Field measurements of water depth were acquired from a reach of the Colorado River near Lees Ferry, Arizona, March16-18, 2021, to support research on remote sensing of water depth from satellite images. The depth measurements included in this data release were obtained along a series of cross-sections using a SonTek RiverSurveyor M9 acoustic Doppler current profiler (ADCP) deployed from a boat. The spatial location of each measurement was obtained using a differential GPS included as part of the RiverSurveyor M9 ADCP instrument package. The map projection and datum for these data are UTM Zone 12S and WGS84, respectively. The USGS Qrev software program was used to ingest and process the raw ADCP data. The Qrev data file was then imported into MATLAB, which was used to create a comma-delimited (*.csv) text file with three columns: Easting_m, Northing_m, and Depth_m; the units of the spatial coordinates and the depths are meters. This ground-based depth data set was used to calibrate (i.e., train) models for inferring water depths from passive optical image data and to assess the accuracy of image-derived depth estimates.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the Colorado River near Lees Ferry, AZ, acquired in March and April of 2021. Field measurements of water depth available through another data release (Legleiter, C.J., Debenedetto, G.P., and Forbes, B.T., 2022, Field measurements of water depth from the Colorado River near Lees Ferry, AZ, March 16-18, 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9HZL7BZ) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: Colorado_mean-spec.tif, Colorado_mean-depth.tif, Colorado_NN-depth.tif, and Colorado-single-image.tif. In addition, to assess the robustness of the Mean-spec and NN-depth methods to the introduction of a large pulse of sediment by a flood event that occurred partway through the image time series, depth maps from before and after the flood are provided in the files Colorado_Mean-spec_after_flood.tif,

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Field-based multibeam sonar surveys were collected along the Colorado River, near Lees Ferry, Arizona from a motorized cataraft. These data were used to assess the accuracy of river bathymetry inferred from the ASTRALiTe bathymetric lidar, acquired contemporaneously from the same survey vessel. These data sets were collected to support research focused on developing innovative methods for non-contact measurement of river discharge based on various forms of remotely sensed data. The sonar survey data were exported to a comma-separated text file and the resulting *.csv file contain for each point the spatial coordinates, and depth (expressed as a negative number), all in meters

Field measurements of water depth were acquired from a reach of the American River at Sailor Bar, near Fair Oaks, California, October 19-21, 2020, to support research on remote sensing of water depth from satellite images. The depth measurements included in this data release were obtained via two different methods: 1) By wading the shallow channel margins with RTK GPS receivers and measuring water surface elevations along the water's edge and bed elevations within the channel; depths were calculated by subtracting bed elevations from the nearest water surface elevation. 2) For the deeper areas representing most of the channel, depths were recorded along a series of cross-sections by a SonTek RiverSurveyor S5 acoustic Doppler current profiler (ADCP) and a SonarMite single beam echo sounder deployed from a boat. The spatial location of each measurement was obtained via Trimble R10 RTK GPS receivers. The map projection and datum for these data are UTM Zone 10 N and WGS84, respectively. The ADCP- and echo sounder-based depth measurements were cross-calibrated to one another using collocated observations from the two instruments and then combined with the wading measurements for the purposes of this data release, which consists of a comma-delimited (*.csv) text file with three columns: Easting_m, Northing_m, and Depth_m; the units of the spatial coordinates and the depths are meters. This ground-based depth data set was used to calibrate (i.e., train) models for inferring water depths from passive optical image data and to assess the accuracy of image-derived depth estimates.

Field measurements of water depth were acquired from a reach of the American River at Sailor Bar, near Fair Oaks, California, October 19-21, 2020, to support research on remote sensing of water depth from satellite images. The depth measurements included in this data release were obtained via two different methods: 1) By wading the shallow channel margins with RTK GPS receivers and measuring water surface elevations along the water's edge and bed elevations within the channel; depths were calculated by subtracting bed elevations from the nearest water surface elevation. 2) For the deeper areas representing most of the channel, depths were recorded along a series of cross-sections by a SonTek RiverSurveyor S5 acoustic Doppler current profiler (ADCP) and a SonarMite single beam echo sounder deployed from a boat. The spatial location of each measurement was obtained via Trimble R10 RTK GPS receivers. The map projection and datum for these data are UTM Zone 10 N and WGS84, respectively. The ADCP- and echo sounder-based depth measurements were cross-calibrated to one another using collocated observations from the two instruments and then combined with the wading measurements for the purposes of this data release, which consists of a comma-delimited (*.csv) text file with three columns: Easting_m, Northing_m, and Depth_m; the units of the spatial coordinates and the depths are meters. This ground-based depth data set was used to calibrate (i.e., train) models for inferring water depths from passive optical image data and to assess the accuracy of image-derived depth estimates.

Field-based real-time kinematic (RTK) GNSS surveys of water surface elevation and channel bed topography were collected along the Colorado River, focusing on two cross-sections from which remotely sensed data were obtained. These data were used to assess the accuracy of river bed elevations inferred from the ASTRALiTe bathymetric lidar, acquired from an unmanned aircraft system (UAS). These data sets were collected to support research focused on developing innovative methods for non-contact measurement of river discharge based on various forms of remotely sensed data. The RTK GNSS surveys were performed using a local base station and Trimble R8 and R10 receivers while wading the channel at each cross-section. The survey data were post-processed by performing an OPUS correction of the static observations collected by the base and adjusting all of the survey points accordingly. The survey data were exported to a comma-separated text file and the resulting *.csv file contain for each point number a point ID, spatial coordinates, elevations, and a depth.

Field-based real-time kinematic (RTK) GNSS surveys of water surface elevation and channel bed topography were collected along the Colorado River, focusing on two cross-sections from which remotely sensed data were obtained. These data were used to assess the accuracy of river bed elevations inferred from the ASTRALiTe bathymetric lidar, acquired from an unmanned aircraft system (UAS). These data sets were collected to support research focused on developing innovative methods for non-contact measurement of river discharge based on various forms of remotely sensed data. The RTK GNSS surveys were performed using a local base station and Trimble R8 and R10 receivers while wading the channel at each cross-section. The survey data were post-processed by performing an OPUS correction of the static observations collected by the base and adjusting all of the survey points accordingly. The survey data were exported to a comma-separated text file and the resulting *.csv file contain for each point number a point ID, spatial coordinates, elevations, and a depth.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the American River near Fair Oaks, CA, acquired in October 2020. Field measurements of water depth available through another data release (Legleiter, C.J., and Harrison, L.R., 2022, Field measurements of water depth from the American River near Fair Oaks, CA, October 19-21, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P92PNWE5) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: American_mean-spec.tif, American_mean-depth.tif, American_NN-depth.tif, and American-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.