While access to discharge data is key to hydrologic studies, it is a serious obstacle in ungauged basins. Currently, Sentinel-2 imagery at high spatiotemporal resolution offers a unique opportunity to infer the relation between pixel-based discharge rate and surface reflectance. One promising approach in this respect has been to find the complex relationship between river discharge and the spectral ratio between two benchmark pixels, namely the wet and dry pixels, whose dynamics resembles river discharge variation. However, this has been challenging due to the adverse impact of soil moisture and mixed land cover on the spectral behavior of the dry pixel. The selection of the wet pixel must also guarantee sufficient sensitivity of its spectral response to water depth fluctuations. To tackle the above issues, in this study, we developed a novel framework that automatizes the selection of the wet and dry pixels by using Sentinel-2 imagery. We also introduced the Normalized Difference Discharge Index (NDDI), as the best band combination, to predict river discharge. We used linear regression with leave-one-out cross-validation as the prediction model, which leverages limited satellite data due to the cloud cover. By implementing the developed framework at multiple gauged points across the continental United States, the best location of the dry pixel was consistently found in urban pixels whose longwave reflectance fall within a certain range. By analyzing the pixel-wised correlation coefficient between surface reflectance at NIR band and river discharge across the studied river widths, we found that the best wet pixels are located along river banks with shallow water depth. These pixels were characterized by the average reflectance higher than the 98th percentile in the green band. Finally, by testing over 4000 band combinations as input to the river discharge prediction model, we found that the normalized difference between B11 and B4 for the wet pixel, as well as the B11 ratioing between the dry and wet pixels yielded the most accurate predictions with R2 = 0.88 and R2 = 0.73, respectively.
6Improving river medium–high flow estimation by CM Hierarchical Classification (CMHC) method using Sentinel-2 imagery
Improving river medium–high flow estimation by CM Hierarchical Classification (CMHC) method using Sentinel-2 imagery
5Comparison of Sentinel-2 Multispectral Imager (MSI) and Landsat 8 Operational Land Imager (OLI) for Vegetation Monitoring
The availability of the coarse to moderate resolution no-cost remote sensing data and advances in image processing algorithms have exponentially increased the usage of geo-spatial technology in the last few decades. The latest Sentinel-2 Multispectral Imager (MSI) provides the surface reflectance data in VNIR and SWIR ranges since 2015 at higher spatial, temporal, and spectral resolution compared to the Landsat multispectral sensors, which have been providing such data since 1970s. The symmetry in spectral bands, sensor’s spectral response, spatial and radiometric resolution of Sentinel-2 MSI with the Landsat-8 OLI (Operational Land Imager) enables their integrated and complimentary use. In this study, we have compared the surface reflectance and vegetation indices (such as NDVI and EVI) values obtained from MSI and OLI sensors in four homogeneous land use land cover (LULC) features as cropland, agriculture fallow land, dense forest and open forest. The assessment is carried out for pre- and post-monsoon seasons over the Banki sub-division region of Cuttack district, Odisha, India. For all the LULC classes, high similarity is observed in the surface reflectance values in each band except NIR, green and red band. Similarly, for both the vegetation indices derived from Landsat 8 and Sentinel-2 data, high correlation with lower RMSE is observed for all the LULC classes. The correlation (R2) for cropland varied between 0.87 and 0.96, which varied between 0.56 and 0.97 for agriculture fallow, between 0.58 and 0.9 for dense forest, and between 0.68 and 0.87 for open forest. The surface reflectance pattern obtained for different vegetated features are similar for Landsat 8 and Sentinel-2. However, significantly a higher surface reflectance is observed for Landsat 8 in NIR band followed by red and green bands, where the differences are low for blue, SWIR1 and SWIR2 bands. The comparative assessment of indices suggests a higher correlation in values between Landsat 8 and Sentinel-2 for homogeneous features compared to heterogeneous class as forest. Thus, the integrated or complementary use of Landsat 8 and Sentinel-2 for heterogeneous features may induce some biases with a limited accuracy.
Read more1Atmospheric Correction Assessment and Normalization Procedure for Coupling Sentinel-2 and Worldview-3 Imagery
Earth Observing Systems (EOS) are a valuable tool for vegetation monitoring. Coupling information from different spaceborne sensors allows reducing the revisit time and a more accurate Earth surface processes monitoring. However, combining surface reflectance (SR) from different EOS requires compensating for atmospheric disturbances and viewing angle induced effects. The objectives of this study were i) to compare the atmospherically corrected Sentinel-2 (S2) and WorldView-3 (WV3) SR with ground-truth spectra acquired with a FieldSpec and ii) to propose and validate a signal normalization procedure for coupling S2 and WV3 imagery. The S2 imagery showed a reliable match with field spectra, however SR from WV3 was significantly different. These discrepancies likely resulted from the off-nadir acquisition angles. The signal normalization procedure, based on calibrating each WV3 band against ground-truth spectra, allowed an accurate coupling of S2 and WV3 imagery ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{RMSE} < 0.007$</tex> in visible bands and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{RMSE}=0.018$</tex> in the NIR band).
Read more66Estimating mangrove leaf area index based on red-edge vegetation indices: A comparison among UAV, WorldView-2 and Sentinel-2 imagery
Estimating mangrove leaf area index based on red-edge vegetation indices: A comparison among UAV, WorldView-2 and Sentinel-2 imagery
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9Beyond river discharge gauging: hydrologic predictions using remote sensing alone
This study suggests a radical approach to hydrologic predictions in ungauged basins, addressing the long standing challenge of issuing predictions when in-situ river discharge does not exist. A simple but powerful rationale for measuring and modeling river discharge is proposed, using coupled advances in hydrologic modeling and satellite remote sensing. Our approach presents a Surrogate River discharge driven Model (SRM) that infers Surrogate River discharge (SR) from remotely sensed microwave signals with the ability to mimic river discharge in varying topographies and vegetation cover, which is then used to calibrate a hydrological model enabling physical realism in the resulting river discharge profile by adding an estimated mean of river discharge via the Budyko framework. The strength of SRM comes from the fact that it only uses remotely sensed data in prediction. The approach is demonstrated for 130 catchments in the Murray Darling Basin (MDB) in Australia, a region of high economic and environmental importance. The newly proposed SR (SRL, representing L-band microwave) boosts the Nash-Sutcliffe Efficiency (NSE) of modeled flow, showing a mean NSE of 0.54, with 70% of catchments exceeding NSE 0.4. We conclude that SRM effectively predicts high-flow and low-flow events related to flood and drought. Overall, this new approach will significantly improve catchment simulation capacity, enhancing water security and flood forecasting capability not only in the MDB but also worldwide.
Read more28Purus River suspended sediment variability and contributions to the Amazon River from satellite data (2000–2015)
Purus River suspended sediment variability and contributions to the Amazon River from satellite data (2000–2015)
27Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements
Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements
Read moreThe Derivation of Irish Water Quality via Sentinel-2 Imagery
The water quality in Republic of Ireland is regulated under the water framework directive (WFD), which requires all EU countries achieve good ecological and chemical status before 2027. However, the Irish Environmental Protection Agency (EPA) reports in 2021 that just half of the rivers, lakes, estuaries, and coastal waters achieved satisfactory or higher status.Water quality in Ireland is monitored by traditional methods, which cannot provide timely spatiotemporal information. While remote sensing (RS)-based water quality monitoring work have been carried out in many EU countries in accordance with WFD directive, the use of RS for water quality estimation in Ireland has not been fully explored.To explore the feasibility of RS for Irish waters, Sentinel-2 surface reflectance has been used to assess several water quality parameters (chlorophyll-a, transparency, turbidity, suspended solids (SS), total nitrogen, total phosphorus, biological oxygen demand (BOD), dissolved oxygen, and chemical oxygen demand (COD)) from March 2017 to July 2022. These were compared with the Sentinel-2 surface reflectance data resulting in a total of 6509 corresponding data points.Initially, empirical algorithms were used to derive water quality concentrations in rivers, lakes, estuaries, and coastal waters separately. Initial results indicate that the combination of green and blue bands was correlated to coastal waters&#8217; chlorophyll-a (R2 = 0.27). For chlorophyll-a in transitional waters, the combination of red and red edge was highly correlated. However, no single band or combination were suitable for deriving chlorophyll-a in lakes. For SS, red and near infrared band are useful in detecting changes in coastal and transitional waters. Whereas, for lakes and rivers, blue and shortwave infrared band were best to derive SS. In addition to empirical algorithms, multiple machine learning methods have been used to derive water quality parameters from Sentinel-2 reflectance, with the aim of exploring if machine learning approaches can improve estimates compared with the empirical approaches.
Read moreSeasonal dynamics of geomorphic units and denitrification rate in a large lowland tropical river
The present investigation was carried out over a 50 km reach, the Padma River of Bangladesh, downstream of the confluence of the Ganges and Brahmaputra rivers. The study area is highly dynamic, with diverse geomorphic units, with high rates of bank erosion, and sediment abstraction. Sentinel-2 imagery (2019-2020) was processed using NDVI values to classify in-channel geomorphic units (GUs). The identified GUs were categorised as islands (human-dominated and vegetated), bars (side bar and mid bar), primary channel, secondary channels, water depressions and dry channels. Monsoonal water flow was considered an essential factor for reshaping and supplying nutrients to GUs. Based on discharge and the surface area of the identified GUs in different seasons, the dynamics of nutrient-retention-relevant geomorphic units (NRRGUs) were assessed. A field study was carried out to validate the classification of GUs and to measure the potential denitrification rate (PDR) in each type. Results of the analysis showed that changes in the surface area of GUs across seasons influence the rate of PDR significantly. Along the surface area, the number of GUs impacted PDR estimation, mainly regulated by river discharge. This type of systematic investigation of the spatial and temporal distribution of geomorphological and biogeochemical process measuring and monitoring will be useful for planning river restoration and ecosystem management programs.
Read more4Influence of River Discharge and Tides on the Salinity Structure at the Magdalena River Estuary
The salinity structure in the Magdalena River estuary results from a massive river discharge and a micro-tidal regime over a narrow-deep channel. Both environmental forcings are analyzed here to assess their effects on the temporal and spatial variability of the estuarine system. We investigated long-term (seasonal to interannual) and short-term (diurnal) changes in salinity structure and circulation through in situ measurements and a 3D hydrodynamic model ensemble (21 model runs). The measurements cover low (below 10th percentile) and mean river discharge conditions. Results show that the Magdalena River estuary (MRE) behaves as a strongly stratified system during the dry season (February to April) and as a vertically homogeneous one during the wet and transitional seasons when the river plume exhibits a lift-off regime at the mouth. A river discharge threshold has been found for the estuary to lift-off regime transition, corresponding to the 30th percentile (5200 m3/s). At the seasonal scale, the salinity intrusion length in the MRE shows more reactivity to river discharge variability than other reported strongly stratified estuaries; it is reflected in the scaling factor n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n$$\\end{document} in the relationship L∼Q-n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L\\sim {Q}^{-n}$$\\end{document}. A n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n$$\\end{document} value of 3.9 is the best fit for the MRE; nevertheless, it follows the Schijf and Schönfeld theoretical model solution for a prismatic flat configuration. Micro tides modulate the salinity intrusion on the diurnal cycle with a more significant effect on salinity intrusion and water exchange than previously reported, particularly during low discharge conditions. The findings from measurements and model scenarios for the MRE can be broadly applied and stand for an exemplary tropical, micro-tidal, anthropogenic intervened system.
Read moreForecasting Discharge and Water Levels of Rivers and Dams using Earth Observation and AI
The exploitation of rivers and hydropower reservoirs involves daily monitoring of&#160; the water resources, the meteorological conditions, the status of&#160; the river banks, the flood areas, etc. As maximum&#160;river discharge&#160;often results in flooding, it is of importance to provide with timely and reliable forecasts of discharge and water levels. Predicting river discharge and water levels has been a subject of hydrological modelling and a topic of serious research. However, only in recent years scholars and practitioners have turned to consider earth observation data for their studies, mainly to compare evidence of flood mapping. We present an approach of using earth observation data to feed AI architectures &#8211; EO4AI &#8211; and produce forecasts for discharge and water level with significant degrees of accuracy. &#160;Our starting point is that river discharge and water levels depend on a variety of meteorological and environmental factors like precipitations, snow cover, soil moisture, vegetation index and satellite data offer rich variety of datasets, supplying this information. We adopt a pipeline of deep learning architectures consisting of GAN, CNN, LSTM and EA to actually generate forecasts for river discharge and water level by using historic satellite data of the meteorological features listed above, and in-situ measurements for water level and discharge. The satellite data are provided by ADAM&#160; via the NoR service of ESA. ADAM provides data access to satellite datasets from different satellites with semantic relevance for the construction of sediment transport and deposition forecast model as discussed above.&#160; We explain the purpose of the pipeline components. Our forecast models are calibrated for 3, 5, 7, 30 days ahead, and our experiments provide predictions for one year ahead with each of the calibrated models. We discuss experiment results carried out with data from the Danube and Arda rivers, including three dams from cascade Arda and compare them with predictions derived with other methods. We demonstrate the viability of the approach and the reliability of the forecasting results. We further show how the forecasts can be used in hydrodynamic modelling context, for early warning applications and for routine water resources management and monitoring tasks.
Read moreEstimation of Attenuation Coefficient Values Using Remote Sensing and Its Relationship With Shallow Water Depth
In ocean remote sensing, the intensity of light entering the water column decreases exponentially with increasing depth due to scattering and particle absorption in the water column. This process of decreasing light intensity is called attenuation. Attenuation is a limiting factor in detecting objects in the water column and seafloor using remote sensing, which relies on light intensity. The attenuation coefficient (Kd) is an important optical property of seawater as it provides information about water clarity and the level of light attenuation. This study aims to analyze the estimation of the attenuation coefficient values and their variability using in-situ measurements and Sentinel-2 level 2A data in Karang Lebar, Pulau Panggang, and Pulau Air, in the Seribu Islands Regency, North Jakarta. We tested several algorithms to estimate the attenuation coefficient values. The research results show that the in-situ Kd and the estimated model values have a good correlation (r = 0.75-0.86). The distribution of attenuation coefficient values in the shallow waters of the study area ranges from 0.06 to 0.18m-1. The accuracy of estimating shallow water depth at the study sites was best represented by R2 and RMSE values in the range of 0-5m with an attenuation coefficient of 0.06-0.11m-1.
 
 Keywords: Diffuse Attenuation Coefficient, Remote Sensing, Sentinel-2 Imagery, Algorithm.
Read moreA satellite-based approach for estimating runoff and river discharge in the Pan-Arctic region from 2003 to 2022
This study presents daily, long-term (2003−2022) satellite-based estimates of river discharge and gridded runoff at 0.25° × 0.25° spatial resolution across the continental Pan-Arctic region. Satellite observations of precipitation from the Integrated Multi-satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM), soil moisture and snow cover fraction from the European Space Agency's Climate Change Initiative (ESA CCI), and Terrestrial Water Storage Anomalies from satellite gravimetry are integrated into a conceptual hydrological model, STREAM, specifically adapted to simulate the hydrological regime of Pan-Arctic regions. The model is calibrated and validated using data from the 15 largest and best-monitored Arctic basins. It demonstrates high performance in reproducing daily river discharge observations, with a median Kling-Gupta Efficiency (KGE) of 0.83. To extend the model to ungauged regions, a regionalization approach based on similarities in basin climatic attributes is proposed, enabling the transfer of calibrated STREAM model parameters. When validated over 26 independent gauging stations, the regionalized river discharge estimates maintained acceptable accuracy, with a median KGE of 0.51. Using this framework, STREAM is applied across the continental Pan-Arctic region covering an area of 16.5 × 10 6 km 2 . The resulting spatially and temporally consistent runoff dataset, which can be used to complement more detailed process-based model estimates, enables the quantification of freshwater fluxes to the Arctic Ocean, estimated at 4760 ± 619 km 3 yr −1 . The results highlight the potential of satellite observations—particularly GRACE data—in supporting large-scale hydrological modelling and in reconstructing the Arctic freshwater cycle in data-scarce environments. • A satellite-based hydrological model (STREAM) is applied to the Pan-Arctic region. • STREAM performs well in 15 Arctic basins with a median KGE of 0.83. • Regionalization enables STREAM application in ungauged Arctic basins. • Development of a satellite-based daily runoff dataset for the Pan-Arctic region.
Read more5Accuracy Assessment of Satellite Derived Bathymetry Model for Depth Extraction in Sorong Shallow Water Area
Marine research has continuously improved the methods in obtaining the related bathymetric data; not only relying on the conventional methods for i.e. echosounder-based methods, but also by incorporating satellite technology for i.e. passive remote sensing technology, in this case, satellite derived bathymetry (SDB). Regarding the SDB method, as we know, variation of sea bed cover can influence the relation between the spectral reflection of shallow water area and the depth of the sea. In this situation, normalization of the sea bed variation is needed. Previous studies have mentioned that the band ratio can help to normalize the variation of sea bed cover. This research is intended to compare the accuracy of satellite derived bathymetry by using single band and band ratio. Four bands of Sentinel 2A (blue, green, red, and NIR bands) are used along with a single beam echosounder (SBES) measurement data published in 2015 used as training and testing data for the SDB model. Furthermore, the influence of sun glint correction to the results was evaluated and the accuracy of the model was estimated. In total there are four single bands and six combinations of band ratio that are used for this research. The results show that green band outperformed band ratio in term of RMSE value. However, visually, only band ratio of blue/green band that provided a much more representative depth spatial distribution especially for shallow water area below 3 m. In this case, band ratio is effective in normalizing the variation of sea bed cover. Furthermore, the use of sun glint correction in the process is also increase accuracies of the SDB model. The highest accuracy was obtained when using green band after sun glint correction with RMSE value 2.999 m while when using band ratio of the blue band to the green band (blue/green), the accuracy was 3.624 m. In conclusion, SDB model to extend methods in obtaining bathymetry data is promising as more images become available free of charge and in various resolutions.
Read more24Trends in Oyster Populations in the Northeastern Gulf of Mexico: An Assessment of River Discharge and Fishing Effects over Time and Space
Within the Big Bend region of the northeastern Gulf of Mexico, one of the least developed coastlines in the continental USA, intertidal and subtidal populations of eastern oyster Crassostrea virginica (hereafter referred to as “oyster”) are a critical ecosystem and important economic constituent. We assessed trends in intertidal oyster populations, river discharge, and commercial fishing activity in the Suwannee River estuary within the Big Bend region using fisheries-independent data from irregular monitoring efforts and publicly available environmental data. We used generalized linear models to evaluate counts of oysters from line-transect surveys over time and space. We assessed model performance using simulation to understand potential bias and then evaluated whether these counts were related to freshwater inputs from the Suwannee River and commercial oyster fishing effort and landings at different time lags. We found that intertidal oyster counts have declined over time and that most of these declines are found in inshore intertidal oyster bars, which are becoming degraded. We also found a significant relationship between oyster counts and a 1-year lag on mean daily Suwannee River discharge, but including commercial fishery trips or landings did not improve model fit. It is unclear whether declines in intertidal oyster bars are offset by formation of new oyster reefs elsewhere. These results quantify rapid declines in intertidal oyster reefs in a region of coastline with high conservation value that can be used to inform ongoing and proposed restoration projects in the region.
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