River Sediment Database (RivSed)

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.