Atmospheric correction algorithm over coastal and inland waters based on the red and NIR bands: application to Landsat-8/OLI and VNREDSat-1/NAOMI observations.

Water pixel extraction and correction of the atmospheric signal represent prerequisite steps prior to applying algorithms dedicated to the assessment of water quality of natural surface water bodies. The recent multiplication of medium spatial resolution sensors (10-60 m) provides the required constellation to monitoring bio-optical and biogeochemical parameters of surface waters at the relevant spatial-temporal scales. Here we present a new approach to identify water pixels and to extract the atmospheric contribution to the top of atmosphere signal measured by the NAOMI sensor on board the first Vietnamese satellite, VNREDSat-1. After verifying the TOA calibration of NAOMI through a vicarious calibration exercise, we adapt a recent water pixel extraction algorithm (WiPE) to NAOMI, and develop a new atmospheric correction algorithm (referred to as red-NIR) based on the use of the red and NIR bands (the only bands available for that purpose on NAOMI) and spectral relationships. The evaluation of red-NIR with a match-up data set gathering remote sensing reflectance, Rrs, measurements performed at the AERONET-OC stations in moderately turbid waters indicates excellent performance in the blue and green part of the spectrum (similar to the performances reached by the SeaDAS NIR-SWIR algorithms) and lower accuracy in the red. Intercomparison of simultaneous images collected by NAOMI and OLI over a more turbid water body shows an excellent agreement between the NAOMI-Rrs estimated by the present processing, and the OLI-Rrs estimated from the ACOLITE algorithm. This approach will allow sensors that do not have SWIR bands, such as SPOT-6 and -7, to be processed, making their data exploitation available for long-term temporal analyses.

Research ReportDiurnal global ocean surface pCO2 and air-sea CO2 flux reconstructed from spaceborne LiDAR data.

The ocean absorbs a significant amount of carbon dioxide (CO2) from the atmosphere, helping regulate Earth's climate. However, our knowledge of ocean CO2 sink levels remains limited. This research focused on assessing daily changes in ocean CO2 sink levels and air-sea CO2 exchange, using a new technique. We used LiDAR technology, which provides continuous measurements during day and night, to estimate global ocean CO2 absorption over 23 years. Our model successfully reproduced sea surface partial pressure of CO2 data. The results suggest the total amount of CO2 absorbed by oceans is higher at night than during the day. This difference arises from a combination of factors like temperatures, winds, photosynthesis, and respiration. Understanding these daily fluctuations can improve predictions of ocean CO2 uptake. It may also help explain why current carbon budget calculations are not fully balanced-an issue scientists have grappled with. Overall, this pioneering study highlights the value of LiDAR's unique day-night ocean data coverage. The findings advance knowledge of ocean carbon cycles and their role in climate regulation. They underscore the need to incorporate day-night variability when assessing the ocean's carbon sink capacity.

Comprehensive, Continuous, and Vertical Measurements of Seawater Constituents with Triple-Field-of-View High-Spectral-Resolution Lidar.

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties.

Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However, accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of, to the best of our knowledge, the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient Kd and the particulate backscattering coefficient bbp at 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea, which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% for Kd and bbp, respectively, corresponding to an improvement of 2.7-13.5 and 4.9-44.1 times, respectively, with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge.

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from satellite ocean colour sensor: application to absorbing aerosols.

This paper presents a new development of the NeuroVaria method. NeuroVaria computes relevant atmospheric and oceanic parameters by minimizing the difference between the observed satellite reflectances and those computed from radiative transfer simulations modelled by artificial neural networks. Aerosol optical properties are computed using the Junge size distribution allowing taking into account highly absorbing aerosols. The major improvement to the method has been to implement an iterative cost function formulation that makes the minimization more efficient. This implementation of NeuroVaria has been applied to sea-viewing wide field-of-view sensor (SeaWiFS) imagery. A comparison with in situ measurements and the standard SeaWiFS results for cases without absorbing aerosols shows that this version of NeuroVaria remains consistent with the former. Finally, the processing of SeaWiFS images of a plume of absorbing aerosols off the US East coast demonstrate the ability of this improved version of NeuroVaria to deal with absorbing aerosols.