River Sediment Database (RivSed)

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