflectance (Rrs) in a virtual 412‐nm band can be estimated for Landsat 8The estimation of Rrs(412) allows to semianalytically derive the absorption coefficients of water components from Landsat 8 reflectanceThe phytoplankton, CDOM, and detritus absorption coefficients derived from simulated Landsat 8 data are satisfactorily accurate

To achieve a smooth transition between algorithms for “clear” water and “turbid” water, we propose a single formula to calculate the input parameter (ip) used for empirical retrieval of absorption coefficients (a) or chlorophyll concentration ([Chl]) from remote-sensing reflectance (R_rs). This formula for ip takes the ratio of the maximum R_rs in the blue-green bands to the sum of R_rs(green) and the scaled R_rs in the red and infrared bands (termed as ip_Max-Sum). We found that, compared to the widely used OC4-type formula for ip, ip_Max-Sum can improve the coefficient of determination from ∼0.88 to 0.99 for absorption coefficient at 440 nm [a(440)] in ∼0.01–20.0  m^–1 ([Chl] ∼0.01–500  mg m^–3). Especially, the sensitivity of ip_Max-Sum to the change in a(440) is about five times greater than that of OC4-type for a(440)>∼1.0  m^−1 ([Chl]>∼10  mg m^−3). These results indicate an advantage of ip_Max-Sum for generating robust and seamless a(440) or [Chl] from clear to highly turbid waters. The inclusion of such a scheme in a quasi-analytical algorithm is also presented.

In ocean-color remote sensing, subsurface remote-sensing reflectance (r_rs) of optically deep waters can be linked to its absorption (a) and backscattering coefficients (b_b) by various models. The use of such models allows for quick calculations r_rs from such coefficients, eliminating the need to solve the radiative transfer equation. In particular, r_rs can be expressed as a function of b_b/(a+b_b). HydroLight and Monte Carlo simulations showed that commonly used models underestimate r_rs in waters with high suspended sediment loads. Monte Carlo simulations confirmed that this issue is due to a sharp increase in multiple scattering events at high turbidity levels. A quartic polynomial model is derived relating r_rs and inherent optical properties (IOPs) for waters of any turbidity, to avoid significant errors in waters of high turbidity.

A recently developed algorithm to estimate surface ocean chlorophyll aconcentrations (Chl in mg m−3), namely, the ocean color index (OCI) algorithm, has been adopted by the U.S. National Aeronautics and Space Administration to apply to all satellite ocean color sensors to produce global Chl maps. The algorithm is a hybrid between a band‐difference color index algorithm for low‐Chl waters and the traditional band‐ratio algorithms (OCx) for higher‐Chl waters. In this study, the OCI algorithm is revisited for its algorithm coefficients and for its algorithm transition between color index and OCx using a merged data set of high‐performance liquid chromatography and fluorometric Chl. Results suggest that the new OCI algorithm (OCI2) leads to lower Chl estimates than the original OCI (OCI1) for Chl < 0.05 mg m−3, but smoother algorithm transition for Chl between 0.25 and 0.40 mg m−3. Evaluation using in situ data suggests that similar to OCI1, OCI2 has significantly improved image quality and cross‐sensor consistency between SeaWiFS, MODISA, and VIIRS over the OCx algorithms for oligotrophic oceans. Mean cross‐sensor difference in monthly Chl data products over global oligotrophic oceans reduced from ~10% for OCx to 1–2% for OCI2. More importantly, data statistics suggest that the current straylight masking scheme used to generate global Chl maps can be relaxed from 7 × 5 to 3 × 3 pixels without losing data quality in either Chl or spectral remote sensing reflectance (Rrs(λ), sr−1) for not just oligotrophic oceans but also more productive waters. Such a relaxed masking scheme yields an average relative increase of 39% in data quantity for global oceans, thus making it possible to reduce data product uncertainties and fill data gaps. There are generally two issues with any remote sensing data product: data quality (accuracy) and data quantity (coverage). In this work, these two issues for global ocean data products of chlorophyll aconcentrations (Chl in mg m−3) are investigated through revisiting a recently developed algorithm concept and statistical analyses of cloud‐adjacent data. The use of more data in algorithm development leads to slightly different algorithm coefficients and smoother transition between clear and turbid waters, and a new straylight masking scheme is proposed to “recover” some of the previously masked data in the global data products. The new algorithm leads to significantly improved cross‐sensor consistency (SeaWiFS, MODIS, VIIRS) as compared to the traditional band‐ratio algorithms, with mean monthly difference reduced from 10% to 1–2%. The new straylight masking scheme leads to relative increase of 39% in data quantity in the global ocean without losing data quality. These improvements are expected to reduce uncertainties and fill gaps in the global data products. OCI chlorophyll aalgorithm revisited for its algorithm coefficients and transitionNew quality‐control scheme proposed to mask straylightBoth data quality and data quantity improved for global oceans

Penetration of visible solar radiation (VSR) drives heating and phytoplankton photosynthesis in the upper water column; thus, it is always important to accurately describe the vertical distribution of VSR in the oceans. Before the invention and application of modern optical‐electronic instruments to measure the vertical profiles of VSR, the transmittance of VSR from surface to deeper ocean (TVSR) was commonly estimated based on water types and subsequently incorporated in dynamic ocean circulation models. However, the measurement of Secchi disk depth (ZSD) has been carried out since the 1860s and there are about a million of ZSDdata available for the global oceans. Because ZSDrepresents a measure of water's transparency, here we present a scheme based on radiative transfer to accurately estimate TVSRwith ZSDas the sole input. It is found that the median ratios between modeled and measured TVSRare ~0.8–1.0 for TVSRin a range of 1–100% for measurements made in coastal waters and oceanic gyres. However, this median ratio spans ~0.04–1.0 for the same measurements when the classical water‐type‐based model was applied. These results suggest a great advantage, and potentially significant impact, in incorporating the volumetric ZSDdata to model the dynamic ocean–atmosphere systems in the past 100+ years. Based on radiative transfer and using numerically simulated data, the transmittance (TVSR) of visible solar radiation (VSR) is modeled as a function of Secchi disk depth (ZSD). This scheme was further evaluated using data from numerical simulations and from field measurements where ZSDspans a range of ~1–75 m. For waters from coastal to super blue oceanic gyres, the modeled TVSRagree with measured TVSRvery well for TVSRgreater than 1%. For the same data set, however, the modeled TVSRcan be 20 times lower than measured TVSRfor oceanic waters when it was estimated based on the traditional water‐type approach. Better modeled TVSRcan improve general ocean circulation models, which opens a door to better study the ocean–atmosphere systems in the past decades to a century with the large volume of ZSDdata. A model based on radiative transfer and Secchi depth (ZSD) is developed to estimate the transmittance of visible solar radiationThe estimated transmittance with ZSDas input agrees very well with measured values, and significantly better than the water‐type schemeThe scheme offers an opportunity to accurately estimate the transmittance of solar radiation in the upper ocean based on century‐long ZSDdata

Various empirical relationships have been developed in the past nine decades to link the Secchi‐disk depth (ZSD) with the diffuse attenuation coefficient (KPAR), the euphotic zone depth (Zeu), and chlorophyll (Chl) concentration, where the latter two are important for the quantification and evaluation of photosynthesis in aquatic environments. There was also a classical theory regarding Secchi‐disk sighting, but large gaps existed between the observations and model outcomes. These gaps have puzzled the ocean community for > 60 yr and have resulted in contradictory conclusions regarding the interpretation and usefulness of ZSD. Here, we compare these measurements with data simulated based on an innovative theory and model regarding ZSD, and we found remarkable agreements between theoretical predictions and these century‐long observations. The results not only resolve the long‐standing puzzles associated with these observations, but also unify the relationships published in the literature. In particular, the ratio of Zeuto ZSDis found to be ∼ 3.5 for all waters, which is ∼ 45% greater than the consensus value of ∼ 2.4 suggested in the past for clear waters. In addition, the new model validates an empirical relationship between ZSDand Chl developed for global oceanic waters, thus providing strong support for using historical ZSDdata to study changes of phytoplankton in global oceans in the past century.

An empirical algorithm for estimating particulate organic carbon (POC) concentration in the surface ocean from satellite observations is formulated and validated using in situ POC data and remote‐sensing reflectance (Rrs) data obtained from match‐up satellite ocean color measurements. The algorithm builds upon the band‐difference algorithm concept, which was originally developed for estimating chlorophyll‐a concentration in clear waters. This algorithm utilizes three spectral bands centered approximately at 490, 550, and 670 nm to determine a color index (CIPOC), from which POC can be estimated from satellite measurements. For comparison, the blue‐green band‐ratio algorithm is also formulated using the same data set of in situ POC and satellite‐derived Rrs. Results show that the statistical parameters characterizing the differences between the satellite‐derived POC and matchup in situ POC are similar when the CIPOCand band ratio algorithms are applied to open ocean waters where the values of CIPOCare relatively low. In coastal waters where the values of CIPOCare generally higher, the statistical parameters of algorithm performance are better for the CIPOCalgorithm. In addition, because the CIPOCalgorithm is less sensitive to errors and noise in the satellite‐derived Rrs, the image quality obtained with this algorithm can be improved for both open‐ocean and coastal waters. Particulate organic carbon (POC) in the global ocean is linked to many important ocean biogeochemical processes and is responsible for large carbon fluxes. POC variations occur over a broad range of spatial (from regional to global) and temporal (from seasonal to decadal) scales due to various factors. Ocean color data acquired from satellite sensors, such as Sea‐viewing Wide‐Field‐of‐view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium‐Resolution Imaging Spectrometer (MERIS), can be used to quantify POC, with the capability for uninterrupted long‐term observations and global coverage. This study demonstrates that the color‐index (band‐difference) approach is applicable to the POC retrieval from remote‐sensing reflectance in both open ocean and coastal waters. A color‐index algorithm for estimating POC is proposedThe performance of the algorithm is compared with the band‐ratio algorithmOptical proxies for POC estimation are discussed

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (R_rs) and Secchi disk depth (Z_SD) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01–6.8  m^−1) from R_rs(λ) for the 350–560 nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly “exact” analytical inversion of an R_rs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350–560 nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of Z_SD places an extra constraint in the inversion of R_rs, no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of Z_SD and R_rs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

Subtropical gyres cover 26%–29% of the world's surface ocean and are conventionally regarded as ocean deserts due to their permanent stratification, depleted surface nutrients, and low biological productivity. Despite tremendous advances over the past three decades, particularly through the Hawaii Ocean Time‐series and the Bermuda Atlantic Time‐series Study, which have revolutionized our understanding of the biogeochemistry in oligotrophic marine ecosystems, the gyres remain understudied. We review current understanding of upper ocean biogeochemistry in the North Pacific Subtropical Gyre, considering other subtropical gyres for comparison. We focus our synthesis on spatial variability, which shows larger than expected dynamic ranges of properties such as nutrient concentrations, rates of N2fixation, and biological production. This review provides new insights into how nutrient sources drive community structure and export in upper subtropical gyres. We examine the euphotic zone (EZ) in subtropical gyres as a two‐layered vertically structured system: a nutrient‐depleted layer above the top of the nutricline in the well‐lit upper ocean and a nutrient‐replete layer below in the dimly lit waters. These layers vary in nutrient supply and stoichiometries and physical forcing, promoting differences in community structure and food webs, with direct impacts on the magnitude and composition of export production. We evaluate long‐term variations in key biogeochemical parameters in both of these EZ layers. Finally, we identify major knowledge gaps and research challenges in these vast and unique systems that offer opportunities for future studies. Vast subtropical oceans feature basin‐wide anticyclonic gyres, which restrict vertical supplies of nutrients, resulting in low surface nutrient concentrations and generally low rates of biological production. The subtropical gyres have therefore traditionally been regarded as ocean deserts. Through a comprehensive data re‐analysis focusing on the North Pacific Subtropical Gyre, we find larger than expected spatiotemporal variability in biogeochemical properties in subtropical gyres, including the distribution of nutrients, N2fixation, and biological production. Such variations are most pronounced through the sunlit water column (euphotic zone [EZ]), including sharp gradients in nutrient concentrations and sources and microbial community structure. Based on analysis and synthesis conducted in this study, we show evidence for the functioning of a two‐layer EZ habitat, with each layer differentiated primarily by inputs of sunlight and nutrients. Our understanding of long‐term biogeochemical variability is relatively restricted, owing to a lack of reliable long‐term observations at adequate spatial scales. We therefore urge the development and use of higher temporal and spatial resolution sampling strategies, for example, through the use of autonomous sampling platforms, that will allow for a greater understanding of biogeochemical processes and facilitate improvements in numerical modeling capabilities. Subtropical gyres display larger spatiotemporal dynamics in biogeochemical properties than previously consideredAn improved two‐layer framework is proposed for the study of nutrient‐driven and biologically mediated carbon export in the euphotic zoneFuture research will benefit from high‐resolution samplings, improved sensitivity of nutrient analyses, and advanced modeling capabilities Subtropical gyres display larger spatiotemporal dynamics in biogeochemical properties than previously considered An improved two‐layer framework is proposed for the study of nutrient‐driven and biologically mediated carbon export in the euphotic zone Future research will benefit from high‐resolution samplings, improved sensitivity of nutrient analyses, and advanced modeling capabilities

The transmittance of solar radiation in the oceanic water column plays an important role in heat transfer and photosynthesis, with implications for the global carbon cycle, global circulation, and climate. Globally, the transmittance of solar radiation in the visible domain (∼400–700 nm) (TRVIS) through the water column, which determines the vertical distribution of visible light, has to be based on remote sensing products. There are models centered on chlorophyll‐a (Chl) concentration or Inherent Optical Properties (IOPs) as both can be derived from ocean color measurements. We present evaluations of both schemes with field data from clear oceanic and from coastal waters. Here five models were evaluated: (1) Morel and Antoine (1994) (MA94), (2) Ohlmann and Siegel (2000) (OS00), (3) Murtugudde et al. (2002) (MU02), (4) Manizza et al. (2005) (MA05), and (5) Lee et al. ([Lee, Z., 2005]) (IOPs05), where the first four are Chl‐based and the last one is IOPs‐based, with all inputs derived from remote sensing reflectance. It is found that the best performing model is the IOPs05, with Unbiased Absolute Percent Difference (UAPD) ∼23%, while Chl‐based models show higher uncertainties (UAPD for MA94: ∼54%, OS00: ∼133%, MU02: ∼56%, and MA05: ∼39%). The IOPs‐based model was insensitive to the type of water, allowing it to be applied in most marine environments; whereas some of the Chl‐based models (MU02 and MA05) show much higher sensitivities in coastal turbid waters (higher Chl waters). These results highlight the applicablity of using IOPs products for such applications. The vertical distribution of solar radiation in the visible domain estimated from models using remote sensing data as input is evaluatedThe best performance was provided by a scheme centered on the Inherent Optical Properties (IOPs)The mean uncertainty of the estimated transmittance for a range of 0.1–100% is ∼23% from the IOPs system and insensitive to water types

Self-shading associated with a skylight-blocked approach (SBA) system for the measurement of water-leaving radiance (L_w) and its correction [Appl. Opt.52, 1693 (2013)APOPAI0003-693510.1364/AO.52.001693] is characterized by Monte Carlo simulations, and it is found that this error is in a range of ∼1%−20% under most water properties and solar positions. A model for estimating this shading error is further developed, and eventually a scheme to correct this error based on the shaded measurements is proposed and evaluated. It is found that the shade-corrected value in the visible domain is within 3% of the true value, which thus indicates that we can obtain not only high precision but also high accuracy L_w in the field with the SBA scheme.

Using simulations, error propagation theory, and measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), we determined the minimal signal‐to‐noise ratio (SNR) required for ocean color measurements and product uncertainties at different spatial and temporal scales. First, based on typical top‐of‐atmosphere (TOA) radiance over the ocean, we evaluate the uncertainties in satellite‐derived Rrsin the visible wavelengths (ΔRrs(vis)) due to sensor noise in both the near‐infrared (NIR) and the visible bands. While the former induces noise in Rrs(vis) through atmospheric correction, the latter has a direct impact on Rrs(vis). Such estimated uncertainties are compared with inherent ΔRrs(vis) uncertainties from in situ measurements and from the operational atmosphere correction algorithm. The comparison leads to a conclusion that once SNR(NIR) is above 600:1, an SNR(vis) better than 400:1 will not make a significant reduction in product uncertainties at pixel level under typical conditions for a solar zenith angle of 45°. Then, such uncertainties are found to decrease significantly in data products of oceanic waters when the 1 km pixels from individual images are binned to lower spatial resolution (e.g., 4 km) or temporal resolution (e.g., monthly). Although these findings do not suggest that passive ocean color sensors should have SNR(vis) around 400:1, they do support the argument for more trade space in higher spatial and/or spectral resolutions once this minimal 400:1 SNR(vis) requirement is met. Minimal signal‐to‐noise ratio for ocean color measurements quantified for the first timeNoise‐induced uncertainties in ocean color data products also quantified at different spatial and temporal scalesResults provide support for sensor design and data interpretation in studying ocean changes in response to climate variability

Spectral remote‐sensing reflectance (Rrs, sr−1) is the key for ocean color retrieval of water bio‐optical properties. Since Rrsfrom in situ and satellite systems are subject to errors or artifacts, assessment of the quality of Rrsdata is critical. From a large collection of high quality in situ hyperspectral Rrsdata sets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate the quality of an individual Rrsspectrum. This QA scheme consists of a unique Rrsspectral reference and a score metric. The reference system includes Rrsspectra of 23 optical water types ranging from purple blue to yellow waters, with an upper and a lower bound defined for each water type. The scoring system is to compare any target Rrsspectrum with the reference and a score between 0 and 1 will be assigned to the target spectrum, with 1 for perfect Rrsspectrum and 0 for unusable Rrsspectrum. The effectiveness of this QA system is evaluated with both synthetic and in situ Rrsspectra and it is found to be robust. Further testing is performed with the NOMAD data set as well as with satellite Rrsover coastal and oceanic waters, where questionable or likely erroneous Rrsspectra are shown to be well identifiable with this QA system. Our results suggest that applications of this QA system to in situ data sets can improve the development and validation of bio‐optical algorithms and its application to ocean color satellite data can improve the short‐term and long‐term products by objectively excluding questionable Rrsdata. A QA system is developed for spectral remote‐sensing reflectanceThe system consists of a reference and a score metricIt is applicable to both remotely sensed and in situ ocean color data

The diffuse attenuation of downwelling irradiance, Kd(m−1), is an important property related to light penetration and availability in aquatic ecosystems. The standard Kd(490) product (the diffuse attenuation coefficient at 490 nm) of the global oceans from satellite remote sensing has been produced with an empirical algorithm, which limits its reliability and applicability in coastal regions. More importantly, as an apparent optical property (AOP), Kdis a function of the angular distribution of the light field (e.g., solar zenith angle). The empirically derived product thus contains ambiguities when compared with in situ measurements as there is no specification regarding the corresponding solar zenith angle associated with this Kd(490) product. To overcome these shortcomings, we refined the Kdproduct with a product termed as the normalized diffuse attenuation coefficient (nKd, m−1), that is equivalent to the Kdin the absence of the atmosphere and with the sun at zenith. Models were developed to get nKdfrom both in situ measurements and ocean color remote sensing. Evaluations using field measurements indicated that the semianalytically derived nKdproduct will not only remove the ambiguities when comparing Kdvalues of different light fields but will also improve the quality of such a product, therefore maximizing the value offered by satellite ocean color remote sensing.

An approach to semianalytically derive waters' inherent optical properties (IOPs) from remote sensing reflectance (Rrs) and at the same time to take into account the residual errors in satellite Rrsis developed for open‐ocean clear waters where aerosols are likely of marine origin. This approach has two components: (1) a scheme of combining a neural network and an algebraic solution for the derivation of IOPs, and (2) relationships between Rrsresidual errors at 670 nm and other spectral bands. This approach is evaluated with both synthetic and Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) data, and the results show that it can significantly reduce the effects of residual errors in Rrson the retrieval of IOPs, and at the same time remove partially the Rrsresidual errors for “low‐quality” and “high‐quality” data defined in this study. Furthermore, more consistent estimation of chlorophyll concentrations between the empirical blue‐green ratio and band‐difference algorithms can be derived from the corrected “low‐quality” and “high‐quality” Rrs. These results suggest that it is possible to improve both data quality and quantity of satellite‐retrieved Rrsover clear open‐ocean waters with a step considering the spectral relationships of the residual errors in Rrsafter the default atmospheric correction procedure and without fixing Rrsat 670 nm to one value for clear waters in a small region such as 3 × 3 box. A step‐wise scheme has been developed for removing residual error from SeaWiFS Level‐2 dataThe scheme can improve both data quality and quantity of SeaWiFS Level‐2 data over clear open‐ocean watersThe scheme can convert ∼50% of the total nonzero invalid SeaWiFS level‐2 data to valid data for longer‐term composites

Usable solar radiation (USR) represents spectrally integrated solar energy in the spectral range of 400–560 nm, a domain where photons penetrate the most in oceanic waters and thus contribute to photosynthesis and heating at deeper depths. Through purely numerical simulations, it was found that the diffuse attenuation coefficient of downwelling USR (Kd(USR), m−1) is nearly a constant vertically in the upper water column for clear waters and most turbid waters. Subsequently an empirical model was developed to estimate Kd(USR) based on the diffuse attenuation coefficient at 490 nm (Kd(490), m−1). We here evaluate this relationship using data collected from a wide range of oceanic and coastal environments and found that the relationship between Kd(490) and Kd(USR) developed via the numerical simulation is quite robust. We further refined this relationship to extend the applicability to “clearest” natural waters. This refined relationship was then used to produce sample distribution of Kd(USR) of global oceans. As expected, extremely low Kd(USR) (∼0.02 m−1) was observed in ocean gyres, while significantly higher Kd(USR) (∼5.2 m−1) was found in very turbid coastal regions. A useful application of Kd(USR) is to easily and accurately propagate surface USR to deeper depths, potentially to significantly improve the estimation of basin scale primary production and heat fluxes in the upper water column. Using field measurements, the vertical variability of Kd(USR) is evaluatedRelationship between Kd(USR) and Kd(490) is refined to extend its applicabilityDistribution of Kd(USR) of global oceans and its implications are shown

The spectral slope of the absorption coefficient of colored dissolved and detrital material (CDM), Scdm(units: nm−1), is an important optical parameter for characterizing the absorption spectral shape of CDM. Although highly variable in natural waters, in most remote sensing algorithms, this slope is either kept as a constant or empirically modeled with multiband ocean color in the visible domain. In this study, we explore the potential of semianalytically retrieving Scdmwith added ocean color information in the ultraviolet (UV) range between 360 and 400 nm. Unique features of hyperspectral remote sensing reflectance in the UV‐visible wavelengths (360–500 nm) have been observed in various waters across a range of coastal and open ocean environments. Our data and analyses indicate that ocean color in the UV domain is particularly sensitive to the variation of the CDM spectral slope. Here, we used a synthesized dataset to show that adding UV wavelengths to the ocean color measurements will improve the retrieval of Scdmfrom remote sensing reflectance considerably, while the spectral band settings of past and current satellite ocean color sensors cannot fully account for the spectral variation of remote sensing reflectance. Results of this effort support the concept to include UV wavelengths in the next generation of satellite ocean color sensors. Unique features of remote sensing reflectance in the UV wavelengths are measured in various watersThe ocean color in the UV is particularly sensitive to the variation of spectral slope of the CDMAdding UV wavebands will significantly improve the retrieval of the spectral slopes for CDM

We evaluated three key components in modeling hyperspectral remote-sensing reflectance in the visible to shortwave-infrared (Vis–SWIR) domain of high-sediment-load (HSL) waters, which are the relationship between remote-sensing reflectance (R_rs) and inherent optical properties (IOPs), the absorption coefficient spectrum of pure water (a_w) in the IR–SWIR region, and the spectral variation of sediment absorption coefficient (a_sed). Results from this study indicate that it is necessary to use a more generalized R_rs–IOP model to describe the spectral variation of R_rs of HSL waters from Vis to SWIR; otherwise it may result in a spectrally distorted R_rs spectrum if a constant model parameter is used. For hyperspectral a_w in the IR–SWIR domain, the values reported in Kou et al. (1993) provided a much better match with the spectral variation of R_rs in this spectral range compared to that of Segelstein (1981). For a_sed spectrum, an empirical a_sed spectral shape derived from sample measurements is found working much better than the traditional exponential-decay function of wavelength in modeling the spectral variation of R_rs in the visible domain. These results would improve our understanding of the spectral signatures of R_rs of HSL waters in the Vis–SWIR domain and subsequently improve the retrieval of IOPs from ocean color remote sensing, which could further help the estimation of sediment loading of such waters. Limitations in estimating chlorophyll concentration in such waters are also discussed.

In the above article [1], (2) should be expressed as follows:

We study the impacts of the bio-optical model variations on the angular distribution (f/Q factor) of the upwelling radiance field in ocean waters. An ocean water bio-optical model has been combined with a vector radiative transfer model to calculate the f/Q factors systematically. The f/Q factors are compared to those in [Appl. Opt.41, 6289 (2002)10.1364/AO.41.006289APOPAI1559-128X] and the differences are found to be within ±10% for 81% of the total number of cases covering all wavelengths, chlorophyll a concentrations, and solar and viewing geometries. The differences are attributed to the choice of ocean water scattering function and scattering coefficient biases. In addition, we study the uncertainty of f/Q factor due to three factors: (I) the absorption coefficient of the colored dissolved organic matter (CDOM), (II) the particle scattering coefficient, and (III) the ocean water depolarization. The impacts of ocean water depolarization on the f/Q variation is found to be negligible. If we perturb the CDOM absorption coefficient by a factor ranging from 0.1 to 10, the f/Q values vary within ±5% of the average behavior of ocean waters for 93% of the cases. If we perturb the scattering coefficients by a factor ranging from 0.5 to 2.0, the f/Q variation is within ±5% for 81% of the cases studied. This work contributes to understanding the uncertainty of ocean color remote sensing.

Hyperspectral (every 5 nm) absorption coefficients of “pure” seawater in the range of 350–550 nm are derived from remote sensing reflectance measured in oligotrophic oceans. The absorption spectrum is reduced by ∼50–70% for the 350–400-nm range and ∼5–10% for the 510–530-nm range compared with the commonly adopted standard for ocean color processing and shows different spectral curvatures. The application of this new spectrum resulted in better retrievals of the phytoplankton absorption coefficient in oligotrophic oceans and will provide better closure of remote sensing reflectance for the UV–visible domain.

Using 901 remote-sensing reflectance spectra (R_rs(λ),sr^−1, λ from 400 to 700 nm with a 5 nm resolution), we evaluated the correlations of R_rs(λ) between neighboring spectral bands in order to characterize (1) the spectral interdependence of R_rs(λ) at different bands and (2) to what extent hyperspectral R_rs(λ) can be reconstructed from multiband measurements. The 901 R_rs spectra were measured over a wide variety of aquatic environments in which water color varied from oceanic blue to coastal green or brown, with chlorophyll-a concentrations ranging from ∼0.02 to >100  mg  m^−3, bottom depths from ∼1  m to >1000  m, and bottom substrates including sand, coral reef, and seagrass. The correlation coefficient of R_rs(λ) between neighboring bands at center wavelengths λ_k and λ_l, r_Δλ(λ_k,λ_l), was evaluated systematically, with the spectral gap (Δλ=λ_l−λ_k) changing between 5, 10, 15, 20, 25, and 30 nm, respectively. It was found that r_Δλ decreased with increasing Δλ, but remained >0.97 for Δλ≤20  nm for all spectral bands. Further, using 15 spectral bands between 400 and 710 nm, we reconstructed, via multivariant linear regression, hyperspectral R_rs(λ) (from 400 to 700 nm with a 5 nm resolution). The percentage difference between measured and reconstructed R_rs for each band in the 400–700 nm range was generally less than 1%, with a correlation coefficient close to 1.0. The mean absolute error between measured and reconstructed R_rs was about 0.00002  sr^−1 for each band, which is significantly smaller than the R_rs uncertainties from all past and current ocean color satellite radiometric products. These results echo findings of earlier studies that R_rs measurements at ∼15 spectral bands in the visible domain can provide nearly identical spectral information as with hyperspectral (contiguous bands at 5 nm spectral resolution) measurements. Such results provide insights for data storage and handling of large volume hyperspectral data as well as for the design of future ocean color satellite sensors.

Optical remote sensing systems aboard geostationary platforms can provide high-frequency observations of bio-optical properties in dynamical coastal/oceanic waters. From the end-user standpoint, it is recognized that the fidelity of daily science products relies heavily on the radiometric sensitivity/performance of the imaging system. This study aims to determine the theoretical detection limits for bio-optical properties observed diurnally from a geostationary orbit. The analysis is based upon coupled radiative transfer simulations and the minimum radiometric requirements defined for the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) mission. The diurnal detection limits are found for the optically active constituents of water, including near-surface concentrations of chlorophyll-a (CHL) and total suspended solids (TSS), and the absorption of colored dissolved organic matter (a_CDOM). The diurnal top-of-atmosphere radiance (L_t) is modeled for several locations across the field of regard (FOR) to investigate the radiometric sensitivity at different imaging geometries. It is found that, in oceanic waters (CHL=0.07??mg/m^3), detecting changes smaller than 0.01??mg/m^3 in CHL is feasible for all locations and hours except for late afternoon observations on the edge of the FOR. For more trophic/turbid waters (0.6

A new radiometric term named as usable solar radiation(USR) is defined to represent the spectrally integrated solar irradiance in the spectral window of 400–560 nm. Through numerical simulations of optically deep waters covering a wide range of optical properties, it is found that the diffuse attenuation coefficient of downwelling USR, Kd(USR), is nearly a constant vertically for almost all oceanic waters (chlorophyll concentration under ∼3 mg m−3). This feature is quite contrary to the diffuse attenuation coefficient of the photosynthetic available radiation, K(PAR), which varies significantly from surface to deeper depths for oceanic waters. It is also found that the ratio of the photosynthetic utilizable radiation (PUR) to the product of USR and phytoplankton absorption coefficient at 440 nm approximates a constant for most oceanic waters. These results support the use of a single Kd(USR) for each water and each sun angle for accurate estimation of USR propagation, and suggest an efficient approach to estimate PUR(z) in the upper water column. These results further indicate that it is necessary and valuable for the generation of USR and Kd(USR) products from satellite ocean color measurements, which can be used to facilitate the studies of heat transfer and photosynthesis in the global oceans. A new radiometric term (USR) is definedAttenuation of USR is nearly constant vertically for oceanic watersUSR and phytoplankton absorption speed the calculation of PUR

Penetration of the photosynthetically available radiation (PAR, over 400–700 nm) in the upper ocean is important for many processes such as water radiant heating and primary productivity. Because of this importance, daily PAR at sea surface (PAR¯(0+)) is routinely generated from ocean-color images for global studies. To propagate this broadband solar radiation through the upper ocean, an attenuation coefficient of PAR (KPAR) is also generated from the same ocean-color measurements. However, due to the empirical nature of the KPARalgorithm, this KPARproduct corresponds to an instantaneous PAR at a fixed sun angle, with no diurnal variability. It is hence necessary to have an attenuation coefficient matching the temporal characteristics of daily PAR. This paper represents an effort to meet this need. Using ECOLIGHT, the subsurface light field for a wide range of water bodies was simulated, from which the attenuation coefficient (KPAR¯) of daily PAR was calculated. We presented the diurnal and vertical variation of this attenuation coefficient, and found that it can be well predicted (within ∼7%) as a function of the total absorption coefficient and backscattering coefficient at 490 nm and the noontime solar zenith angle. This new model offers an efficient and reasonably accurate approach for quantifying daily upper water column PAR within the global ocean from satellite measurements of water color.

Raman scattering can be a significant contributor to the emergent radiance spectrum from the surface ocean. Here, we present an analytical approach to directly estimate the Raman contribution to remote sensing reflectance, and evaluate its effects on optical properties estimated from two common semianalytical inversion models. For application of the method to ocean color remote sensing, spectral irradiance products in the ultraviolet from the OMI instrument are merged with MODerate-resolution Imaging Spectroradiometer (MODIS) data in the visible. The resulting global fields of Raman-corrected optical properties show significant differences from standard retrievals, particularly for the particulate backscattering coefficient, b_bp, where average errors in clear ocean waters are ∼50%. Given the interest in transforming b_bp into biogeochemical quantities, Raman scattering must be accounted for in semianalytical inversion schemes.

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

It has been a long-standing goal to precisely measure water-leaving radiance (L_w, or its equivalent property, remote-sensing reflectance) in the field, but reaching this goal is quite a challenge. This is because conventional approaches do not provide a direct measurement of L_w, but rather measure various related components and subsequently derive this core property from these components. Due to many uncontrollable factors in the measurement procedure and imprecise post-measurement processing routines, the resulting L_w is inherently associated with various levels of uncertainties. Here we present a methodology called the skylight-blocked approach (SBA) to measure L_w directly in the field, along with results obtained recently in the Laurentian Great Lakes. These results indicate that SBA can measure L_w in high precision. In particular, there is no limitation of water types for the deployment of SBA, and the requirement of post-measurement processing is minimum; thus high-quality L_w for a wide range of aquatic environments can be acquired.

Sensor design and mission planning for satellite ocean color measurements requires careful consideration of the signal dynamic range and sensitivity (specifically here signal-to-noise ratio or SNR) so that small changes of ocean properties (e.g., surface chlorophyll-a concentrations or Chl) can be quantified while most measurements are not saturated. Past and current sensors used different signal levels, formats, and conventions to specify these critical parameters, making it difficult to make cross-sensor comparisons or to establish standards for future sensor design. The goal of this study is to quantify these parameters under uniform conditions for widely used past and current sensors in order to provide a reference for the design of future ocean color radiometers. Using measurements from the Moderate Resolution Imaging Spectroradiometer onboard the Aqua satellite (MODISA) under various solar zenith angles (SZAs), typical (L_typical) and maximum (L_max) at-sensor radiances from the visible to the shortwave IR were determined. The L_typical values at an SZA of 45° were used as constraints to calculate SNRs of 10 multiband sensors at the same L_typical radiance input and 2 hyperspectral sensors at a similar radiance input. The calculations were based on clear-water scenes with an objective method of selecting pixels with minimal cross-pixel variations to assure target homogeneity. Among the widely used ocean color sensors that have routine global coverage, MODISA ocean bands (1 km) showed 2–4 times higher SNRs than the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (1 km) and comparable SNRs to the Medium Resolution Imaging Spectrometer (MERIS)-RR (reduced resolution, 1.2 km), leading to different levels of precision in the retrieved Chl data product. MERIS-FR (full resolution, 300 m) showed SNRs lower than MODISA and MERIS-RR with the gain in spatial resolution. SNRs of all MODISA ocean bands and SeaWiFS bands (except the SeaWiFS near-IR bands) exceeded those from prelaunch sensor specifications after adjusting the input radiance to L_typical. The tabulated L_typical, L_max, and SNRs of the various multiband and hyperspectral sensors under the same or similar radiance input provide references to compare sensor performance in product precision and to help design future missions such as the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission and the Pre-Aerosol-Clouds-Ecosystems (PACE) mission currently being planned by the U.S. National Aeronautics and Space Administration (NASA).

Relationships between the satellite-derived diffuse attenuation coefficient of downwelling irradiance (K_d) and airborne-based vertical attenuation of lidar volume backscattering (α) were examined in two coastal environments. At 1.1?km resolution and a wavelength of 532?nm, we found a greater connection between α and K_d when α was computed below 2?m depth (Spearman rank correlation coefficient up to 0.96), and a larger contribution of K_d to α with respect to the beam attenuation coefficient as estimated from lidar measurements and K_d models. Our results suggest that concurrent passive and active optical measurements can be used to estimate total scattering coefficient and backscattering efficiency in waters without optical vertical structure.

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within ±10% for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.

Remote sensing of coastal and inland waters requires sensors to have a high spatial resolution to cover the spatial variation of biogeochemical properties in fine scales. High spatial-resolution sensors, however, are usually equipped with spectral bands that are wide in bandwidth (50 nm or wider). In this study, based on numerical simulations of hyperspectral remote-sensing reflectance of optically-deep waters, and using Landsat band specifics as an example, the impact of a wide spectral channel on remote sensing is analyzed. It is found that simple adoption of a narrowband model may result in >20% underestimation in calculated remote-sensing reflectance, and inversely may result in >20% overestimation in inverted absorption coefficients even under perfect conditions, although smaller (~5%) uncertainties are found for higher absorbing waters. These results provide a cautious note, but also a justification for turbid coastal waters, on applying narrowband models to wideband data.

Remote sensing of coastal and inland waters requires sensors to have a high spatial resolution to cover the spatial variation of biogeochemical properties in fine scales. High spatial-resolution sensors, however, are usually equipped with spectral bands that are wide in bandwidth (50 nm or wider). In this study, based on numerical simulations of hyperspectral remote-sensing reflectance of optically-deep waters, and using Landsat band specifics as an example, the impact of a wide spectral channel on remote sensing is analyzed. It is found that simple adoption of a narrowband model may result in >20% underestimation in calculated remote-sensing reflectance, and inversely may result in >20% overestimation in inverted absorption coefficients even under perfect conditions, although smaller (~5%) uncertainties are found for higher absorbing waters. These results provide a cautious note, but also a justification for turbid coastal waters, on applying narrowband models to wideband data.

Hyperspectral instruments provide the spectral detail necessary for extracting multiple layers of information from inherently complex coastal environments. We evaluate the performance of a semi-analytical optimization model for deriving bathymetry, benthic reflectance, and water optical properties using hyperspectral AVIRIS imagery of Kaneohe Bay, Hawaii. We examine the relative impacts on model performance using two different atmospheric correction algorithms and two different methods for reducing the effects of sunglint. We also examine the impact of varying view and illumination geometry, changing the default bottom reflectance, and using a kernel processing scheme to normalize water properties over small areas. Results indicate robust model performance for most model formulations, with the most significant impact on model output being generated by differences in the atmospheric and deglint algorithms used for preprocessing.