Optimal Band Ratio Analysis Research Articles

Integrating Depth Measurements From Gaging Stations With Image Archives for Spectrally Based Remote Sensing of River Bathymetry

AbstractRemote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image‐derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R2 (p < 0.026). Currently, BaMGRID is best‐suited for site‐by‐site analysis to support practical applications at the reach scale; continuous, basin‐wide mapping of river bathymetry will require additional research.

Comparison between Hyperspectral and Multispectral Retrievals of Suspended Sediment Concentration in Rivers

Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation in rivers has not been fully validated. This study thus compares the performance of hyperspectral RS with that of multispectral RS by conducting field-scale experiments in shallow rivers. In the field experiments, we measured radiance from a water body mixed with suspended sediments using a drone-mounted hyperspectral sensor, with the sediment and riverbed types considered as controlling factors. We retrieved the SSC from UAV imagery using an optimal band ratio analysis, which successfully estimated SSC distributions in the sand bed conditions with both multispectral and hyperspectral data. In the vegetated bed conditions, meanwhile, the prediction accuracy decreased significantly due to the temporally varying bottom reflectance associated with the random movement of vegetation caused by near-bed turbulence. This is because temporally inhomogeneous bottom reflectance distorts the relationship between the SSC and total reflectance. Nevertheless, the hyperspectral imaging exhibited better prediction accuracy than the multispectral imaging, effectively extracting optimal spectral bands sensitive to back-scattered reflectance from sediments while constraining the bottom reflectance caused by the vegetation-covered bed.

Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images

Introduction: Information on spatial patterns of water depth in river channels is valuable for numerous applications, but such data can be difficult to obtain via traditional field methods. Ongoing developments in remote sensing technology have enabled various image-based approaches for mapping river bathymetry; this study evaluated the potential to retrieve depth from multispectral images acquired by an uncrewed aircraft system (UAS).Methods: More specifically, we produced depth maps for a 4 km reach of a clear-flowing, relatively shallow river using an established spectrally based algorithm, Optimal Band Ratio Analysis. To assess accuracy, we compared image-derived estimates to direct measurements of water depth. The field data were collected by wading and from a boat equipped with an echo sounder and used to survey cross sections and a longitudinal profile. We partitioned our study area along the Sacramento River, California, USA, into three distinct sub-reaches and acquired a separate image for each one. In addition to the typical, self-contained, per-image depth retrieval workflow, we also explored the possibility of exporting a relationship between depth and reflectance calibrated using data from one site to the other two sub-reaches. Moreover, we evaluated whether sampling configurations progressively more sparse than our full field survey could still provide sufficient calibration data for developing robust depth retrieval models.Results: Our results indicate that under favorable environmental conditions like those observed on the Sacramento River during low flow, accurate, precise depth maps can be derived from images acquired by UAS, not only within a sub-reach but also across multiple, adjacent sub-reaches of the same river.Discussion: Moreover, our findings imply that the level of effort invested in obtaining field data for calibration could be significantly reduced. In aggregate, this investigation suggests that UAS-based remote sensing could facilitate highly efficient, cost-effective, operational mapping of river bathymetry at the reach scale in clear-flowing streams.

Morphological Dune Mapping in Shallow Alluvial Stream Using UAV-based Hyperspectral Images

Characterizing morphological features in shallow streams such as dunes and ripples is vital to studies on fluvial geomorphology and in-stream habitat assessments of stream ecology. The paper aimed to examine the feasibility of a conventional hyperspectral method called linear optimal band ratio analysis for capturing the detailed morphologies in shallow small streams, allowing the identification of ripples and dunes. The present study involved a dedicated field experiment at the Gam stream, which is a tributary of the Nakdong River, South Korea. An unmanned aerial vehicle based hyperspectral image was obtained with a spatial resolution of <10 cm and developed an optimal depth-band ratio rating by densely scattered in situ bathymetry measurements with a portable Real Time Kinematic Global Positioning System. The derived hyperspectral bathymetric map with a 7 cm spatial resolution successfully captured the detailed bed morphology, where dunes with sizes of 1.5 m were clearly identifiable. The correlation with depth measured by RTK-GPS was found to be 0.956, with Root Mean Square Error of 0.033 meter. The research confirmed that the conventional linear OBRA used for low-altitude UAV-based hyperspectral images can capture morphological features in shallow streams with a high spatial resolution.

Riverine Bathymetry Survey Using Unmanned Aerial Vehicle-Based Multispectral Image

River basin surveys are conducted with the aim of providing essential foundational information for the formulation of water management policies, as mandated by relevant laws and regulations. These surveys cover key investigation areas necessary for river basin management, including basic conditions, water conveyance, dimensions, environmental ecology, and more. Among the survey methods, the utilization of remote sensing data, such as drone monitoring imagery and satellite imagery, is employed for various purposes such as the safety management of hydraulic structures like dams and embankments, water quality monitoring, river terrain surveys, and assessments of changes in riverbeds. Recently, research in river basin studies has been conducted using hyperspectral imagery, which includes hundreds of spectral bands, in addition to standard RGB imagery. Hyperspectral imagery offers the advantage of high spectral resolution, making it suitable for multi-parameter assessments. However, it comes with the drawback of large initial data volumes and complex preprocessing requirements due to the abundance of spectral information. On the other hand, multispectral imagery, which collects spectral information from fewer than ten bands, is widely used, especially in agriculture and forestry. It allows for immediate monitoring of parameters like the normalized difference vegetation index (NDVI) using just two bands and facilitates the analysis of crop growth status and more. Research on bathymetric estimation using hyperspectral imagery has traditionally relied on the Optimal Band Ratio Analysis (OBRA), which utilizes band ratios highly correlated with depth to construct bathymetric maps. In this study, we applied the existing hyperspectral bathymetric estimation technique to multispectral imagery to assess the feasibility of bathymetric estimation using reduced spectral bands. We captured multispectral imagery and constructed bathymetric maps to evaluate the applicability of multispectral imagery in river basin applications. Furthermore, to overcome the limitations of traditional OBRA, we employed Gaussian mixture models for image clustering to improve the accuracy of bathymetric estimation.

Estimation of shallow stream bathymetry under varying suspended sediment concentrations and compositions using hyperspectral imagery

Hyperspectral remote sensing has the proven ability to provide bathymetric estimates over relatively clear water. High turbidity caused by suspended sediments scatters light, decreasing light penetration to depth and is thus regarded as the most confounding factor in remote sensing-based bathymetry. Therefore, the effect of varying sediment-laden flow conditions on remote sensing-based bathymetry measurements was investigated in this study to assess the applicability of hyperspectral imagery and a robust method was proposed to account for such conditions. To acquire spectral data under various suspended sediment and bottom conditions, experiments involving the injection of sand and yellow loess were performed in outdoor field-scale channels with three bottom conditions (coarse sand, vegetation, and fine sand) under shallow stream conditions (< 1 m). Using the acquired data, the random forest with recursive feature elimination (RF-RFE) method was used to select and learn the optimal spectral band combination for each bottom type. The results were then compared with those of the optimal band ratio analysis (OBRA) method, which has been most widely used for bathymetry measurements via remote sensing imagery. The reflectance spectra differed depending on the bottom type although they were similar regardless of the bottom type when high reflectance was distributed at high concentrations. Given such optically complex data, OBRA exhibited very low accuracy because it was substantially affected by suspended sediment. However, RF-RFE showed an accuracy of R2 ≥ 0.95 and mapped a stable bathymetry distribution under sediment-laden flow conditions by utilizing 6–12 spectral bands as the optimal combination, depending on the bottom type. This result demonstrated that although the contribution of bottom reflectance was weak when the suspended sediment concentration was high, hyperspectral remote sensing is still applicable if spectral bands of various wavelengths can be incorporated to account for nonlinearity. While the effectiveness of this method could be limited to the specific conditions of suspended sediment and bottom properties, it is expected to be more robust to various confounding factors after being trained under diverse spectral conditions.

Bathymetry retrieval from CubeSat image sequences with short time lags

The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.

Remote Sensing of Visible Dye Concentrations During a Tracer Experiment on a Large, Turbid River

AbstractUnderstanding dispersion in rivers is critical for numerous applications, such as characterizing larval drift for endangered fish species and responding to spills of hazardous materials. Injecting a visible dye into the river can yield insight on dispersion processes, but conventional field instrumentation yields limited data on variations in dye concentration over time at a few, fixed points. Remote sensing can provide more detailed, spatially distributed information on the dye's motion, but this approach has only been tested in clear‐flowing streams. The purpose of this study was to assess the potential of remote sensing to facilitate tracer studies in more turbid rivers. To pursue this objective, we injected Rhodamine WT dye into the Missouri River and collected field spectra from a boat, videos from a small unoccupied aircraft system (sUAS), and orthophotos from an airplane. Applying an optimal band ratio analysis (OBRA) algorithm to the field spectra revealed strong correlations (R2 = 0.936) between a spectrally based quantity and in situ concentration measurements. OBRA also performed well for broadband RGB (red, green, blue) images extracted from the sUAS‐based videos; the resulting concentration maps were used to produce animations that captured movement of the dye pulse. Spectral mixture analysis of repeat orthophoto coverage yielded relative concentration estimates that provided a synoptic perspective on dispersion of the dye throughout the entire 13.8 km reach over the full 2.5‐hr duration of the experiment. The results of this study demonstrate the potential to remotely sense tracer dye concentrations in large, highly turbid rivers.

River Bathymetry Retrieval From Landsat-9 Images Based on Neural Networks and Comparison to SuperDove and Sentinel-2

The Landsat mission has kept an eye on our planet, including water bodies, for 50 years. With the launch of Landsat-9 and its onboard Operational Land Imager 2 (OLI-2) in September 2021, more subtle variations in brightness (14-bit dynamic range) can be captured than previous sensors in the Landsat series (e.g., 12-bit Landsat-8). The enhanced radiometric resolution of OLI-2 appeals to the aquatic remote sensing community because the instrument might be capable of resolving smaller differences in water-leaving radiance. This study evaluates the potential to map river bathymetry from Landsat-9 imagery. We employ a neural network (NN)-based regression model for bathymetry retrieval and compare the results with optimal band ratio analysis (OBRA). The effect of Landsat-9 pan-sharpening on depth retrieval is also examined. In addition, we perform an intersensor comparison with Sentinel-2 and newly available 8-band SuperDoves from the PlanetScope constellation. Depth retrieval results from the Colorado and Potomac Rivers imply that Landsat-9 provided more accurate bathymetry across a range of depths up to 20 m, particularly when pan-sharpened. Downsampling the SuperDove data improved bathymetry retrieval due to enhanced signal-to-noise ratio, most notably in deep waters (maximum detectable depth increased from &#x223C;15 to &#x223C;20 m). Similarly, the enhanced spectral resolution of 8-band SuperDoves improved depth retrieval relative to 4-band Doves. The NN-based model outperformed OBRA by incorporating more spectral information.

On improved nearshore bathymetry estimates from satellites using ensemble and machine learning approaches

In this article, two popular linear empirical methods viz., log-ratio model (LRM) and log-linear model (LLM) are used to derive water depths in shallow nearshore waters from Sentinel-2 multi-spectral images. Based on these empirical models, a multi-scene ensemble and a non-linear Support Vector Regression (SVR) machine learning approaches are applied to improve the accuracies from these traditional methods. In this analysis, firstly the best scene is selected from a set of six Sentinel-2 A&B images using noise-equivalent reflectance NEΔRrs (sr−1), optimal bands for LRM and LLM are selected using Optimal band ratio analysis (OBRA) and by computing Pearson Correlation Coefficient (R) between each band respectively. A total of 80% depth data points obtained from JetSki based echo-sounding measurements are used for training and the remaining 20% are used for testing each approach. The overall errors during the test phase for the range of depth 0–12 m are 0.79 m (0.67 m), 0.94 m (0.66 m) and 0.57 m (0.39 m) using traditional empirical LRM (LLM) methods from the best image, empirical-based ensemble and SVR approaches respectively. Irrespective of the approach, the LLM produced smoother and relatively accurate bathymetry as compared to the LRM. The LLM based SVR ML approach provides the best performance over the entire depth range as compared to all the methods considered in this study. Therefore, this method can be used for efficiently estimating nearshore depths and produce updated high-resolution bathymetry maps for many coastal applications.

The optical river bathymetry toolkit

AbstractSpatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.

SMART-SDB: Sample-specific multiple band ratio technique for satellite-derived bathymetry

Spectrally-based retrieval of bathymetry is challenging in inland/coastal waters due to variations in factors other than water depth across a water body, including bottom type, optically significant constituents, water surface roughness, and others. Optimal band ratio analysis (OBRA) is the most widely used method to deal with these confounding effects to retrieve water depth. OBRA identifies the pair of bands that provides the highest accuracy among all possible pairs when using a log-transformed band ratio model. However, this approach fits a single ratio model to all training samples without fully accounting for the heterogeneity of the aforementioned complicating factors. To deal with this problem, we introduce a novel method called Sample-specific Multiple bAnd Ratio Technique for Satellite-Derived Bathymetry (SMART-SDB). The proposed SMART-SDB technique partitions the feature space of the spectral data and creates for every subspace a different band ratio model that performs better than the other models within that subspace. Bathymetry is then predicted based on the closest model/s in the feature space by performing a sample-specific k-nearest neighbor (K−NN) analysis. The depth estimates provided by the neighboring models are averaged with weights proportional to their inverse distance in the feature space. The proposed method can be used in both inland and coastal waters. Here, we thoroughly examine its effectiveness in the challenging and heterogenous environments of fluvial systems using a wide range of spectral data including radiative transfer simulations, an airborne hyperspectral image of the Snake River (USA), and a WorldView-3 satellite image of the Sarca River (Italy). The results of this study indicate a significant improvement in depth retrieval for estimations based on the SMART-SDB method relative to standard OBRA, either when a single model or multiple models are employed.

The Contribution of Multispectral Satellite Image to Shallow Water Bathymetry Mapping on the Coast of Misano Adriatico, Italy

The results of absolute satellite-derived bathymetry (SDB) are presented in the current study. A comparative analysis was conducted on empirical methods in order to explore the potential of SDB in shallow water on the coast of Misano, Italy. Operations were carried out by relying on limited in situ water depth data to extract and calibrate bathymetry from a QuickBird satellite image acquired on a highly dynamic coastal environment. The image was processed using the log-band ratio and optimal band ratio analysis (OBRA) methods. Preprocessing steps included the conversion of the raw satellite image into top of atmosphere reflectance, spatial filtering, land and water classification, the determination of the optimal OBRA spectral band pairs, and the estimation of relative SDB. Furthermore, calibration and vertical referencing were performed via in situ bathymetry acquired in November 2007. The relative bathymetry obtained from different band ratios were vertically referenced to the local datum using in situ water depth in order to obtain absolute SDB. The coefficient of determination (R2) and vertical root mean square error (RMSE) were computed for each method. A strong correlation with in situ field bathymetry was observed for both methods, with R2 = 0.8682 and RMSE = 0.518 m for the log-band ratio method and R2 = 0.8927–0.9108 and RMSE = 0.35 m for the OBRA method. This indicated a high degree of confidence of the SDB results obtained for the study area, with a high performance of the OBRA method for SDB mapping in turbid water.

An Experimental Evaluation of the Feasibility of Inferring Concentrations of a Visible Tracer Dye from Remotely Sensed Data in Turbid Rivers

The movement of contaminants and biota within river channels is influenced by the flow field via various processes of dispersion. Understanding and modeling of these processes thus can facilitate applications ranging from the prediction of travel times for spills of toxic materials to the simulation of larval drift for endangered species of fish. A common means of examining dispersion in rivers involves conducting tracer experiments with a visible tracer dye. Whereas conventional in situ instruments can only measure variations in dye concentration over time at specific, fixed locations, remote sensing could provide more detailed, spatially-distributed information for characterizing dispersion patterns and validating two-dimensional numerical models. Although previous studies have demonstrated the potential to infer dye concentrations from remotely sensed data in clear-flowing streams, whether this approach can be applied to more turbid rivers remains an open question. To evaluate the feasibility of mapping spatial patterns of dispersion in streams with greater turbidity, we conducted an experiment that involved manipulating dye concentration and turbidity within a pair of tanks while acquiring field spectra and hyperspectral and RGB (red, green, blue) images from a small Unoccupied Aircraft System (sUAS). Applying an optimal band ratio analysis (OBRA) algorithm to these data sets indicated strong relationships between spatially averaged reflectance (i.e., water color) and Rhodamine WT dye concentration across four different turbidity levels from 40–60 NTU. Moreover, we obtained high correlations between spectrally based quantities (i.e., band ratios) and dye concentration for the original, essentially continuous field spectra; field spectra resampled to the bands of a five-band imaging system and an RGB camera; and both hyperspectral and RGB images acquired from an sUAS during the experiment. The results of this study thus confirmed the potential to map dispersion patterns of tracer dye via remote sensing and suggested that this empirical approach can be extended to more turbid rivers than those examined previously.

Remote sensing of tracer dye concentrations to support dispersion studies in river channels

We evaluated the potential of remote sensing to enhance tracer experiments by providing spatially distributed information on visible dye concentration. During tests in an experimental facility and a large natural channel, we measured Rhodamine WT concentration and spectral reflectance. As an initial proof-of-concept at the River Experiment Center in Korea, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River, field spectra were collected from a boat and hyperspectral images and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. Both the flume and field tests yielded very strong (R2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to hyperspectral images and that dye concentrations could be estimated nearly as reliably from three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.

Defining the Limits of Spectrally Based Bathymetric Mapping on a Large River

Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images from a deep, clear-flowing channel to develop techniques for inferring the maximum detectable depth, d m a x , directly from an image and identifying optically deep areas that exceed d m a x . Optimal Band Ratio Analysis (OBRA) of progressively truncated subsets of the calibration data provided an estimate of d m a x by indicating when depth retrieval performance began to deteriorate due to the presence of depths greater than the sensor could detect. We then partitioned the calibration data into shallow and optically deep ( d &gt; d m a x ) classes and fit a logistic regression model to estimate the probability of optically deep water, P r ( O D ) . Applying a P r ( O D ) threshold value allowed us to delineate optically deep areas and thus only attempt depth retrieval in relatively shallow locations. For the Kootenai River, d m a x reached as high as 9.5 m at one site, with accurate depth retrieval ( R 2 = 0.94 ) in areas with d &lt; d m a x . As a first step toward scaling up from short reaches to long river segments, we evaluated the portability of depth-reflectance relations calibrated at one site to other sites along the river. This analysis highlighted the importance of calibration data spanning a broad range of depths. Due to the inherent limitations of passive optical depth retrieval in large rivers, a hybrid field- and remote sensing-based approach would be required to obtain complete bathymetric coverage.

Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA

AbstractRemote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements with a 1.6‐km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths with 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 and the K nearest neighbors regression machine learning algorithm. Linear, quadratic, exponential, power, and lowess Optimal Band Ratio Analysis models enabled flexible curve‐fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. K nearest neighbors regression increased observed versus predicted (OP) R2 values, particularly for the satellite image; we also found that preprocessing 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.

Multiple Optimal Depth Predictors Analysis (MODPA) for river bathymetry: Findings from spectroradiometry, simulations, and satellite imagery

Remote mapping of bathymetry can play a key role in gaining spatial and temporal insight into fluvial processes, ranging from hydraulics and morphodynamics to habitat conditions. This research introduces Multiple Optimal Depth Predictors Analysis (MODPA), which combines previously developed depth predictors along with additional predictors derived from the intensity component of the HSI color space transformation. MODPA empirically selects a set of optimal predictors among all candidates utilizing partial least squares (PLS), stepwise, or principal component (PC) regression models. The primary focus of this study was on shallow (<1 m deep) and clearly flowing streams where substrate variability could have a pronounced effect on depth retrieval. Spectroscopic experiments were performed under controlled conditions in a hydraulic laboratory to examine the robustness of bathymetry models with respect to changes in bottom type. Further, simulations from radiative transfer modeling were used to extend the analysis by isolating the effect of inherent optical properties (IOPs) and by investigating the performance of bathymetry models in optically complex and deeper streams. The bathymetry of the Sarca River, a shallow river in the Italian Alps, was mapped using a WorldView-2 (WV-2) image, for which we evaluated the atmospheric compensation (AComp) product. Results indicated the greater 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. The HSI intensity component enhanced the accuracy of depth retrieval, particularly in optically-complex waters and also for low spectral resolution imagery (e.g., GeoEye). Further, the enhanced spectral resolution of WV-2 imagery improved bathymetry retrieval compared to 4-band GeoEye data.

Sampling Strategies to Improve Passive Optical Remote Sensing of River Bathymetry

Passive optical remote sensing of river bathymetry involves establishing a relation between depth and reflectance that can be applied throughout an image to produce a depth map. Building upon the Optimal Band Ratio Analysis (OBRA) framework, we introduce sampling strategies for constructing calibration data sets that lead to strong relationships between an image-derived quantity and depth across a range of depths. Progressively excluding observations that exceed a series of cutoff depths from the calibration process improved the accuracy of depth estimates and allowed the maximum detectable depth (dmax) to be inferred directly from an image. Depth retrieval in two distinct rivers also was enhanced by a stratified version of OBRA that partitions field measurements into a series of depth bins to avoid biases associated with under-representation of shallow areas in typical field data sets. In the shallower, clearer of the two rivers, including the deepest field observations in the calibration data set did not compromise depth retrieval accuracy, suggesting that dmax was not exceeded and the reach could be mapped without gaps. Conversely, in the deeper and more turbid stream, progressive truncation of input depths yielded a plausible estimate of dmax consistent with theoretical calculations based on field measurements of light attenuation by the water column. This result implied that the entire channel, including pools, could not be mapped remotely. However, truncation improved the accuracy of depth estimates in areas shallower than dmax, which comprise the majority of the channel and are of primary interest for many habitat-oriented applications.

Spectrally based bathymetric mapping of a dynamic, sand‐bedded channel: Niobrara River, Nebraska, USA

AbstractMethods for spectrally based mapping of river bathymetry have been developed and tested in clear‐flowing, gravel‐bed channels, with limited application to turbid, sand‐bed rivers. This study used hyperspectral images and field surveys from the dynamic, sandy Niobrara River to evaluate three depth retrieval methods. The first regression‐based approach, optimal band ratio analysis (OBRA), paired in situ depth measurements with image pixel values to estimate depth. The second approach used ground‐based field spectra to calibrate an OBRA relationship. The third technique, image‐to‐depth quantile transformation (IDQT), estimated depth by linking the cumulative distribution function (CDF) of depth to the CDF of an image‐derived variable. OBRA yielded the lowest depth retrieval mean error (0.005 m) and highest observed versus predicted R2 (0.817). Although misalignment between field and image data did not compromise the performance of OBRA in this study, poor georeferencing could limit regression‐based approaches such as OBRA in dynamic, sand‐bedded rivers. Field spectroscopy‐based depth maps exhibited a mean error with a slight shallow bias (0.068 m) but provided reliable estimates for most of the study reach. IDQT had a strong deep bias but provided informative relative depth maps. Overprediction of depth by IDQT highlights the need for an unbiased sampling strategy to define the depth CDF. Although each of the techniques we tested demonstrated potential to provide accurate depth estimates in sand‐bed rivers, each method also was subject to certain constraints and limitations.