Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery

Inter-Comparison of Methods for Lake Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis

Different methods are available for retrieving chlorophyll-a (Chl-a) in inland waters from optical imagery, but there is still a need for an inter-comparison among the products. Such analysis can provide insights into the method selection, integration of products, and algorithm development. This work aims at inter-comparison and consistency analyses among the Chl-a products derived from publicly available methods consisting of Case-2 Regional/Coast Colour (C2RCC), Water Color Simulator (WASI), and OC3 (3-band Ocean Color algorithm). C2RCC and WASI are physics-based processors enabling the retrieval of not only Chl-a but also total suspended matter (TSM) and colored dissolved organic matter (CDOM), whereas OC3 is a broadly used semi-empirical approach for Chl-a estimation. To pursue the inter-comparison analysis, we demonstrate the application of Sentinel-2 imagery in the context of multitemporal retrieval of constituents in some Italian lakes. The analysis is performed for different bio-optical conditions including subalpine lakes in Northern Italy (Garda, Idro, and Ledro) and a turbid lake in Central Italy (Lake Trasimeno). The Chl-a retrievals are assessed versus in situ matchups that indicate the better performance of WASI. Moreover, relative consistency analyses are performed among the products (Chl-a, TSM, and CDOM) derived from different methods. In the subalpine lakes, the results indicate a high consistency between C2RCC and WASI when a_CDOM (440) < 0.5 m^-1, whereas the retrieval of constituents, particularly Chl-a, is problematic based on C2RCC for high-CDOM cases. In the turbid Lake Trasimeno, the extreme neural network of C2RCC provided more consistent products with WASI than the normal network. OC3 overestimates the Chl-a concentration. The flexibility of WASI in the parametrization of inversion allows for the adaptation of the method for different optical conditions. The implementation of WASI requires more experience, and processing is time demanding for large lakes. This study elaborates on the pros and cons of each method, providing guidelines and criteria on their use

Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2

The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water qual-ity parameters from Landsat-9 imagery based on both physics-based and machine learning mod-eling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Land-sat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality prod-ucts derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R2 of 0.89) compared to Sentinel-2 (R2= 0.71). The improved constituent re-trieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this as-sumption

Rivers Hydromorphological Characterization from High Resolution Remotely Sensed Data

Remote sensing techniques could enable remarkable advances in characterizing rivers hydromorphology by providing spatially and temporally explicit information. Remote mapping of hydromorphology can play a decisive role in a wide range of river science and management applications including habitat modeling and river restoration. High resolution satellite imagery (HRSI) has recently emerged as potentially powerful means of mapping riverine environments. This research aims to develop advanced methodologies for processing HRSI to map and quantify a set of key hydromorphological attributes including: (1) river boundaries, (2) bathymetry and (3) riverbed types and compositions. Boundary pixels of rivers are subject to spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super resolution mapping (SRM) are focused as two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for NDWI (OBA-NDWI) is proposed to identify the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as a predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) and interpolation-based algorithms are applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2=90%, RMSE=7% for WorldView-2 (WV-2) image and R2=87%, RMSE=9% for Geoeye image). The spectral bands of WV-2 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas about 10% with respect to conventional hard classification. This research introduces multiple optimal depth predictors analysis (MODPA) that combines previously developed depth predictors along with other measures such as the intensity components of HSI color space. To avoid over-fitting of the linear model, statistically optimal predictors are selected based on one of partial least square (PLS), stepwise and principal component (PC) regressions. The primary focus of this study is on shallow and clearly flowing streams where substrate variability could have pronounced effect on depth retrievals. Spectroscopic experiments are performed in controlled condition of a hydraulic laboratory to examine the robustness of bathymetry models with respect to changes in bottom types. Further, simulations from radiative transfer modeling are used to extend the analysis by isolating the effect of inherent optical properties (IOPs) and also by investigating the performance of bathymetry models in optically complex and also deeper streams. Bathymetry of Sarca, a shallow river in Italian Alps, is also mapped using a WorldView-2 (WV-2) image where the atmospheric compensation (AComp) product is evaluated for the first time. Results indicate the robustness of multiple-predictor models particularly MODPA rather than single-predictor models such as optimal band ratio analysis (OBRA) with respect to heterogeneity of bottom types, IOPs and atmospheric effects. This study suggests extra predictors when the multiple regression is assisted with an optimal predictors selection process (e.g. MODPA). The extra predictors enhance the accuracy of depth retrievals particularly in optically complex waters and also for low spectral resolution imagery (e.g. GeoEye). Further, enhanced spectral resolution of WV-2 compared to GeoEye improves the bathymetry retrievals. MODPA based on PLS regression provided improvements on the order of 0.05 R2 and 0.7 cm RMSE compared to multiple Lyzenga and 0.18 R2 and 2 cm RMSE compared to OBRA using AComp reflectances of WV-2 for Sarca River with a maximum 0.8 m depth. In addition, a theoretical approach namely hydraulically assisted bathymetry (HAB) is assessed and further modified for calibration of bathymetry models that provided comparable results with the empirical calibration approach. Substrate mapping in fluvial systems has not received as much attention as that in nearshore optically shallow waters of inland and coastal areas. The research to date has been primarily based on surface spectral reflectance data without accounting for water column attenuations. This study aims at retrieving the bottom reflectances in shallow rivers and then examining the effectiveness of inferred bottom spectra in mapping of substrate types. Bathymetry and diffuse attenuation coefficient (kd) are derived from above-water reflectances for which some in-situ/known depths are required. Following the retrievals of depth and kd, bottom reflectances are estimated based on a water column correction method. Moreover, the efficacy of vegetation indices (VIs) is examined for making distinction among the densities of submerged aquatic vegetation (SAV) using either above-water or retrievals of bottom reflectances. This research benefits, for the first time, from three different approaches including controlled spectroscopic measurements in a hydraulic lab, simulations from radiative transfer modeling and an 8-band WordView-3 (WV-3) image. The results indicate the significant enhancements of streambed mapping using inferred bottom reflectances than using above-water spectra. This is evident, for instance, on clustering of three bottom types using simulated spectra with 20% enhancement of overall accuracy. Deep-water correction demonstrated to have most of an impact on retrievals of bottom reflectances only in NIR bands when the water column is relatively thick (> 0.5 m) and/or when the water is turbid. The red-edge (RE) band of WV-3/WV-2 improves remarkably the detection of SAV densities based on the VIs either using above-water or retrieved bottom spectra. Further, the simulated spectra suggest that enhanced spectral resolution of 8-band WV-3 leads to improvements in streambed mapping compared to traditional 4-band imagery. This study demonstrated the feasibility of retrieving bottom reflectances and mapping SAV densities from space in a shallow river using the WV-3 image (user and producer accuracies of 67% and 60% in average for three levels of SAV densities). Moreover, the feasibility of mapping grain size classes is assessed using spectral information based on laboratory experiments coupled with simulations. The changes in grain sizes affect the magnitude of reflectances while the shape of spectra remains almost identical. This characteristic feature demonstrated high potentials for mapping grain size classes by retrieving the bottom reflectances. In summary, HRSI provided promising results and effective means of mapping the selected hydromorphological attributes of shallow rivers in spatially continuous and in large extents

Reconstruction of River Boundaries at Sub-Pixel Resolution: Estimation and Spatial Allocation of Water Fractions

Boundary pixels of rivers are subject to a spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super-resolution mapping (SRM) are conducted in two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for the normalized difference water index (OBA-NDWI) is proposed for identifying the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) algorithm and interpolation-based algorithms are also applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2 = 0.9, RMSE = 7% for eight-band WorldView-3 (WV-3) image and R2 = 0.87, RMSE = 9% for GeoEye image). The spectral bands of WV-3 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas by about 10% with respect to conventional hard classificatio

Water quality retrieval and algal bloom detection using high-resolution cubesat imagery

none2noRecent advancements in developing small satellites known as CubeSats provide an increasingly viable means of characterizing the dynamics of inland and nearshore waters with an unprecedented combination of high revisits (&lt; 1 day) with a high spatial resolution (meter-scale). Estimation of water quality parameters can benefit from the very high spatiotemporal resolution of CubeSat imagery for monitoring subtle variations and identification of hazardous events like algal blooms. In this study, we present the first study on retrieving lake chlorophyll-a (Chl-a) concentration and detecting algal blooms using imagery acquired by the PlanetScope constellation which is currently the most prominent source of CubeSat data. Moreover, the concentration of total suspended matter (TSM) is retrieved that is an indicator of turbidity. The retrievals are based upon inverting the radiative transfer model. The low spectral resolution (four bands) of PlanetScope imagery poses challenges for such a physics-based inversion due to spectral ambiguities in optically-complex waters like inland waters. To deal with this issue, the number of variable parameters is minimized through inverse modeling. Given the significance of having high-quality water-leaving reflectance for physics-based models, a variable parameter (gdd) is considered to compensate for the atmospheric and sun-glint artifacts. The results compared to the in-situ data indicate high potentials of PlanetScope imagery in retrieving water quality parameters and detection of algal blooms in our case study (Lake Trasimeno, Italy).noneNiroumand Jadidi, Milad; Bovolo, FrancescaNiroumand Jadidi, Milad; Bovolo, Francesc

Influential Visual Design Parameters on TV Weather Maps

Information transformation on television weather maps (TVWMs) is influenced by visual elements for a broad range of viewers. This research emphasizes the cartographic aspects of TVWMs through evaluating their visual variables. Currently defined visual variables including basic, dynamic and motion variables are investigated and some suggestions are made to improve their application on TVWMs. The rates of the represented visual information within different frames and the related standard deviation are proposed as measures to improve the performance of the ‘duration’ dynamic variable. The concept of ‘visual expressions’ is introduced, and their applications at the organisational level of map design are discussed. Such expressions (including background, boundary, spatial order, zoom and overview maps) are examined as tools for ‘user orientation’ in particular, and their role as dominant parameters in TVWMs’ cartographic communication is considered. Their incorporation in TVWMs of a number of global news channels is evaluated. Firstly, the concepts of visual design parameters are utilized as a foundation for an analytical evaluation, then an empirical evaluation is carried out based on a statistical investigation of a sample of TV viewers. The resulting ranking order and correlation coefficients for each of the elements shows a firm agreement, corroborating views on the importance and proficiency of the visual elements in communicating weather information. As a result, TVWMs of well-known global TV channels (BBC, Euronews, France24, PressTV) are ranked with respect to the effectiveness of their designs

Temporally transferable machine learning model for total suspended matter retrieval from Sentinel-2

The empirical (regression-based) models have long been used for retrieving water quality parameters from optical imagery by training a model between image spectra and collocated in-situ data. However, a need clearly exists to examine and enhance the temporal transferability of models. The performance of a model trained in a specific period can deteriorate when applied at another time due to variations in the composition of constituents, atmospheric conditions, and sun glint. In this study, we propose a machine learning approach that trains a neural network using samples distributed in space and time, enabling the temporal robustness of the model. We explore the temporal transferability of the proposed neural network and standard band ratio models in retrieving total suspended matter (TSM) from Sentinel-2 imagery in San Francisco Bay. Multitemporal Sentinel-2 imagery and in-situ data are used to train the models. The transferability of models is then examined by estimating the TSM for imagery acquired after the training period. In addition, we assess the robustness of the models concerning the sun glint correction. The results imply that the neural network-based model is temporally transferable (R2 ≈ 0.75; RMSE ≈ 7 g/m3 for retrievals up to 70 g/m3) and is minimally impacted by the sun glint correction. Conversely, the ratio model showed relatively poor temporal robustness with high sensitivity to the glint correction

Sub-pixel mapping of water boundaries using pixel swapping algorithm (case study: Tagliamento River, Italy)

Taking the advantages of remotely sensed data for mapping and monitoring of water boundaries is of particular importance in many different management and conservation activities. Imagery data are classified using automatic techniques to produce maps entering the water bodies’ analysis chain in several and different points. Very commonly, medium or coarse spatial resolution imagery is used in studies of large water bodies. Data of this kind is affected by the presence of mixed pixels leading to very outstanding problems, in particular when dealing with boundary pixels. A considerable amount of uncertainty inescapably occurs when conventional hard classifiers (e.g., maximum likelihood) are applied on mixed pixels. In this study, Linear Spectral Mixture Model (LSMM) is used to estimate the proportion of water in boundary pixels. Firstly by applying an unsupervised clustering, the water body is identified approximately and a buffer area considered ensuring the selection of entire boundary pixels. Then LSMM is applied on this buffer region to estimate the fractional maps. However, resultant output of LSMM does not provide a sub-pixel map corresponding to water abundances. To tackle with this problem, Pixel Swapping (PS) algorithm is used to allocate sub-pixels within mixed pixels in such a way to maximize the spatial proximity of sub-pixels and pixels in the neighborhood. The water area of two segments of Tagliamento River (Italy) are mapped in sub-pixel resolution (10m) using a 30m Landsat image. To evaluate the proficiency of the proposed approach for sub-pixel boundary mapping, the image is also classified using a conventional hard classifier. A high resolution image of the same area is also classified and used as a reference for accuracy assessment. According to the results, sub-pixel map shows in average about 8 percent higher overall accuracy than hard classification and fits very well in the boundaries with the reference map