Diurnal remote sensing of coastal/oceanic waters: a radiometric analysis for Geostationary Coastal and Air Pollution Events

Assessing potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for water quality monitoring across the coastal United States

The Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) will provide unique high temporal frequency observations of the United States coastal waters to quantify processes that vary on short temporal and spatial scales. The frequency and coverage of observations from geostationary orbit will improve quantification and reduce uncertainty in tracking water quality events such as harmful algal blooms and oil spills. This study looks at the potential for GLIMR to complement existing satellite platforms from its unique geostationary viewpoint for water quality and oil spill monitoring with a focus on temporal and spatial resolution aspects. Water quality measures derived from satellite imagery, such as harmful algal blooms, thick oil, and oil emulsions are observable with glint <0.005 sr-1, while oil films require glint >10-5 sr-1. Daily imaging hours range from 6 to 12 h for water quality measures, and 0 to 6 h for oil film applications throughout the year as defined by sun glint strength. Spatial pixel resolution is 300 m at nadir and median pixel resolution was 391 m across the entire field of regard, with higher spatial resolution across all spectral bands in the Gulf of Mexico than existing satellites, such as MODIS and VIIRS, used for oil spill surveillance reports. The potential for beneficial glint use in oil film detection and quality flagging for other water quality parameters was greatest at lower latitudes and changed location throughout the day from the West and East Coasts of the United States. GLIMR scan times can change from the planned ocean color default of 0.763 s depending on the signal-to-noise ratio application requirement and can match existing and future satellite mission regions of interest to leverage multi-mission observations.

Performance and Uncertainty of Satellite-Derived Bathymetry Empirical Approaches in an Energetic Coastal Environment

Objectives of this study are to evaluate the performance of different satellite-derived bathymetry (SDB) empirical models developed for multispectral satellite mission applications and to propose an uncertainty model based on inferential statistics. The study site is the Arcachon Bay inlet (France). A dataset composed of 450,837 echosounder data points and 89 Sentinel-2 A/B and Landsat-8 images acquired from 2013 to 2020, is generated to test and validate SDB and uncertainty models for various contrasting optical conditions. Results show that water column optical properties are characterized by a high spatio-temporal variability controlled by hydrodynamics and seasonal conditions. The best performance and highest robustness are found for the cluster-based approach using a green band log-linear regression model. A total of 80 satellite images can be exploited to calibrate SDB models, providing average values of root mean square error and maximum bathymetry of 0.53 m and 7.3 m, respectively. The uncertainty model, developed to extrapolate information beyond the calibration dataset, is based on a multi-scene approach. The sensitivity of the model to the optical variability not explained by the calibration dataset is demonstrated but represents a risk of error of less than 5%. Finally, the uncertainty model applied to a diachronic analysis definitively demonstrates the interest in SDB maps for a better understanding of morphodynamic evolutions of large-scale and complex coastal systems.

Super-resolution optical mapping of floating macroalgae from geostationary orbit

The spatial resolution of an observation from a geostationary orbiting satellite is usually too coarse to track small scale macroalgae blooms. For macroalgae mapping to benefit from a geostationary orbit's staring monitoring and frequent revisit intervals, we introduced a super-resolution method that reconstructs a high-resolution (HR) image of a region from a sequence of raw geostationary low-resolution images of the same region. We tested our method with GF-4 images at 50 m spatial resolution and demonstrated that the spatial resolution increased to 25 m. In addition, the derived HR image had better image quality characterized by a higher signal-to-noise ratio, clarity, and contrast. The increased spatial resolution and improved image quality improved our ability to distinguish macroalgae patches from the surrounding waters, especially tiny patches of macroalgae, and to precisely delineate the patch boundaries. Lastly, we more accurately estimated the areal coverage of the patches by reducing underestimation of the coverage of tiny patches and overestimation of the coverage of large patches.

Semi-analytical algorithms of ocean color remote sensing under high solar zenith angles

With the increasing interest in ocean color remote sensing in polar oceans and geostationary ocean color satellite with diurnal observations, it is unavoidable to encounter ocean color retrievals under high solar zenith angles. Under these scenarios, the capability of current remote sensing algorithms is poorly known. In this study, the performance of the two widely used semi-analytical algorithms for the water inherent optical properties (QAA and GSM01) under high solar zenith angle conditions were firstly evaluated based on global in situ data set (SeaBASS-NOMAD). The results showed that the performances of both QAA and GSM01 degraded significantly with the increasing in solar zenith angle (SZA), and the biases increased about 1.3-fold when SZA varied from 30° to 80°. The high uncertainties at high SZA was mainly induced by the systematic overestimation of the key parameter u (ratio of backscattering coefficient to the sum of absorption and backscattering coefficients) at high solar zenith angles. Based on the Hydrolight-simulated data set, a new model (NN-algorithm) for retrieving u from remote sensing reflectance was developed for high solar zenith angle conditions using the neural network method. The validation results revealed that the NN-algorithm could improve the estimation of parameter u and further ocean color products. In addition, our results indicate that a more accurate atmosphere correction is needed to deal with ocean color remote sensing data acquired under large solar zenith angle conditions.

Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

Basic slit spectroscope reveals three-dimensional scenes through diagonal slices of hyperspectral cubes

A basic slit spectroscope is usually held close to the eye to produce the spectrum of a single slit view. However, a more distant viewer may have multiple slit views at once, an effect of dispersion that has been overlooked. Investigations of spectroscopic image geometry reveal that the maximum field of view equals the dispersion angle. Spectrally decoded camera-obscura projections compose three-dimensional images of a scene, emulating a Benton hologram. The images represent diagonal sections of a hyperspectral datacube. Consequently, the spectroscope can be used as an autostereoscopic display and for a fourth technique of hyperspectral data acquisition, named spatiospectral scanning.