Constructing a robust ocean color (OC) record (e.g., water transparency, phytoplankton absorption) for long-term assessments of coastal and inland water ecosystems from past, present, and future missions requires high-quality spectral remote sensing reflectance (${R} _{\text {rs}}$) products. Using the GLORIA dataset (Lehmann et al., 2023), we evaluated the quality of${R} _{\text {rs}}$products from the moderate resolution imaging spectroradiometer (MODIS on Terra and Aqua), medium resolution imaging spectrometer (MERIS), and visible infrared imaging radiometer suite (VIIRS) processed via the two-band heritage atmospheric correction method (a combination of near-infrared and shortwave infrared bands) available in the SeaWiFS Analysis Data Analysis System (SeaDAS). Overall, retrieval residuals are consistent within a few percentages among the four missions. Median residuals ranged from$\sim $20% in the$\sim $550-nm band to$>$60% in the$\sim $412-nm bands. Spectrally averaged root mean squared differences for all the missions were$\sim $0.0024 sr$^{-1}$with one standard deviation of$\sim $0.001 sr$^{-1}$. The corresponding (median) biases in the visible bands varied from −60% to −3%, with the largest biases identified in MERIS and VIIRS products. Despite the lower sensitivity of band-ratio algorithms to residuals in specific spectral regions [e.g., OC3 chlorophyll-a algorithm is less prone to residuals in${R} _{\text {rs}}$($\lambda >600$nm)], other algorithms or downstream products that leverage all the visible bands are highly compromised. We underscore the need to improve the quality of${R} _{\text {rs}}$products, thereby enabling the reconstruction of baseline OC products of high caliber in global coastal and inland waters that are often near human activity.

Machine Learning (ML) models have emerged as powerful and accurate tools for the estimation of the properties of the water column from optical remote sensing data. In particular, machine learning models with a probabilistic output like Mixture Density Networks (MDNs) and Bayesian Neural Network with MC-Dropout (BNN-MC) have emerged as the front-runners for the task of Chlorophyll-a estimation. Another advantage of these models is that they also provide a method to capture the confidence associated with the prediction of the machine learning model for each sample. This paper extends the analysis of the performance of such models to the task of simultaneous estimation of a set of water quality indicators, to provide the water resource managers with a more complete picture on the state of the water column. The paper compares and contrasts the performance of the BNN-MC and MDN in terms of both parameter and uncertainty estimation. Further, the effect of the spectral resolution on the model performance is also analyzed by verifying model performance at both multispectral and hyperspectral sensor-resolutions.

Mixture density networks (MDNs) have emerged as a powerful tool for estimating water-quality indicators, such as chlorophyll-a (Chl$a$) from multispectral imagery. This study validates the use of an uncertainty metric calculated directly from Chl$a$estimates of the MDNs. We consider multispectral remote sensing reflectance spectra ($R_{\text {rs}}$) for three satellite sensors commonly used in aquatic remote sensing, namely, the ocean and land colour instrument (OLCI), multispectral instrument (MSI), and operational land imager (OLI). First, a study on a labeled database of colocated in situ Chl$a$and$R_{\text {rs}}$measurements clearly illustrates that the suggested uncertainty metric accurately captures the reduced confidence associated with test data, which is drawn for a different distribution than the training data. This change in distribution maybe due to: 1) random noise; 2) uncertainties in the atmospheric correction; and 3) novel (unseen) data. The experiments on the labeled in situ dataset show that the estimated uncertainty has a correlation with the expected predictive error and can be used as a bound on the predictive error for most samples. To illustrate the ability of the MDNs in generating consistent products from multiple sensors, per-pixel uncertainty maps for three near-coincident images of OLCI, MSI, and OLI are produced. The study also examines temporal trends in OLCI-derived Chl$a$and the associated uncertainties at selected locations over a calendar year. Future work will include uncertainty estimation from MDNs with a multiparameter retrieval capability for hyperspectral and multispectral imagery.

In the above article[1], the references in the following paragraph should be corrected as shown below. This excerpt appears in the first column on page 4 of the above article.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched to continue the legacy of the MODerate Resolution Imaging Spectroradiometer (MODIS). Despite recent studies demonstrating the use of VIIRS observations over inland waters, VIIRS has not been widely used to generate water quality products (e.g., chlorophyll-a (Chl-a), suspended particulate matter (SPM)) in relatively large turbid lakes. This study examines the quality of VIIRS-derived remote sensing reflectance (Rrs) from four different atmospheric-correction processors with matchups from 13 lakes sized between 107 km2and 2573 km2across the eastern plain of China. NOAA’s operational Rrsoutperforming Rrsretrieved by other state-of-the-art algorithms were shown to contain mean uncertainties of 57%, 33%, 20%, 28% for Rrs(486), Rrs(551), Rrs(671), and Rrs(745), respectively, which induced ~55% uncertainty in satellite-retrieved SPM and Chl-afrom recently developed algorithms in the studied lakes. A deep neural network was developed for simultaneous retrievals of Chl-aand SPM from VIIRS Rayleigh-corrected reflectance to improve accuracy. The model with satisfactory accuracy (mean uncertainty of 28% for Chl-aand 20% for SPM) outperformed other machine learning approaches and nearly halved uncertainties compared to those obtained from satellite-derived Rrsproducts. Within the 2012-2020 period, high-quality VIIRS-derived Chl-aand SPM across 61 lakes in eastern China had evident interannual variability in SPM but insignificant temporal variations in Chl-a. This study provides validated, high-quality, basin-scale VIIRS-derived Chl-aand SPM products in eastern China during the past decade. Our results offer a strategy for improving regional water quality products from VIIRS data.

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large ($N =905$) database of colocatedin situradiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ($R_{\mathrm {rs}}$) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of$R_{\mathrm {rs}}$spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between$\sim 73$% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.

In order to control the spread of the pandemic of Corona-Virus Disease 2019 (COVID-19), lockdowns of various durations and intensities have been established in many countries over the world all through the year 2020. The trilateral dashboard jointly implemented by NASA, JAXA and ESA aims at exploiting remote-sensing data to evaluate the impact of these restrictions, and subsequent recovery phases on many different environmental, agriculture and economic indicators. More specifically, this paper presents the indicators implemented to monitor the impact of COVID-19 restrictions on Water Quality, together with preliminary analysis results over a few Areas of Interest.

With the increasing need to construct long-term climate-quality data records to understand, monitor, and predict climate variability and change, it is vital to continue systematic satellite measurements along with the development of new technology for more quantitative and accurate observations. The Suomi National Polar-orbiting Partnership mission provides continuity in monitoring the Earth's surface and its atmosphere in a similar fashion as the heritage MODIS instruments onboard the National Aeronautics and Space Administration's Terra and Aqua satellites. In this paper, we aim at quantifying the consistency of Aqua MODIS and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Reflectance (LSR) and NDVI products as related to their inherent spatial sampling characteristics. To avoid interferences from sources of measurement and/or processing errors other than spatial sampling, including calibration, atmospheric correction, and the effects of the bidirectional reflectance distribution function, the MODIS and VIIRS LSR products were simulated using the Landsat-8's Operational Land Imager (OLI) LSR products. The simulations were performed using the instruments' point spread functions on a daily basis for various OLI scenes over a 16-day orbit cycle. It was found that the daily mean differences due to discrepancies in spatial sampling remain below 0.0015 (1%) in absolute surface reflectance at subgranule scale (i.e., OLI scene size). We also found that the MODIS-VIIRS product intercomparisons appear to be minimally impacted when differences in the corresponding view zenith angles (VZAs) are within the range of -15° to -35° (VZAV - VZAM), where VIIRS and MODIS footprints resemble in size. In general, depending on the spatial heterogeneity of the OLI scene contents, per-grid-cell differences can reach up to 20%. Further spatial analysis of the simulated NDVI and LSR products revealed that, depending on the user accuracy requirements for product intercomparisons, spatial aggregations may be used. It was found that if per-grid-cell differences on the order of 10% (in LSR or NDVI) are tolerated, the product intercomparisons are expected to be immune from differences in spatial sampling.

The operational Land Imager (OLI) aboard Landsat 8 was launched in February 2013 to continue the Landsat's mission of monitoring earth resources at relatively high spatial resolution. Compared to Landsat heritage sensors, OLI has an additional 443-nm band (termed coastal/aerosol (CA) band), which extends Landsat's potential for mapping/monitoring water quality in coastal/inland waters. In addition, OLI's pushbroom design allows for longer integration time and, as a result, higher signal-to-noise ratio (SNR). Using a series of radiative transfer simulations, we provide insights into the radiometric sensitivity of OLI when studying coastal/inland waters. This will address how the changes in water constituents manifest at the sensor level and whether the changes are resolvable (focal plane) relative to OLI's overall noise1.