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USGS2024

An intercomparison of DOC estimated from fDOM sensors in wildfire affected streams of the western United States

Wildfires in the western United States (US) have been demonstrated to affect water quality, including dissolved organic carbon (DOC), in streams. Elevated post-wildfire DOC concentration poses a potential risk to drinking water treatment systems. In-stream measurements of fluorescent dissolved organic matter (fDOM), a proxy for DOC, have shown potential to detect dynamic changes in DOC. High frequency monitoring of water temperature, turbidity, and fDOM was used in conjunction with discrete sampling during targeted storm events and at fixed intervals to estimate DOC in five western US streams following wildfires in 2020 and 2021 with the objective to characterise and compare responses to wildfire among sites. The elevated turbidity conditions typical after wildfire presented a challenge to fDOM measurements and there was a need to identify appropriate turbidity corrections at burned sites. A combination of established and novel methods corrected fDOM concentrations for turbidity effects up to 800 Formazin nephelometric units (FNU). Pre-wildfire high frequency water quality data in adjacent burned and unburned watersheds allowed for separation of climate effects on DOC at one of the sites. Hydrology, climate and landcover were more important drivers of post-wildfire DOC yield than wildfire characteristics. Seasonal patterns of DOC were unchanged by wildfire in snowmelt-driven watersheds. Large, transient spikes in DOC concentration following frontal and convective storms were observed post-wildfire at all burned sites, but not at the unburned site. These spikes often exceeded operational thresholds for drinking water treatment. This study highlights the ability to develop high frequency DOC estimates in surface waters up to 800 FNU using fDOM sensors and targeted storm sampling and emphasises the value of high frequency pre-wildfire data in adjacent burned and unburned watersheds for separating climate and wildfire effects.

California, Colorado, Montana, Oregon↗

USGS2021

Local variations in broadband sensor installations: Orientations, sensitivities, and noise levels

As seismologists continue to place more stringent demands on data quality, accurately described metadata are becoming increasingly important. In order to better constrain the orientation and sensitivities of seismometers deployed in U.S. Geological Survey networks, the Albuquerque Seismological Laboratory (ASL) has recently begun identifying true north with a fiber optic gyroscope (FOG) and has developed methodologies to constrain mid-band, vertical component sensitivity levels to less than 1% in a controlled environment. However, questions remain regarding the accuracy of this new alignment technique as well as if instrument sensitivities and background noise levels are stable when the seismometers are installed in different environmental settings. In this study, we examine the stability and repeatability of these parameters by reinstalling two high-quality broadband seismometers (Streckeisen STS-2.5 and Nanometrics T-360 Global Seismographic Network (GSN) version) at different locations around the ASL and comparing them to each other and a reference STS-6 seismometer that stayed stationary for the duration of the experiment. We find that even in different environmental conditions, the sensitivities of the two broadband seismometers stayed stable to within 0.1% and that orientations attained using the FOG are generally accurate to within a degree. However, one install was off by 5° due to a mistake made by the installation team. These results indicate that while technology and methodologies are now in place to calibrate and orient a seismometer to within 1°, human error both during the installation and while producing the metadata is often a limiting factor. Finally, we find that background noise levels at short periods (0.1–1 s) become noisier when the sensors are emplaced in unconsolidated materials, whereas the noise levels at long periods (30–100 s) are not sensitive to local geological structure on the vertical components.

Pure and Applied Geophysics↗

USGS2026

Growth and erosion of volcanic islands since 1963 analyzed by multi-sensor satellite data and historical records

Most volcanic activity is taking place in the oceans. Depending on water depth, eruption recurrence times and volumes, a new island can form. The power of erosional forces, the type of erupted material and the efficiency of secondary processes determine the island’s lifetime. Since the famous eruption of Surtsey (Iceland) in 1963, at least another 23 volcanic islands have appeared. Some islands remained intact for years or decades, whereas others disappeared within just weeks or months. In this study, we analyzed satellite data to determine growth and erosion rates of the volcanic edifices related to these 24 islands. We combined multi-sensor (optical, thermal, radar) satellite data time series with information from literature and the Global Volcanism Program database. We developed a comprehensive dataset, including 19 parameters, on the islands’ lifetime, shape, area, volume, eruption style and duration, environmental conditions, development of sedimentary deposits, and the geomorphic evolution of the island over time. Our dataset is available in a database format. This database allows us to test eight hypotheses about factors influencing the islands’ lifetime. Our results show that instead of one single critical factor, a combination of different factors influences the life history of volcanic islands. For instance, we show that larger islands do not necessarily live longer. The mechanical properties of the eruption products affect the island’s structural integrity. Irrespective of the material, a minimum initial area of around 50,000 m 2 seems to be a reasonable threshold to give the island a chance to exist longer.

Bulletin of Volcanology↗

USGS2012

Designs and test results for three new rotational sensors

We discuss the designs and testing of three rotational seismometer prototypes developed at the Institute of Geophysics, Academy of Sciences (Prague, Czech Republic). Two of these designs consist of a liquid-filled toroidal tube with the liquid as the proof mass and providing damping; we tested the piezoelectric and pressure transduction versions of this torus. The third design is a wheel-shaped solid metal inertial sensor with capacitive sensing and magnetic damping. Our results from testing in Prague and at the Albuquerque Seismological Laboratory of the US Geological Survey of transfer function and cross-axis sensitivities are good enough to justify the refinement and subsequent testing of advanced prototypes. These refinements and new testing are well along.

Journal of Seismology↗

USGS2017

New insights into nitrate dynamics in a karst groundwater system gained from in situ high-frequency optical sensor measurements

Understanding nitrate dynamics in groundwater systems as a function of climatic conditions, especially during contrasting patterns of drought and wet cycles, is limited by a lack of temporal and spatial data. Nitrate sensors have the capability for making accurate, high-frequency measurements of nitrate in situ, but have not yet been evaluated for long-term use in groundwater wells. We measured in situ nitrate continuously in two groundwater monitoring wells —one rural and one urban—located in the recharge zone of a productive karst aquifer in central Texas in order to resolve changes that occur over both short-term (hourly to daily) and long-term (monthly to yearly) periods. Nitrate concentrations, measured as nitrate-nitrogen in milligrams per liter (mg/L), during drought conditions showed little or no temporal change as groundwater levels declined. During aquifer recharge, extremely rapid changes in concentration occurred at both wells as documented by hourly data. At both sites, nitrate concentrations were affected by recharging surface water as evidenced by nitrate concentrations in groundwater recharge (0.8–1.3 mg/L) that were similar to previously reported values for regional recharging streams. Groundwater nitrate concentrations responded differently at urban and rural sites during groundwater recharge. Concentrations at the rural well (approximately 1.0 mg/L) increased as a result of higher nitrate concentrations in groundwater recharge relative to ambient nitrate concentrations in groundwater, whereas concentrations at the urban well (approximately 2.7 mg/L) decreased as a result of the dilution of higher ambient nitrate concentrations relative to those in groundwater recharge. Notably, nitrate concentrations decreased to as low as 0.8 mg/L at the urban site during recharge but postrecharge concentrations exceeded 3.0 mg/L. A return to higher nitrate concentrations postrecharge indicates mobilization of a localized source of elevated nitrate within the urbanized area of the aquifer. Changes in specific conductance were observed at both sites during groundwater recharge, and a significant correlation between specific conductance and nitrate (correlation coefficient [R] = 0.455) was evident at the urban site where large (3-fold) changes in nitrate occurred. Nitrate concentrations and specific conductance measured during a depth profile indicated that the water column was generally homogeneous as expected for this karst environment, but changes were observed in the most productive zone of the aquifer that might indicate some heterogeneity within the complex network of flow paths. Resolving the timing and magnitude of changes and characterizing fine-scale vertical differences would not be possible using conventional sampling techniques. The patterns observed in situ provided new insight into the dynamic nature of nitrate in a karst groundwater system.

Texas↗

USGS2015

Using motion-sensor camera technology to infer seasonal activity and thermal niche of the desert tortoise ( Gopherus agassizii )

Understanding the relationships between environmental variables and wildlife activity is an important part of effective management. The desert tortoise ( Gopherus agassizii ), an imperiled species of arid environments in the southwest US, may have increasingly restricted windows for activity due to current warming trends. In summer 2013, we deployed 48 motion sensor cameras at the entrances of tortoise burrows to investigate the effects of temperature, sex, and day of the year on the activity of desert tortoises. Using generalized estimating equations, we found that the relative probability of activity was associated with temperature (linear and quadratic), sex, and day of the year. Sex effects showed that male tortoises are generally more active than female tortoises. Temperature had a quadratic effect, indicating that tortoise activity was heightened at a range of temperatures. In addition, we found significant support for interactions between sex and day of the year, and sex and temperature as predictors of the probability of activity. Using our models, we were able to estimate air temperatures and times (days and hours) that were associated with maximum activity during the study. Because tortoise activity is constrained by environmental conditions such as temperature, it is increasingly vital to conduct studies on how tortoises vary their activity throughout the Sonoran Desert to better understand the effects of a changing climate.

California↗

USGS2015

Inferring spatial and temporal behavioral patterns of free-ranging manatees using saltwater sensors of telemetry tags

Diving or respiratory behavior in aquatic mammals can be used as an indicator of physiological activity and consequently, to infer behavioral patterns. Five Antillean manatees, Trichechus manatus manatus , were captured in Chetumal Bay and tagged with GPS tracking devices. The radios were equipped with a micropower saltwater sensor (SWS), which records the times when the tag assembly was submerged. The information was analyzed to establish individual fine-scale behaviors. For each fix, we established the following variables: distance ( D ), sampling interval ( T ), movement rate ( D / T ), number of dives ( N ), and total diving duration (TDD). We used logic criteria and simple scatterplots to distinguish between behavioral categories: ‘Travelling’ ( D / T ≥ 3 km/h), ‘Surface’ (↓TDD, ↓ N ), ‘Bottom feeding’ (↑TDD, ↑ N ) and ‘Bottom resting’ (↑TDD, ↓ N ). Habitat categories were qualitatively assigned: Lagoon, Channels, Caye shore, City shore, Channel edge, and Open areas. The instrumented individuals displayed a daily rhythm of bottom activities, with surfacing activities more frequent during the night and early in the morning. More investigation into those cycles and other individual fine-scale behaviors related to their proximity to concentrations of human activity would be informative

Chetumal Bay↗

USGS2016

Oil slick morphology derived from AVIRIS measurements of the Deepwater Horizon oil spill: Implications for spatial resolution requirements of remote sensors

Using fine spatial resolution (~ 7.6 m) hyperspectral AVIRIS data collected over the Deepwater Horizon oil spill in the Gulf of Mexico, we statistically estimated slick lengths, widths and length/width ratios to characterize oil slick morphology for different thickness classes. For all AVIRIS-detected oil slicks (N = 52,100 continuous features) binned into four thickness classes (≤ 50 μm but thicker than sheen, 50–200 μm, 200–1000 μm, and > 1000 μm), the median lengths, widths, and length/width ratios of these classes ranged between 22 and 38 m, 7–11 m, and 2.5–3.3, respectively. The AVIRIS data were further aggregated to 30-m (Landsat resolution) and 300-m (MERIS resolution) spatial bins to determine the fractional oil coverage in each bin. Overall, if 50% fractional pixel coverage were to be required to detect oil with thickness greater than sheen for most oil containing pixels, a 30-m resolution sensor would be needed.

Marine Pollution Bulletin↗

USGS2007

Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor

Mapping tools are needed to document the location and extent of Phragmites australis, a tall grass that invades coastal marshes throughout North America, displacing native plant species and degrading wetland habitat. Mapping Phragmites is particularly challenging in the freshwater Great Lakes coastal wetlands due to dynamic lake levels and vegetation diversity. We tested the applicability of Hyperion hyperspectral satellite imagery for mapping Phragmites in wetlands of the west coast of Green Bay in Wisconsin, U.S.A. A reference spectrum created using Hyperion data from several pure Phragmites stands within the image was used with a Spectral Correlation Mapper (SCM) algorithm to create a raster map with values ranging from 0 to 1, where 0 represented the greatest similarity between the reference spectrum and the image spectrum and 1 the least similarity. The final two-class thematic classification predicted monodominant Phragmites covering 3.4% of the study area. Most of this was concentrated in long linear features parallel to the Green Bay shoreline, particularly in areas that had been under water only six years earlier when lake levels were 66??cm higher. An error matrix using spring 2005 field validation points (n = 129) showed good overall accuracy-81.4%. The small size and linear arrangement of Phragmites stands was less than optimal relative to the sensor resolution, and Hyperion's 30??m resolution captured few if any pure pixels. Contemporary Phragmites maps prepared with Hyperion imagery would provide wetland managers with a tool that they currently lack, which could aid attempts to stem the spread of this invasive species. ?? 2006 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2016

Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data

There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43 years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all available clear Landsat 5–8 observations with the new Continuous Change Detection and Classification (CCDC) algorithm was very effective for illuminating vegetation trends. There are a number of potential challenges for assessing gradual changes, including atmospheric impacts, algorithm development and visualization of the changes. One of the biggest challenges for studying gradual change will be the lack of appropriate data for validating results and products.

Remote Sensing of Environment↗

USGS2020

Classification of oil spill by thicknesses using multiple remote sensors

Satellite Synthetic Aperture Radar (SAR) is an operational tool for monitoring and assessment of oil spills. Satellite SAR has primarily been used to detect the presence/absence of oil, yet its ability to discriminate oil emulsions within a detected oil slick has not been fully exploited. Additionally, one of the challenges in the past has been the ability to deliver strategic information derived from satellite remote sensing in a timely fashion to responders in the field. This study presents methods for the rapid classification of oil types and estimated thicknesses, from which information about thick oil and oil emulsions (i.e., “actionable” oil) can be delivered in an operational timeframe to responders in the field. Experiments carried out at the OHMSETT test facility in New Jersey demonstrate that under specific viewing conditions, a single polarization satellite SAR image can record a signal variance between thick stable emulsions and non-emulsified oil. During a series of field campaigns in the Gulf of Mexico with in situ measurements of oil thickness, multiple satellite data were obtained including fully polarimetric C-band SAR imagery from RADARSAT-2 and multispectral imagery from ASTER and WorldView-2. One campaign included the airborne polarimetric UAVSAR L-band sensor. An oil/emulsion thickness classification product was generated based on RADARSAT-2 polarimetric imagery using entropy and the damping ratio derivations. Herein, we present the classification methods to generate oil thickness products from SAR, validated by sea-truth observations, the multispectral imagery, and the UAVSAR data. We tested the ability to deliver these products with minimum latency to responding vessels via NOAA. During field operations in the Gulf of Mexico, a satellite SAR-based product of oil delineation by relative thickness was delivered to a responding vessel 42 min after the RADARSAT-2 data acquisition. This proof-of-concept test using satellite SAR and multispectral imagery to detect emulsions and deliver a derived information product to a vessel in near-real-time points directly to methods for satellite-based assets to be used in the near future for oil spill tactical response operations.

Louisiana↗

USGS2024

Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems

The deposits from volcanic eruptions represent the record of activity at a volcano. Identification, classification, and interpretation of these deposits are crucial to the understanding of volcanic processes and assessing hazards. However, deposits often cover large areas and can be difficult or dangerous to access, making field mapping dangerous and time-consuming. Remote sensing techniques are often used to map and identify the deposits of volcanic eruptions, though these techniques present their own trade-offs in terms of image resolution, wavelength, and observation frequency. Here, we present a new approach for mapping and classifying volcanic deposits using a multi-sensor unoccupied aerial system (UAS), and demonstrate its application on lava and tephra deposits associated with the 2018 eruption of Sierra Negra volcano (Galápagos Archipelago, Ecuador). We surveyed the study area and collected visible and thermal infrared (TIR) images. We used structure-from-motion photogrammetry to create a digital elevation model (DEM) from the visual images and calculated the solar heating rate of the surface from temperature maps based on the TIR images. We find that the solar heating rate is highest for tephra deposits and lowest for ʻaʻā lava, with pāhoehoe lava having intermediate values. This is consistent with the solar heating rate correlating to the density and particle size of the surface. The solar heating rate for the lava flow also decreases with increasing distance from the vent, consistent with an increase in density as the lava degasses. We combined the surface roughness (calculated from the DEM) and the solar heating rate of the surface to remotely classify tephra deposits and different lava morphologies. We applied both supervised and unsupervised machine learning algorithms and demonstrate that supervised methods can replicate the manual classification while the unsupervised method can identify major surface units with no ground truth information. These methods allow for remote mapping and classification at high spatial resolution (< 1 meter) of a variety of volcanic deposits, with potential for application to deposits from other processes (e.g., fluvial, glacial) and deposits on other planetary bodies.

Galápagos Archipelago, Sierra Negra volcano↗

USGS2002

Impact of sensor's point spread function on land cover characterization: Assessment and deconvolution

Measured and modeled point spread functions (PSF) of sensor systems indicate that a significant portion of the recorded signal of each pixel of a satellite image originates from outside the area represented by that pixel. This hinders the ability to derive surface information from satellite images on a per-pixel basis. In this study, the impact of the PSF of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m bands was assessed using four images representing different landscapes. Experimental results showed that though differences between pixels derived with and without PSF effects were small on the average, the PSF generally brightened dark objects and darkened bright objects. This impact of the PSF lowered the performance of a support vector machine (SVM) classifier by 5.4% in overall accuracy and increased the overall root mean square error (RMSE) by 2.4% in estimating subpixel percent land cover. An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%. This method differs from traditional PSF inversion deconvolution methods in that the PSF was adjusted with lower weighting factors for signals originating from neighboring pixels than those specified by the PSF model. By using this deconvolution method, the lost classification accuracy due to residual impact of PSF effects was reduced to only 1.66% in overall accuracy. The increase in the RMSE of estimated subpixel land cover proportions due to the residual impact of PSF effects was reduced to 0.64%. Spatial aggregation also effectively reduced the errors in estimated land cover proportion images. About 50% of the estimation errors were removed after applying the deconvolution method and aggregating derived proportion images to twice their dimensional pixel size. ?? 2002 Elsevier Science Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2024

GNSS reflectometry from low-cost sensors for continuous in situ contemporaneous glacier mass balance and flux divergence

Recent advances in remote sensing have produced global glacier surface elevation change data. Parsing these elevation change signals into contributions from the climate (i.e. climatic mass balance) and glacier dynamics (i.e. flux divergence) is critical to enhance our process-based understanding of glacier change. In this study, we evaluate three approaches for direct, continuous measurements of the climatic mass balance, flux divergence, and elevation change at a site on Gulkana Glacier in Alaska using low-cost GNSS sensors, GNSS interferometric reflectometry (GNSS-IR), banded ablation stakes with time-lapse cameras, and combinations thereof. Cumulative climatic mass balance over the season was 4.85 m and the three approaches were within 0.08 m through early July before the snowpack melted, and within 0.28 m through mid-August. The flux divergence increased from 0.52 ± 0.03 cm d -1 before June 3 to roughly 0.73 cm d -1 after June 27. We demonstrate a single GNSS system fixed atop an ablation stake can measure contemporaneous climatic mass balance, flux divergence, and elevation change based on the antenna’s position and GNSS-IR techniques. The ability of these systems to measure glacier mass balance and flux divergence offers unique opportunities for year-round observations on mountain glaciers in the future.

Alaska↗

USGS2014

Mississippi River nitrate loads from high frequency sensor measurements and regression-based load estimation

Accurately quantifying nitrate (NO 3 – ) loading from the Mississippi River is important for predicting summer hypoxia in the Gulf of Mexico and targeting nutrient reduction within the basin. Loads have historically been modeled with regression-based techniques, but recent advances with high frequency NO 3 – sensors allowed us to evaluate model performance relative to measured loads in the lower Mississippi River. Patterns in NO 3 – concentrations and loads were observed at daily to annual time steps, with considerable variability in concentration-discharge relationships over the two year study. Differences were particularly accentuated during the 2012 drought and 2013 flood, which resulted in anomalously high NO 3 – concentrations consistent with a large flush of stored NO 3 – from soil. The comparison between measured loads and modeled loads (LOADEST, Composite Method, WRTDS) showed underestimates of only 3.5% across the entire study period, but much larger differences at shorter time steps. Absolute differences in loads were typically greatest in the spring and early summer critical to Gulf hypoxia formation, with the largest differences (underestimates) for all models during the flood period of 2013. In additional to improving the accuracy and precision of monthly loads, high frequency NO 3 – measurements offer additional benefits not available with regression-based or other load estimation techniques.

Louisiana↗

USGS2007

Identifying spatial variability of groundwater discharge in a wetland stream using a distributed temperature sensor

Discrete zones of groundwater discharge in a stream within a peat‐dominated wetland were identified on the basis of variations in streambed temperature using a distributed temperature sensor (DTS). The DTS gives measurements of the spatial (±1 m) and temporal (15 min) variation of streambed temperature over a much larger reach of stream (>800 m) than previous methods. Isolated temperature anomalies observed along the stream correspond to focused groundwater discharge zones likely caused by soil pipes within the peat. The DTS also recorded variations in the number of temperature anomalies, where higher numbers correlated well with a gaining reach identified by stream gauging. Focused zones of groundwater discharge showed essentially no change in position over successive measurement periods. Results suggest DTS measurements will complement other techniques (e.g., seepage meters and stream gauging) and help further improve our understanding of groundwater–surface water dynamics in wetland streams.

Water Resources Research↗

USGS2018

Seismic sensors record a hurricane’s roar

The instruments installed at Global Seismographic Network (GSN) stations were designed to record Earth’s vibrations, but they sometimes pick up sound waves from unexpected sources. For example, newly installed infrasound sensors at a station on Puerto Rico recorded the passage of Hurricane Maria on 20 September 2017.

Eos, Earth and Space Science News↗

USGS2019

Remote sensing of river bathymetry: Evaluating a range of sensors, platforms, and algorithms on the upper Sacramento River, California, USA

Remote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements from a 1.6 km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths from a range of platforms, sensors, and depth retrieval methods. Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR and applied a generalized version of Optimal Band Ratio Analysis (OBRA) and the K nearest neighbors regression (KNN) machine learning algorithm. Linear, quadratic, exponential, power, and lowess OBRA models enabled more flexible curve-fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. KNN increased observed vs. predicted R2 values, particularly for the satellite image; we also found that pre-processing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments.

California↗