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 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.

This data release includes multispectral images and field measurements of water depth from the Sacramento River near Glenn, California, used to evaluate the potential for efficient reach-scale mapping of river bathymetry using Uncrewed Aircraft Systems (UAS). The images were acquired by a MicaSense RedEdge-MX Dual Camera deployed from a Trinity F90 vertical take-off and landing (VTOL) UAS. The 4 km long study area along the Sacramento River was subdivided into three distinct but adjacent areas of interest (AOIs) and image data were collected from one AOI each day between September 14 and 16, 2021. The image data were ortho-rectified using Quantum-Systems QBase 3D and Agisoft Metashape software and saved as GeoTIFF files. A header text file is also included with each of the images and includes information on the 10 bands of the MicaSense RedEdge-MX Dual Camera, which include: Band 1 (Coastal blue): 444 nm Band 2 (Blue): 475 nm Band 3 (Green): 531 nm Band 4 (Green): 560 nm Band 5 (Red): 650 nm Band 6 (Red): 668 nm Band 7 (Red Edge): 705 nm Band 8 (Red Edge): 717 nm Band 9 (Red Edge): 740 nm Band 10 (Near Infrared): 842 nm Each image has an associated ENVI format header text file that also contains this spectral information. The pixel values for these images are digital numbers between 0 and 65535 for all 10 spectral bands. To convert these digital numbers to reflectance values between 0 and 1, divide each pixel value in each band by 32768. To display the images as a familiar true-color composite, band 5 can be displayed as red, band 4 as green, and band 2 as blue. Water depths were measured directly in the field to calibrate relationships between depth and reflectance and to assess the accuracy of image-derived depth estimates. In shallow areas along the margins of the channel, depth measurements were made while wading with a Trimble R10 real-time kinematic (RTK) global navigation satellite system (GNSS) receiver to record spatial coordinates and riverbed elevations. Additional points along the edge of the water were surveyed to capture the water surface elevation and depths for points within the channel were calculated by subtracting the local bed elevation from the nearest water surface elevation. For deeper areas that could not be waded safely, another Trimble R10 RTK GNSS receiver was mounted on a boat with an outboard motor and connected to a Seafloor Systems SonarMite echo sounder that measured the water depth. The boat-based data were collected along a series of 25 channel-spanning cross sections and one longitudinal profile that extended over the full length of the study area. The map projection and datum for the shapefiles and GeoTIFF images contained in this data release are UTM Zone 10 N and NAD83, respectively. Each shapefile has coordinates in units of meters and the depth measurements are also in meters.

This dataset is a LAS (industry-standard binary format for storing lidar point clouds) dataset containing light detection and ranging (lidar) data and sonar data representing the beach and near-shore bathymetry of Lake Superior at Minnesota Point, near the Duluth entry, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: lidar, 0.055 meters (m); multibeam sonar, 0.511 m; single-beam sonar, 1.687 m. The LAS dataset was used to create digital elevation models (DEMs) of 10 m (32.8084 feet) and 1 m (3.28084 feet) cell size, of the approximate 1.78 square kilometer surveyed area. Lidar data were collected November 01, 2022 using a boat mounted Velodyne VLP-16 unit and methodology similar to that described by Huizinga and Wagner (2019). Multibeam sonar data were collected October 31-November 01, 2022 using a Norbit integrated wide band multibeam system compact (iWBMSc) sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected November 01, 2022 using a Ceescope echosounder and methodology similar to that described by Wilson and Richards (2006). This project followed similar methods to that of Wagner, Lund, and Sanks (2020), who completed a similar survey in 2019.

This dataset is a LAS (industry-standard binary format for storing lidar point clouds) dataset containing light detection and ranging (lidar) data and sonar data representing the beach and near-shore bathymetry of Lake Superior at Minnesota Point, near the Duluth entry, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: lidar, 0.055 meters (m); multibeam sonar, 0.511 m; single-beam sonar, 1.687 m. The LAS dataset was used to create digital elevation models (DEMs) of 10 m (32.8084 feet) and 1 m (3.28084 feet) cell size, of the approximate 1.78 square kilometer surveyed area. Lidar data were collected November 01, 2022 using a boat mounted Velodyne VLP-16 unit and methodology similar to that described by Huizinga and Wagner (2019). Multibeam sonar data were collected October 31-November 01, 2022 using a Norbit integrated wide band multibeam system compact (iWBMSc) sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected November 01, 2022 using a Ceescope echosounder and methodology similar to that described by Wilson and Richards (2006). This project followed similar methods to that of Wagner, Lund, and Sanks (2020), who completed a similar survey in 2019.

This dataset is a LAS (industry-standard binary format for storing lidar point clouds) dataset containing light detection and ranging (lidar) data and sonar data representing the beach and near-shore topography of Lake Superior at Minnesota Point, near the Duluth entry, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: lidar, 0.094 meters (m); multibeam sonar, 0.501 m; single-beam sonar, 1.876 m. The LAS dataset was used to create digital elevation models (DEMs) of 10 m (32.8084 feet) and 1 m (3.28084 feet) resolution, of the approximate 1.75 square kilometer surveyed area. Lidar data were collected August 22, 2022 using a boat mounted Velodyne VLP-16 unit and methodology similar to that described by Huizinga and Wagner (2019). Multibeam sonar data were collected August 22-23, 2022 using a Norbit integrated wide band multibeam system compact (iWBMSc) sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected August 23, 2022 using a Ceescope echosounder and methodology similar to that described by Wilson and Richards (2006).This project followed similar methods to that of Wagner, Lund, and Sanks (2020), who completed a similar survey in 2019.

This dataset is a LAS (industry-standard binary format for storing lidar point clouds) dataset containing light detection and ranging (lidar) data and sonar data representing the beach and near-shore topography of Lake Superior at Minnesota Point, near the Duluth entry, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: lidar, 0.094 meters (m); multibeam sonar, 0.501 m; single-beam sonar, 1.876 m. The LAS dataset was used to create digital elevation models (DEMs) of 10 m (32.8084 feet) and 1 m (3.28084 feet) resolution, of the approximate 1.75 square kilometer surveyed area. Lidar data were collected August 22, 2022 using a boat mounted Velodyne VLP-16 unit and methodology similar to that described by Huizinga and Wagner (2019). Multibeam sonar data were collected August 22-23, 2022 using a Norbit integrated wide band multibeam system compact (iWBMSc) sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected August 23, 2022 using a Ceescope echosounder and methodology similar to that described by Wilson and Richards (2006).This project followed similar methods to that of Wagner, Lund, and Sanks (2020), who completed a similar survey in 2019.

This dataset is a LAS (industry-standard binary format for storing large point clouds) dataset containing light detection and ranging (LiDAR) data and sonar data representing the beach and near-shore topography of Lake Superior at Minnesota Point, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: LiDAR, 0.137 meters (m); multi-beam sonar, 1.029 m; single-beam sonar, 0.999 m. The LAS dataset was used to create a 10-m (32.8084 feet) digital elevation model (DEM) of the approximately 5.9 square kilometer (2.3 square mile) surveyed area using the "LAS dataset to raster" tool in Esri ArcGIS, version 10.7. LiDAR data were collected August 10, 2019 using a boat-mounted Optech ILRIS scanner and methodology similar to that described by Huizinga and Wagner (2019). Multi-beam sonar data were collected August 7-11, 2019 using an R2Sonic 2024 sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected August 27-28, 2019 using a CEESCOPE single-beam echosounder and methodology similar to that described by Wilson and Richards (2006).

This dataset is a LAS (industry-standard binary format for storing large point clouds) dataset containing light detection and ranging (LiDAR) data and sonar data representing the beach and near-shore topography of Lake Superior at Minnesota Point, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: LiDAR, 0.137 meters (m); multi-beam sonar, 1.029 m; single-beam sonar, 0.999 m. The LAS dataset was used to create a 10-m (32.8084 feet) digital elevation model (DEM) of the approximately 5.9 square kilometer (2.3 square mile) surveyed area using the "LAS dataset to raster" tool in Esri ArcGIS, version 10.7. LiDAR data were collected August 10, 2019 using a boat-mounted Optech ILRIS scanner and methodology similar to that described by Huizinga and Wagner (2019). Multi-beam sonar data were collected August 7-11, 2019 using an R2Sonic 2024 sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected August 27-28, 2019 using a CEESCOPE single-beam echosounder and methodology similar to that described by Wilson and Richards (2006).

This dataset is a LAS dataset containing light detection and ranging (lidar) data and sonar data representing the beach and near-shore topography of Lake Superior at Minnesota Point, Duluth, Minnesota. The LAS dataset was used to create a digital elevation model (DEM) of the approximately 2.27 square kilometer surveyed area. Lidar data were collected July 23, 2020 using a boat mounted Velodyne unit. Multibeam sonar data were collected July 20th and 23rd, 2020 using a Norbit integrated wide band multibeam system compact (iWBMSc) sonar unit. Methodology similar to Wagner, D.M., Lund, J.W., and Sanks, K.M., 2020 was used.