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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 in20% underestimation in calculated remote-sensing reflectance, and inversely may result in20% overestimation in inverted absorption coefficients even under perfect conditions, although smaller (approximately 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.

Hyperion is a hyperspectral sensor on board NASA's EO-1 satellite with a spatial resolution of approximately 30 m and a swath width of about 7 km. It was originally designed for land applications, but its unique spectral configuration (430 nm - 2400 nm with a ~10 nm spectral resolution) and high spatial resolution make it attractive for studying complex coastal ecosystems, which require such a sensor for accurate retrieval of environmental properties. In this paper, Hyperion data over an area of the Florida Keys is used to develop and test algorithms for atmospheric correction and for retrieval of subsurface properties. Remote-sensing reflectance derived from Hyperion data is compared with those from in situ measurements. Furthermore, water's absorption coefficients and bathymetry derived from Hyperion imagery are compared with sample measurements and LIDAR survey, respectively. For a depth range of ~ 1 - 25 m, the Hyperion bathymetry match LIDAR data very well (~11% average error); while the absorption coefficients differ by ~16.5% (in a range of 0.04 - 0.7 m-1 for wavelengths of 410, 440, 490, 510, and 530 nm) on average. More importantly, in this top-to-bottom processing of Hyperion imagery, there is no use of any a priori or ground truth information. The results demonstrate the usefulness of such space-borne hyperspectral data and the techniques developed for effective and repetitive observation of complex coastal regions.

For optically deep waters, remote-sensing reflectance (r(rs)) is traditionally expressed as the ratio of the backscattering coefficient (b(b)) to the sum of absorption and backscattering coefficients (a + b(b)) that multiples a model parameter (g, or the so-called f'/Q). Parameter g is further expressed as a function of b(b)/(a + b(b)) (or b(b)/a) to account for its variation that is due to multiple scattering. With such an approach, the same g value will be derived for different a and b(b) values that provide the same ratio. Because g is partially a measure of the angular distribution of upwelling light, and the angular distribution from molecular scattering is quite different from that of particle scattering; g values are expected to vary with different scattering distributions even if the b(b)/a ratios are the same. In this study, after numerically demonstrating the effects of molecular and particle scatterings on the values of g, an innovative r(rs) model is developed. This new model expresses r(rs) in two separate terms: one governed by the phase function of molecular scattering and one governed by the phase function of particle scattering, with a model parameter introduced for each term. In this way the phase function effects from molecular and particle scatterings are explicitly separated and accounted for. This new model provides an analytical tool to understand and quantify the phase-function effects on r(rs), and a platform to calculate r(rs) spectrum quickly and accurately that is required for remote-sensing applications.

For open ocean and coastal waters, a multiband quasi-analytical algorithm is developed to retrieve absorption and backscattering coefficients, as well as absorption coefficients of phytoplankton pigments and gelbstoff. This algorithm is based on remote-sensing reflectance models derived from the radiative transfer equation, and values of total absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. In the calculation of total absorption coefficient, no spectral models for pigment and gelbstoff absorption coefficients are used. Actually those absorption coefficients are spectrally decomposed from the derived total absorption coefficient in a separate calculation. The algorithm is easy to understand and simple to implement. It can be applied to data from past and current satellite sensors, as well as to data from hyperspectral sensors. There are only limited empirical relationships involved in the algorithm, and they are for less important properties, which implies that the concept and details of the algorithm could be applied to many data for oceanic observations. The algorithm is applied to simulated data and field data, both non-case1, to test its performance, and the results are quite promising. More independent tests with field-measured data are desired to validate and improve this algorithm.

In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R2 = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.

By use of a common primary-production model and identical photosynthetic parameters, four different methods were used to calculate quanta (Q) and primary production (P) at depth for a study of high-latitude North Atlantic waters. The differences among the four methods relate to the use of pigment information in the upper water column. Methods 1 and 2 use pigment biomass (B) as an input and a subtropical, empirical relation between K(d) (diffuse attenuation coefficient) and B to estimate Q at depth. Method 1 uses measured B, but Method 2 uses B derived from the Coastal Zone Color Scanner (subtropical algorithm) as inputs. Methods 3 and 4 use the phytoplankton absorption coefficient (a(ph)) instead of B as input, and Method B uses empirically derived a(ph)(440) and K(d) values, and Method 4 uses analytically derived a(ph)(440) and a (total absorption coefficient) values based on the same remote measurements as Method 2. When the calculated and the measured values of Q(z) and P(z) were compared, Method 4 provided the closest results [for P(z), r(2) = 0.95 (n = 24), and for Q(z), r(2) = 0.92 (n = 11)]. Method 1 yielded the worst results [for P(z), r(2) = 0.56 and for Q(z), r(2) = 0.81]. These results indicate that one of the greatest uncertainties in the remote estimation of P can come from a potential mismatch of the pigment-specific absorption coefficient (a(ph)*), which is needed implicitly in current models or algorithms based on B. We point out that this potential mismatch can be avoided if we arrange the models or algorithms so that they are based on the pigment absorption coefficient (a(ph)). Thus, except for the accuracy of the photosynthetic parameters and the above-surface light intensity, the accuracy of the remote estimation of P depends on how accurately a(ph) can be estimated, but not how accurately B can be estimated. Also, methods to derive a(ph) empirically and analytically from remotely sensed data are introduced. Curiously, combined application of subtropical algorithms for both B and K(d) to subarctic waters apparently compensates to some extent for effects that are due to their similar and implicit pigment-specific absorption coefficients for the calculation of Q(z).

A method to derive in-water absorption coefficients from total remote-sensing reflectance (ratio of the upwelling radiance to the downwelling irradiance above the surface) analytically is presented. For measurements made in the Gulf of Mexico and Monterey Bay, with concentrations of chlorophyll-a ranging from 0.07 to 50 mg/m(3), comparisons are made for the total absorption coefficients derived with the suggested method and those derived with diffuse attenuation coefficients. For these coastal to open-ocean waters, including regions of upwelling and the Loop Current, the results are as follows: at 440 nm the difference between the two methods is 13.0% (r(2) = 0.96) for total absorption coefficients ranging from 0.02 to 2.0 m(-1); at 488 nm the difference is 14.5% (r(2) = 0.97); and at 550 nm the difference is 13.6% (r(2) = 0.96). The results indicate that the method presented works very well for retrieval of in-water absorption coefficients exclusively from remotely measured signals, and that this method has a wide range of potential applications in oceanic remote sensing.

The quantum yield of photosynthesis (mol C/mol photons) was calculated at six depths for the waters of the Marine Light-Mixed Layer (MLML) cruise of May 1991. As there were photosynthetically available radiation (PAR) but no spectral irradiance measurements for the primary production incubations, three ways are presented here for the calculation of the absorbed photons (AP) by phytoplankton for the purpose of calculating phi. The first is based on a simple, nonspectral model; the second is based on a nonlinear regression using measured PAR values with depth; and the third is derived through remote sensing measurements. We show that the results of phi calculated using the nonlinear regreesion method and those using remote sensing are in good agreement with each other, and are consistent with the reported values of other studies. In deep waters, however, the simple nonspectral model may cause quantum yield values much higher than theoretically possible.

Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.

Secchi disk depth ( Z SD ) is a measure of water transparency, whose interpretation has wide applications from diver visibility to studies of climate change. This transparency has been explained in the past 60 + years with the underwater visibility theory, the branch of the general visibility theory for visual ranging in water. However, through a thorough review of the physical processes involved in visual ranging in water, we show that this theory may not exactly represent the sighting of a Secchi disk by a human eye. Further, we update the Law of Contrast Reduction, a key concept in visibility theory, and develop a new theoretical model to interpret Z SD . Unlike the classical model that relies strongly on the beam attenuation coefficient, the new model relies only on the diffuse attenuation coefficient at a wavelength corresponding to the maximum transparency for such interpretations. This model is subsequently validated using a large (N = 338) dataset of independent measurements covering oceanic, coastal, and lake waters, with results showing excellent agreement (~ 18% average absolute difference, R 2 = 0.96) between measured and theoretically predicted Z SD ranging from 30 m without regional tuning of any model parameters. This study provides a more generalized view of visual ranging, and the mechanistic model is expected to significantly improve the current capacity in monitoring water transparency of the global aquatic environments via satellite remote sensing.

The NASA Geostationary Coastal and Air Pollution Events satellite mission plans to keep an eye on short-term processes that affect coastal communities and ecosystems.

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.