No abstract

There is currently a scarcity of paired in-situ aquatic optical and biogeophysical data for productive inland waters, which critically hinders our capacity to develop and validate robust retrieval models for Earth Observation applications. This study aims to address this limitation through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which is the first to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size- and type-specific phytoplankton inherent optical properties (IOPs) for mixed eukaryotic/cyanobacteria assemblages; 2) calculations of mixed assemblage chlorophyll-a (chl-a) fluorescence; 3) modeled phycocyanin concentration derived from assemblage-based phycocyanin absorption; 4) and paired sensor-specific top-of-atmosphere reflectances, including optically extreme cases and the contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships of concentrations and IOPs to those of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, and used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and IOPs over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. The results of this work represent a significant leap forward in our capacity for routine, global monitoring of inland 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.

Paired measurements of phytoplankton absorption and backscatter, the inherent optical properties central to the interpretation of ocean colour remote sensing data, are notoriously rare. We present a dataset of Chlorophyll a (Chl a) -specific phytoplankton absorption, scatter and backscatter for 17 different phytoplankton groups, derived from first principles using measured in vivo pigment absorption and a well-validated semi-analytical coated sphere model which simulates the full suite of biophysically consistent phytoplankton optical properties. The optical properties of each simulated phytoplankton cell are integrated over an entire size distribution and are provided at high spectral resolution. The model code is additionally included to enable user access to the complete set of wavelength-dependent, angularly resolved volume scattering functions. This optically coherent dataset of hyperspectral optical properties for a set of globally significant phytoplankton groups has potential for use in algorithm development towards the optimal exploitation of the new age of hyperspectral satellite radiometry.