The monitoring of water biophysical parameters and the management of aquatic ecosystems are crucial to address the current state of degradation of inland and coastal waters. Water quality monitoring not only supports management decisions, but also provides important insights to better understand changing structural and functional processes. To date, most applications developed for inland waters have relied on multispectral and medium to coarse resolution satellites, while a new generation of spaceborne imaging spectroscopy is now available and future global missions are under development. This study aims to evaluate both the Level 1 PRISMA products, in different processing versions, and the Level 2 products based on coincident in-situ data collected in different aquatic ecosystems. Examples of water quality mapping in different use cases will then be provided, also in synergy with other missions such as EnMAP and Sentinel-2.

Climate change is having a significant negative impact on freshwater systems, which provide multiple ecosystem services. In this context, the present study aims to show an overview of the main objectives achieved by exploiting the hyperspectral reflectance data provided by the PRISMA sensor to map aquatic ecosystems. Water quality products were generated using three different approaches: the bio-optical model BOMBER, adaptive semi-empirical algorithms, and machine learning models. These methods were tested in very different waterbodies worldwide: five lakes, two lagoons and one river. To assess the accuracy of the water quality products, comparisons were performed with reference measurements. The results showed an average R2= 0.70 and encourage using PRISMA data for aquatic applications in synergy with existing multispectral and future hyperspectral data.

COVID-19 lockdown measures brought to a drastic reduction of anthropic impacts on the environment, including the marine-coastal system. EO data have the potential to monitor and diagnose the subsequent effects of the lockdown in terms of water quality. The ESA-RACE project SOON aims to link complex patterns that may arise from the environmental changes due to COVID-19 lockdown by using EO data, also seeking to assess connectivity between inland and marine system. Within this frame, here we present a holistic, satellite-based analysis of the spatial-temporal variability of environmental parameters in the North Adriatic Sea (NAS; Mediterranean basin), exploiting the synergy of OC and SST products from different sensors, as well as in situ hydrologic data. Our analysis indicates a favorable interplay of environmental variability and reduction of anthropogenic activity that results in negative anomalies of Chlorophyll-a and Total Suspended Matter with respect to the climatologic values.

This study presents an assessment of PRISMA (PRecursore IperSpettrale della Missione Applicativa) for water applications, by including a general description of the mission and by focusing on the standard Level 1 (L1) and Level 2 (L2) products acquired on different aquatic systems. A preliminary analysis on the estimation of signal-to-noise ratio (SNR) from L1 PRISMA products in the visible near infrared spectra (VISNIR) is indicating values comparable to Sentinel-2 MSI from literature, apart at short wavelengths where PRISMA SNR is lower. Few examples of L2 products in terms of water reflectance are also presented to encourage the exploitation of PRISMA for aquatic ecosystem mapping. The results of this study are indicating that even if some applications might be developed with the standard L2 products, an ad-hoc processing might be necessary to include the specific requirements of atmospheric correction of water targets. Further analyses with a higher number of imagery data are however recommended to fully characterize the PRISMA products.

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.

This letter presents an application of continuous wavelet analysis, providing a new semi-empirical approach to estimate Chlorophyll-a (Chl-a) in optically complex inland waters. Traditionally spectral narrow band ratios have been used to quantify key diagnostic features in the remote sensing signal to estimate concentrations of optically active water quality constituents. However, they cannot cope easily with shifts in reflectance features caused by multiple interactions between variable absorption and backscattering effects that typically occur in optically complex waters. We use continuous wavelet analysis to detect Chl-a features at various wavelengths and frequency scales. Using the wavelet decomposition, we build a 2-D correlation scalogram between in situ pond reflectance spectra and in situ Chl-a concentration. By isolating the most informative wavelet regions via thresholding, we could relate all five regions to known inherent optical properties. We select the optimal feature per region and compare them to three well-known narrow band ratio models. For this experimental application, the wavelet features outperform the NIR-red models, while fluorescence line height (FLH) yield comparable results. Because wavelets analyze the signal at different scales and synthesize information across bands, we hypothesize that the wavelet features are less sensitive to confounding factors, such as instrument noise, colored dissolved organic matter, and suspended matter.

This paper outlines a methodology for the estimation of the environmental noise equivalent reflectance in aquatic remote sensing imagery using an object-based segmentation approach. Noise characteristics of remote sensing imagery directly influence the accuracy of estimated environmental variables and provide a framework for a range of sensitivity, sensor specification, and algorithm design studies. The proposed method enables estimation of the noise equivalent reflectance covariance of remote sensing imagery through homogeneity characterization using image segmentation. The method is first tested on a synthetic data set with known noise characteristics and is successful in estimating the noise equivalent reflectance under a range of segmentation structures. Testing on a Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) hyperspectral image in a coral reef environment shows the method to produce comparable noise equivalent reflectance estimates in an optically shallow water environment to those previously derived in optically deep water. This method is of benefit in aquatic studies where homogenous regions of optically deep water were previously required for image noise estimation. The ability of the method to characterize the covariance of an image is of significant benefit when developing probabilistic inversion techniques for remote sensing.

Consistent and accurate information on inland water quality over wider areas of the Australian continent are required to assess current condition and trends in response to key environmental and climatic impacts. Optical remote sensing offers a method to objectively assess this over multiple spatial scales provided retrieval algorithms are accurate. Here, we present the results of initial research aimed at exploring the optical variability in Australian inland waters and of linear matrix inversion algorithms applied to both in situ reflectance spectra and high resolution satellite data to retrieve water inland water quality parameters. In situ sampling reveals a high degree of optical variability both within and between lakes across the regions sampled with regional patterns evident; sub-tropical and tropical lakes exhibited greater optical complexity than deep lakes in mid-latitude regions. Clustering analysis indicated the presence of 8 different optical water types in the water bodies measured. The ability of the linear matrix inversion algorithm to map water quality, tested on in situ reflectance and WorldView2 image datasets, showed relative accuracy when parameter sets were sufficient to achieve algorithm closure. Improved algorithm parameterization will be required to account for the high degree in spatial and temporal optical variability observed in Australian inland waters.

This paper argues for the development of a quantitative global inland water quality product based on satellite optical remote sensing. Water quality is a critical component of global fresh water security and ecosystem health, yet is often overlooked when global analyses of water security are undertaken. In the face of declining surface measurements and datasets across the globe, alternatives to conventional water quality measurement are required. The case for an optical remote sensing based inland water quality product is a strong one. Global products of ocean color and their dissemination infrastructure are operational and widely used, providing a framework for global inland products. Furthermore, coastal and inland water quality algorithms based on spectral inversion algorithms are now sufficiently mature to cope with the greater variability of inherent optical properties in these systems. While these algorithms provide the greatest promise for reliable, robust and simultaneous retrieval of several water quality variables across sensors, limited knowledge of the bio-optical properties of inland waters and limited validation currently prevent global implementation. Internationally coordinated efforts are required to accumulate representative bio-optical data to improve our understanding of the optical complexity and variability of inland waters.

Accurate baseline data of macrophyte extent is vital in estuarine monitoring. Previous techniques have often been laborious and subjective, while a purely empirical methodology often precludes transferring the method to other systems. The development of objective physics-based inversions models allows for the retrieval of; water depth, substratum composition and concentration of the water constituents from hyperspectral imagery. This paper describes approaches required to apply this method to QuickBird multispectral data from 2003 and 2008 over an estuarine lake. The addition of the inversion models quality control, improved the classification accuracy.

The successful launch of Hyperion in November 2000 bridged the gap between the high-resolution (spatial and spectral) airborne remote sensing and the lower resolution satellite remote sensing. Although designed as a technical demonstration for land applications, Hyperion was tested for its capabilities over a range of water targets in Eastern Australia, including Moreton Bay in southern Queensland. Moreton Bay was the only Australian Earth Observing 1 (EO-1) Hyperion coastal site used for calibration/validation activities. This region was selected due to its spatial gradients in optical depth, water quality, bathymetry, and substrate composition. A combination of turbid and humic river inputs, as well as the open ocean flushing, determines the water quality of the bay. The field campaigns were coincident with Hyperion overpasses, retrieved inherent optical properties, apparent optical properties, substrate reflectance spectra, and water quality parameters. Environmental noise calculations demonstrate that Hyperion has sufficient sensitivity to detect optical water quality concentrations of colored dissolved organic matter, chlorophyll, and suspended matter in the complex waters of Moreton Bay. A methodology was developed integrating atmospheric and hydrooptical radiative transfer models (MODTRAN-4, Hydrolight) to estimate the underwater light field. A matrix inversion method was applied to retrieve concentrations of chlorophyll, colored dissolved organic matter, and suspended matter, which were comparable to those estimated in the field on the days of the overpass.