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The River Sediment Database (RiverSed) database contains Total Suspended Sediment (TSS) concentrations derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. TSS concentrations represent reach integrated medians concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected ithin each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). Files: 1) Metadata (RiverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (riverSed_usa_v1.0.txt). Table of TSS concentration and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv). 6) Matchup database with extended metadata on locations and in-situ data (Aquasat_TSS_v1.0.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_v1.csv) 8) The xgboost model that can make TSS predictions over inland waters in USA with 9 Landsat bands/band combinations (final_model_xgbLinear_v1.rds). The model can be loaded in R. Future version will have the xgb object to be compatible across languages.

This dataset contains the two datasets detailed in "Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery" by D.S. Dale Et al. (2023). The file "LonokeComplete.zip" file contains 16 .lif files that were used in the training and testing phase of the study. The "55tilesComplete.zip" file contains 110 .tif files (55 image and 55 label). These images were used to assess the models spatial transferability. Both file configurations are processed by the code linked in the paper. Supported in Part by NASA Water Resources Award 80NSSC22K0923 and U.S. Geological Survey under Cooperative Agreement G20AC00448 and G21AC10729. {"references": ["DS Dale Et al., 2023"]}

The River Sediment Database (RivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of NHDPlusV2 centerlines where high quality river water pixels were detected within each Landsat image from 1984-2018. This is built in the River Surface Reflectance database (RiverSR) also in Zenodo (Gardner et al,. 2020 Geophysical Research Letters). The paper associated with RivSed: Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8 Files: 1) Metadata (riverSed_v1.0_metadata.pdf): Description of all data files associated with this repository. 2) RiverSed (RiverSed_USA_v1.1.txt). Table of SSC and associated data that is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons_v1.0.shp). 5) The look up table for reach IDs of original (COMID) and modified (ID) NHDplusV2 centerlines. (COMID_ID.csv). Short reaches were joined together to optimize for remote sensing data collection and make more consistent reach lengths. 6) SSC-Landsat matchup database with extended metadata on locations and in-situ data derived from Aquasat (Ross et al., 2019) (Aquasat_TSS_v1.1.csv) 7) The final training data used to build the xgboost machine learning model (train_clean_xgb_v1.1.csv) 8) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (finalmodel_xgb_v1.1.rds and .RData). The model can only be loaded in R for now.

This dataset includes both raw and processed data associated with the publication entitled "Assessment of the effects of the 2021 Caldor megafire on soil physical properties, eastern Sierra Nevadas, USA", published in MDPI Fire (doi: 10.3390/fire6020066). Raw files include exported .xlsx files from Meter Group HYPROP analyses, .csv files from 10 replicate measurements of saturated hydraulic conductivity for each analyzed sample using the Meter Group KSAT device, and raw .dat files from measurement of bulk thermal properties. A single additional file also documents the laboratory results from particle size and loss on ignition analyses. Processed data includes curve fitting parameters associated with fitting the soil water retention curves (SWRC) and thermal conductivity functions (TCFs) for each sample, as described in Sion et al. (2023). Additional requests associated with data from Sion et al. (2023) should be directed to the lead author.

Agriculture is the largest user of water in the United States. Yet, we do not understand the spatially resolved sources of irrigation water use by crop. The goal of this study is to estimate crop-specific irrigation water use from surface water withdrawals, total groundwater withdrawals, and nonrenewable groundwater depletion for the Continental United States. Water use by source is provided for 20 crops and crop groups from 2008 to 2020 at the county spatial resolution. These results present the first national-scale assessment of irrigation by crop, county, water source, and year. In total, there are nearly 2.5 million data points in this dataset (3,142 counties; 13 years; 3 water sources; and 20 crops). This dataset supports the paper by Ruess et al (2023) in Water Resources Research, https://doi.org/10.1029/2022WR032804. When using, please cite as: Ruess, P.J., Konar, M., Wanders, N. , & Bierkens, M. (2023). Irrigation by crop in the Continental United States from 2008 to 2020, Water Resources Research, 59, e2022WR032804. https://doi.org/10.1029/2022WR032804

Please review Zhang et al. (2021) for details on study design and datasets (https://doi.org/10.1016/j.watres.2022.118443). In summary, predictor and response variable data was acquired from the Chesapeake Bay Program and USGS. This data was subjected to a trend analysis to estimate the MK linear slope change for both predictor and response variables. After running a cluster analysis on the scaled TN loading time series (the response variable), the cluster assignment was paired with the slope estimates from the suite of predictor variables tied to the nutrient inventory and static geologic and land use variables. From there, an RF analysis was executed to link trends in anthropogenic driver and other contextual environmental factors to the identified trend cluster types. After calibrating the RF model, likelihood of improving, relatively static, or degrading catchments across the Chesapeake Bay were identified for the 2007 to 2018 period. Tabular data is available on the journal website and PUBMED, and the predictor/response variable data can be downloaded individually in the USGS and Chesapeake Bay Program links listed in the data access section

This study was supported by a Carnegie Trust for the Universities of Scotland Carnegie PhD Scholarship (to F.D.W.C.), hosted in the Edinburgh E3 U.K. Natural Environment Research Council (NERC) Doctoral Training Partnership (NE/L002558/1) and the Scottish Alliance for Geoscience, Environment and Society (SAGES) Graduate School. The study was also produced with the financial assistance of the Prince Albert II of Monaco Foundation (to F.D.W.C.), the NERC / U.S National Science Foundation (NSF) International Thwaites Glacier Collaboration grants NE/S006613 (ITGC-GHOST; to R.G.B.) and NE/S006796 (ITGC-PROPHET; to N.G.) (ITGC contribution no. ITGC-088), NERC grant NE/T001607/1 (QuORUM project to N.G. and S.F.B.T.), the ESA 4D Antarctica and Digital Twin Antarctica projects 4000128611/19/I‐DT (to N.G.), and NSF grant 2045075 (to E.J.S.). This dataset contains the grounding-line and ice-velocity change observations presented in Christie et al. (Nature Communications, 2023). Data are provided in ESRI .shp (grounding line location and change records) and .TIF (ice velocity and change records) formats, and detailed information about the data collection methods, sources and other technical information can be found within the accompanying README files inside the .ZIP folder.

Dataset Description This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters. The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus. Dataset Description Sample Count Receiver Count No-Tx Samples 46 10 to 25 1-Tx Samples 4822 10 to 25 2-Tx Samples 346 11 to 12 The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows: \(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \) Measurement Parameters Description Frequency 462.7 MHz Radio Gain 35 dB Receiver Sample Rate 2 MHz Sample Length N=10,000 Band-pass Filter 6 kHz Transmitters 0 to 2 Transmission Power 1 W Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device. Usage Instructions Data is provided in .json format, both as one file and as split files. import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys: rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name. tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude. metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords File Separations and Train/Test Splits In the separated_data.zip folder there are several train/test separations of the data. all_data contains all the data in the main JSON file, separated by the number of transmitters. stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers. train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json. The random split is a random 80/20 split of the data. special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus. The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split. The seasonal split contains data separated by the month of collection, in April or July. The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data. campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json. Digital Surface Model The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models. To read the data in python: import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product. DSM DOI: https://doi.org/10.5069/G9TH8JNQ

Code in support of "Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery" by R.L. Dell and others. The scripts provided facilitate the pre-processing of Landsat 8 images for the training, validation, and application of of a Random Forest Classifier. Scripts to train, validate, and apply a Random Forest Classifier are also provided. All scipts are written in Google Earth Engine. The methodological information relating to these scripts can be found in the companion paper: Dell RL, Banwell AF, Willis IC, Arnold NS, Halberstadt ARW, Chudley TR, Pritchard HD (2021). Supervised classification of slush and ponded water on Antarctic ice shelves using Landsat 8 imagery. Journal of Glaciology 1-14. https://doi.org/10.1017/jog.2021.114.