The NAIP Imagery Hybrid (US Edition) web map features recent high-resolution National Agriculture Imagery Program (NAIP) imagery for the United States and is optimized for display quality and performance. The map also includes a reference layer. This NAIP imagery is from the USDA Farm Services Agency. The NAIP imagery in this map has been visually enhanced and published as a raster tile layer for optimal display performance.NAIP imagery collection occurs on an annual basis during the agricultural growing season in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection.This basemap is available in the United States Vector Basemaps gallery and uses NAIP Imagery and World Imagery (Firefly) raster tile layers. It also uses the Hybrid Reference (US Edition) and Dark Gray Base (US Edition) vector tile layers.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. This service contains NAIP imagery from 2017 in the Web Mercator projection.

This image layer features recent high-resolution (1m) aerial imagery for the continental United States made available by the USDA Farm Services Agency in 2014. The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition. This image layer provides access to NAIP imagery in 2014 for each state (as available). This imagery is published in 4-bands (RGB and Near Infrared), and is displayed in Natural Color. You also have the the option to change the image display to use false color (Renderer: FalseColorComposite) to show the IR band or to display the Normalized Difference Vegetation Index (Renderer: NDVI_Color) showing relative biomass of an area. Use this image layer if you want to display NAIP imagery for 2014 at large scales. You can add this image layer directly to your map but in order to view the imagery, you'll need to know where NAIP 2014 imagery is available and zoom to your local level area of interest before imagery will display. We suggest using the NAIP 2014 web map for a better experience.This image layer includes USA NAIP 2014 imagery. You can discover and access other layers available for NAIP through the Living Atlas of the World and through the NAIP Imagery group.

This data layer is an element of the Oregon GIS Framework. A mosaic derived from half-meter resolution color Digital Orthophoto Quadrangles (DOQ) of the entire state of Oregon from the summer of 2005 for multiple state agencies in Oregon. The original content was produced utilizing the scanned aerial film acquired during peak agriculture growing seasons under the National Agriculture Imagery Program (NAIP) under contract for the United States Department of Agriculture (USDA) for the Farm Service Agency's (FSA) Compliance Program. A DOQ is a raster image in which displacement in the image caused by sensor orientation and terrain relief has been removed. A DOQ combines the image characteristics of a photograph with the geometric qualities of a map. The geographic extent of the DOQ is a full 7.5-minute map (latitude and longitude) with a nominal buffer. The horizontal accuracy is within 5 meters of reference ortho imagery (1992 USGS DOQs.) The 1992 USGS DOQ imagery met National Map Accuracy Standards at 1:24,000 scale for 7.5-minute quadrangles. Translated to the ground, the 0.5 mm error distance at 1:24,000 scale is 39.4 ft (12 meters), making the absolute accuracy for the 2005 Oregon DOQs +/- 17 meters. The process of reprojecting and mosaicing the images may have added a potential shift of +/-0.75 meters, making the cumulative accuracy +/-17.75 meters. The original images were projected into Oregon Lambert (2992). The imagery is provided in the Web Mercator Auxiliary Sphere projection as a tiled service, and in the State Lambert projection as an image service. Using web services to stream imagery: https://imagery.oregonexplorer.info/arcgis/rest/services/NAIP_2005

NAIP Imagery, (2016) - Shows imagery of the National Agriculture Imagery Program (NAIP) during the 2016 agricultural growing season in Indiana (from June 2016 through August 2016). The purpose of the program is to provide current information regarding agricultural conditions in support of the United States Department of Agriculture (USDA) farm programs. This imagery has a cell size of 60 cm by 60 cm. Please visit the following Web page of the Indiana Spatial Data Portal (ISDP) for more detailed information and access to downloads: 2012 National Agriculture Imagery Program (UITS downloads will show 2016 NAIP imagery results) For more information, please visit the following URL - USDA NAIP Imagery information page

This data publication contains both field and spatial components of a research project regarding the role of fire refugia in promoting ecosystem resilience. These data were collected in the western regions of the United States including the Southern Rocky Mountains (Colorado and New Mexico), the Blue Mountains (Oregon), the Kaibab and Coconino Plateaus and Mogollon Rim (Arizona). Data were collected at 12 field sites which were within burned areas selected for their occurrence in a post-fire time frame considered sufficient for observation of forest recovery. Field data were collected in 2017 along a spatial gradient defined by distance from surviving trees; the tree maps were developed using National Agriculture Imagery Program (NAIP) digital images taken post-fire (2007-2014) at the field sites. Variables measured in the field included species, cover and height of herbaceous and woody plants, ground cover and data on residual trees.Data were collected to meet the project objective: To assess the ecological role of fire refugia as a component of ecosystem resilience.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here.

Base Imagery acquired from the USDA FSA Aerial Photography Field Office acquired through the National Agriculture Imagery Program: 2005 - 1:12,000-scale true color ortho-rectified imagery, compressed county mosaic,2 meter pixel resolution. Ancillary Imagery acquired by the Kansas Applied Remote Sensing Program, a division of the Kansas Biological Survey: October 26, 2005 - 1:8,500-scale color infrared digital ortho-imagery, uncompressed, 0.75 meter pixel resolution.

A combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here.

Maps used for field sampling were created using 2013 National Agriculture Imagery Program (NAIP) aerial imagery. Higher resolution imagery was downloaded from the Clark County GIS website for the Water Resources Education Center (Clark County 2014).

Aerial imagery of the state of Washington, from National Agricultural Imagery Program (NAIP)

OverviewThis dataset contains real overhead images of wind turbines in the US collected through the National Agriculture Imagery Plan (NAIP), as well as synthetic overhead images of wind turbines created to be similar to the real images. All of these images are 608x608. For more details on the methodology and data, please read the sections below, or look at our website: Locating Energy Infrastructure with Deep Learning (duke-bc-dl-for-energy-infrastructure.github.io).Real DataThe real data consists of images.zip and labels.zip. There are 1,742 images in images.zip, and for each image in this folder, there is a corresponding label with the same name, but a different extension. Some images do not have labels, meaning there are no wind turbines in those images. Many of these overhead images of wind turbines were collected from Power Plant Satellite Imagery Dataset (figshare.com) and then hand labeled. Others were collected using Google Earth Engine or EarthOnDemand and then labeled. All of the images collected are from the National Agricultural Imagery Program (NAIP), and all are 608x608 pixels. The labels are in YOLOv3 format, meaning each line in the text file corresponds with one wind turbine. Each line is formatted as: class x_center y_center width height. Since there is only one class, class is always zero, and the x, y, width, and height are relative to the size of the image and are between 0-1.The image_locations.csv file contains the latitude and longitude for each image. It also contains the image's geographic domain that we defined. Our data comes from what we defined as four regions - Northeast (NE), Eastern Midwest (EM), Northwest (NW), and Southwest (SW), and these are included in the csv file for each image. These regions are defined by groups of states, so any data in WA, ID, or MT would be in the Northwest region.Synthetic DataThe synthetic data consists of synthetic_images.zip and synthetic_labels.zip. These images and labels were automatically generated using CityEngine. Again, all images are 608x608, and the format of the labels is the same. There are 943 images total, and at least 200 images for each of the four geographic domains that we defined in the US (Northwest, Southwest, Eastern Midwest, Northeast). The generation of these images consisted of the software selecting a background image, then generating 3D models of turbines on top of that background image, and then positioning a simulated camera overhead to capture an image. The background images were collected nearby the locations of the testing images.ExperimentationOur Duke Bass Connections 2020-2021 team performed many experiments using this data to test if the synthetic imagery could help the performance of our object detection model. We designed experiments where we would have a baseline dataset of just real imagery, train and test an object detection model on it, and then add in synthetic imagery into the dataset, train the object detection model on the new dataset, and then compare it's performance with the baseline. For more information on the experiments and methodology, please visit our website here: Locating Energy Infrastructure with Deep Learning (duke-bc-dl-for-energy-infrastructure.github.io).

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here.

To complete the automated phase, CTI subcontracted with Photo Science (based in Lexington, KY) to create a BIBE landform layer and a drainage/wash layer. Photo Science reviewed and acquired all National Elevation Dataset (NED) 10-meter DEMs for the project area and mosaiced them into a seamless coverage. The DEM data was then manipulated to create the following derived spatial layers: aspect, slope, three hillshade datasets (different azimuth angles), a contour range layer, and a compound topographic index (or wetness index) that models water flow and accumulation. Similarly, Photo Science also acquired the 2012 National Agriculture Imagery Program (NAIP) imagery for the entire project area as high-resolution (1-meter pixels) digital ortho quarter quadrangles (DOQQs). The NAIP DOQQs were mosaiced and resampled from 1-meter to 10-meter pixels to match the DEM resolution. Erdas Imagine software was then used to derive a normalized difference vegetation index (NDVI) and a near infrared (NIR) band texture layer from the imagery using a 9x9 moving window. During the planning and coordination phase, CTI staff reviewed all available digital imagery for its potential use as the BIBE basemap. The most promising and easy to access was the data catalog found on the Texas Natural Resource Information System (TNRIS) website. Navigating to the orthoimagery-statewide web page, the list of existing imagery covering BIBE included multiple NAIP products. The corresponding 2010 and 2012 NAIP 1-meter DOQQs for BIBE were downloaded and used during the early planning stages of this project and to produce field maps and interim products.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

The following is excerpted from the metadata provided by NASS (USDA) for the source data set CDL_2012_CLIP_20131230093625_434373848.tif: "The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2012 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. "Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the USGS National Land Cover Database 2006 (NLCD 2006), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter 16 day Normalized Difference Vegetation Index (NDVI) composites. "Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The NLCD 2006 is used as non-agricultural training and validation data. Please refer to the 'Supplemental_Information' Section of this metadata file for a complete list of all imagery, ancillary data, and training/validation data used to generate this state's CDL. The strength and emphasis of the CDL is agricultural land cover. Please note that no farmer reported data are derivable from the Cropland Data Layer."

Digitization of oil and gas well pad sites in the Piceance region of Western Colorado. Well pad sites were delineated using a modified version of the Rapid Land Cover Mapping protocol (Preston and Kim, 2016). The base imagery used to delineate boundaries is the 2015 National Agriculture Imagery Program (NAIP) imagery. Well coordinate locations facilitating the targeting of well pad sites were downloaded from the Colorado Oil and Gas Conservation Commission (COGCC) in February 2016.

This model is a two-dimensional (2D) hydraulic model created in the Hydraulic Engineering Center’s River Analysis System (HEC-RAS). The model was created for a segment of the San Saba River between Harkeyville and San Saba, TX, USA. The model’s geometry is based on United States Geological Survey 3D Elevation Program data collected in 2018, and the channel bathymetry was burned in using cross-sectional data collected by Texas State University researchers in 2018. The model was calibrated using water surface elevation and velocity measurements taken during field data collection. Methods Available data: Researchers from Texas State University collected depth, flow velocity, and wetted width data at 200 cross-sections spaced approximately 350 ft apart using the equipment listed in Table 1. Table 1. Equipment used and their accuracy for Texas State University data collection. Table from Harris et al. (2023).

Parameter

Equipment

Unit Accuracy

Location

GPSMap 64 Handheld GPS

10-50 feet

Velocity

Hach Velocity Meter (Model FH950.1)

0.1 feet/second

Depth

An adjustable “ruler” stick with feet as units

0.1 feet

Wetted Width

Laser Technology Inc. TruPulse 360r

3 feet to nonideal (natural) target

Data was collected between June 4th and June 27th, 2018. During this time period, USGS gage 08146000 (San Saba, TX) recorded discharges ranging from 11.9 to 396 cfs, with an average discharge of 20 cfs. USGS 3DEP 1 m resolution data collected between February 14th and April 22nd, 2018, was used to create the HEC-RAS terrain (Merrick-Surdex 2018). Discharge at USGS gage 08146000 ranged from 40.5 to 966 cfs during this time period. For much of the time period, the discharge was approximately 60 cfs. Bathymetric areas: The 3DEP data was imported as a terrain in HEC-RAS v.6.2, and field-collected cross sections were burned into the channel following methods from Harris et al. (2023). The 95 most upstream sites in the segment were associated with a single depth measurement in the center of the channel, whereas the remaining 105 cross sections were associated with three depth measurements collected in the center of the channel and on the left and right, although the position of measurements were not recorded. For cross sections that had three depth measurements, if the standard deviation of the depth exceeded 0.25 ft, all three measurements were used to delineate the cross section in HEC-RAS. For all other cross sections, a single depth was used to delineate the cross section (either the single available depth measurement or the average depth based on three measurements; Harris et al., 2023). A final bathymetric/topographic surface was generated following Harris et al. (2023) using inverse distance weighted interpolation with the field-collected cross sections to estimate channel bathymetry. Landcover was delineated using aerial photography (USDA 2018) and associated Manning’s N roughness values were determined following Chow (1959) and Harris et al. (2023) (Table 2). Table 2. Selected Manning’s N roughness values based on delineated landcover. Adapted from Harris et al. (2023).

Landcover Description

Chow 1959 Description, which has minimum/normal/maximum ranges (Manning's n Values (orst.edu))

Selected Roughness

Channel

(Main channel or Mountain Streams)

Channel

Sluggish reaches, weedy, deep pools (normal)

0.07

Channel2

clean, winding, some pools and shoals, some weeds and more stones (maximum)

0.05

Channel3

Clean, straight, full stage, no rifts or deep pools (minimum)

0.025

Cobbly3

No vegetation in channel, banks usually steep, trees and brush along banks submerged at high stages, bottom: gravel, cobbles, and few boulders (minimum)

0.03

Ineffective Sec2

Sluggish reaches, weedy, deep pools (maximum)

0.08

Ineffective Sec3

Very weedy reaches, deep pools, or floodways with heavy stand of timber and underbrush (normal)

0.1

Ineffective Sec4

Very weedy reaches, deep pools, or floodways with heavy stand of timber and underbrush (maximum)

0.15

Intermediate Zone

(Floodplains)

Grassy Floodway

Scattered brush/heavy weeds (maximum) or light brush and trees in summer (between normal and maximum)

0.07

Floodplain

(Floodplains)

Dense Woody

Dense willows, summer straight (minimum) or heavy stand of timber, downed trees, little undergrowth (normal)

0.1

Dense Woody2

Dense willows, summer straight (normal)

0.2

Sparse Shrub

Light to dense brush (Various definitions, ranges from minimum to maximum)

0.08

NoData

Scattered brush, heavy weeds (between normal and maximum)

0.06

A 2-D HECRAS mesh was created following Harris et al. (2023) with a mesh size of 40 square feet and a breakline with cell size of 20 feet located in the center of the channel. A 12 cfs unsteady flow simulation was run as a “hot-start” to fill the modeled channel and subsequently used as the initial conditions for additional flows simulated for the segment. Because the discharge recorded at USGS gage 08146000 varied during the field sampling period, different sections of the segment were calibrated to different discharges to match field conditions at the time of data collection (Table 3). Table 3. Discharges used to calibrate 2-D HEC-RAS model based on discharges recorded at USGS gage 08146000 during field collection dates in 2018.

Calibration discharge (cfs)

Average field discharge (cfs)

Range of field discharges (cfs)

Dates (2018)

Cross section

12

12.5

9.7-15.6

6/25; 6/27

29370-20044; 8279-467

16

15.9

13.5-17.4

6/21; 6/26

19597-8667

20

20

16.8-22.4

6/13-6/14; 6/20

49011-29699

26

26.5

21.1-30.3

6/12

56420-49340

34

33.8

30.3-36.5

6/11

63009-56840

40

40.3

20.6-114

6/4

72209-63766

Calibration was conducted in accordance with methods from Harris et al. (2023), with an initial channel roughness of 0.07 adjusted on a case-by-case basis throughout the segment based on comparisons of field-measured and modeled depth and velocity at cross sections. In addition, modeled channel widths were compared to aerial imagery for select discharges, and floodplain roughness was adjusted as needed in an attempt to match channel width from imagery (USDA, 2004-2018; Table 4). Table 4. Average discharge recorded at USGS gage 08146000 on select dates when aerial imagery from the National Agricultural Inventory Program (NAIP) was available (USDA, 2004-2018), used for comparison of imagery channel width with modeled channel width.

Discharge (cfs)

Imagery

Imagery date

12.5

NAIP

August 16th, 2006

22

NAIP

July 12th, 2014

54

NAIP

July 31st, 2010

86

NAIP

August 3rd, 2016

241

NAIP

December 12th, 2004

1600

NAIP

October 26th, 2018

The final overall root mean-squared error of the model after calibration was 0.31 ft s-1 for velocity and 0.34 ft for depth. Error at individual cross sections was also recorded for reference purposes. Summary of assumptions: This HEC-RAS model has assumptions matching those of Harris et al. (2023). Discharge data from 2018 at USGS gage 08146000 (San Saba, TX) have been approved by USGS. Usage notes: HEC-RAS 6.2 is a free hydraulic analysis software available for download from the U.S. Army Corps of Engineers. References: Chow VT. Open-channel hydraulics: New York: McGraw-Hill; 1959. Harris A, Wiest S, Cushway KC, Mitchell ZA, Schwalb AN. Hydraulic model (HEC-RAS) of the Upper San Saba River between For McKavett and Menard, TX [Dataset]. Dryad Data Repository; 2023. https://doi.org/10.5061/dryad.pc866t1tt. Merrick-Surdex. Lidar Mapping Report. 2018. Prepared for United States Geological Survey contract G16PC0029. Mitchell ZA. The role of life history strategies and drying events in shaping mussel communities: a multiscale approach [dissertation]. San Marcos (TX): Texas State University. 2020. Mitchell ZA, Cottenie K, Schwalb AN. Trait-based and multi-scale approach provides insight on responses of freshwater mussels to environmental heterogeneity. Ecosphere. 2023; 14(7):e4533. https://doi.org/10.1002/ecs2.4533. Mitchell ZA, Schwalb AN, Cottenie K. Trait-based and multi-scale approach provides insight on responses of freshwater mussels to environmental heterogeneity [Dataset]. Dryad Data Repository; 2023. https://doi.org/10.5061/dryad.msbcc2g3d. United States Department of Agriculture (USDA). Texas NAIP Imagery, 2018. Web. 2022-03-09.

This map features recent high-resolution (1m) aerial imagery for the continental United States made available by the USDA Farm Services Agency. The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition. This image layer provides access to the most recent NAIP imagery for each state and will be updated annually as new imagery is made available. This imagery is published in 4-bands (RGB and Near Infrared), where available, with the option to display the imagery as false color to show the IR band or to display the NDVI (Normalized Difference Vegetation Index) showing relative biomass of an area.This map features the NAIP image layer along with the Imagery with Labels basemap for reference purposes. The NAIP imagery may be more recent in some cases than the current imagery in the World Imagery basemap, so you can use them both for comparison purposes. The map also includes a World Transportation layer than can be turned on at large scales for additional reference information (e.g. street labels).The image layer currently includes NAIP 2010-2014 imagery, having been updated recently to include NAIP 2014 imagery where available. It will be updated with NAIP 2015 imagery as that becomes publicly available.

This data release includes cross section survey data collected during site visits to USGS gaging stations located throughout the Willamette and Delaware River Basins and multispectral images of these locations acquired as close in time as possible to the date of each site visit. In addition, MATLAB source code developed for the Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) framework is also provided. The site visit data were obtained from the Aquarius Time Series database, part of the USGS National Water Information System (NWIS), using the Publish Application Programming Interface (API). More specifically, a custom MATLAB function was used to query the FieldVisitDataByLocationServiceRequest endpoint of the Aquarius API by specifying the gaging station ID number and the date range of interest and then retrieve the QRev XML attachments associated with site visits meeting these criteria. These XML files were then parsed using another custom MATLAB function that served to extract the cross section survey data collected during the site visit. Note that because many of the site visits involved surveying cross sections using instrumentation that was not GPS-enabled, latitude and longitude coordinates were not available and no data values (NaN) are used in the site visit files provided in this data release. Remotely sensed data acquired as close as possible to the date of each site visit were also retrieved via APIs. Multispectral satellite images from the PlanetScope constellation were obtained using custom MATLAB functions developed to interact with the Planet Orders API, which provided tools for clipping the images to a specified area of interest focused on the gaging station and harmonizing the pixel values to be consistent across the different satellites within the PlanetScope constellation. The data product retrieved was the PlanetScope orthorectified 8-band surface reflectance bundle. PlanetScope images are acquired with high frequency, often multiple times per day at a given _location, and so the search was restricted to a time window spanning from three days prior to three days after the site visit. All images meeting these criteria were downloaded and manually inspected; the highest quality image closest in time to the site visit date was retained for further analysis. For the gaging stations within the Willamette River Basin, digital aerial photography acquired through the National Agricultural Imagery Program (NAIP) in 2022 were obtained using a similar set of MATLAB functions developed to access the USGS EarthExplorer Machine-to-Machine (M2M) API. The NAIP quarter-quadrangle image encompassing each gaging station was downloaded and then clipped to a smaller area centered on the gaging station. Only one NAIP image at each gaging station was acquired in 2022, so differences in streamflow between the image acquisition date and the date of the site visit closest in time were accounted for by performing separate NWIS web queries to retrieve the stage and discharge recorded at the gaging station on the date the image was acquired and on the date of the site visit. These data sets were used as an example application of the framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) and this data release also provides MATLAB source code developed to implement this approach. The code is packaged in a zip archive that includes the following individual .m files: 1) getSiteVisit.m, for retrieving data collected during site visits to USGS gaging stations through the Aquarius API; 2) Qrev2depth.m, for parsing the XML file from the site visit and extracting depth measurements surveyed along a channel cross section during a direct discharge measurement; 3) orderPlanet.m, for searching for and ordering PlanetScope images via the Planet Orders API; 4) pollThenGrabPlanet.m, for querying the status of an order and then downloading PlanetScope images requested through the Planet Orders API; 5) organizePlanet.m, for file management and cleanup of the original PlanetScope image data obtained via the previous two functions; 6) ingestNaip.m, for searching for, ordering, and downloading NAIP data via the USGS Machine-to-Machine (M2M) API; 7) naipExtractClip.m, for clipping the downloaded NAIP images to the specified area of interest and performing file management and cleanup; and 8) crossValObra.m, for performing spectrally based depth retrieval via the Optimal Band Ratio Analysis (OBRA) algorithm using a k-fold cross-validation approach intended for small sample sizes. The files provided through this data release include: 1) A zipped shapefile with polygons delineating the Willamette and Delaware River basins 2) .csv text files with information on site visits within each basin during 2022 3) .csv text files with information on PlanetScope images of each gaging station close in time to the date of each site visit that can be used to obtain the image data through the Planet Orders API or Planet Explorer web interface. 4) A .csv text tile with information on NAIP images of each gaging station in the Willamette River Basin as close in time as possible to the date of each site visit, along with the stage and discharge recorded at the gaging station on the date of image acquisition and the date of the site visit. 5) A zip archive of the clipped NAIP images of each gaging station in the Willamette River Basin in GeoTIFF format. 6) A zip archive with source code (MATLAB *.m files) developed to implement the Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) framework.

This data set consists of repeat digital imagery from the tower-mounted digital cameras (hereafter, PhenoCams) at the Jornada Experimental Range. JER is a member of the PhenoCam network, which has as its mission to serve as a long-term, continental-scale, phenological observatory. Imagery is uploaded to the PhenoCam server every 30 minutes. The archived images provide a permanent record that can be visually-inspected to determine the phenological state of the vegetation at any point in time. Vegetation greenness metrics (e.g., GCC) derived from the ratio of the green color band to sum of red, green, and blue color bands serve as proxies for vegetation greenness. Greenness metrics can be extracted from the images using simple image processing methods in 1-day or 3-day increments. This dataset is available to the public and may be freely downloaded. Please keep the designated Contact person informed of any plans to use the dataset. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the dataset must include proper acknowledgement. The development of PhenoCam has been supported by multiple entities. Those include the Northeastern States Research Cooperative, NSF’s Macrosystems Biology program (award EF-1065029), DOE’s Regional and Global Climate Modeling program (award DE-SC0016011), the US National Park Service Inventory and Monitoring Program, the USA National Phenology Network (grant number G10AP00129 from the United States Geological Survey), and is currently supported by the Department of Agriculture (USDA) Agricultural Research Service. This research is a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR and the Jornada Experimental Range are supported by the United States Department of Agriculture (USDA) Agricultural Research Service.