LimnoSat-US: A Remote Sensing Dataset for U.S. Lakes from 1984-2020
- 1. University of North Carolina at Chapel Hill
- 2. Colorado State University
Description
LimnoSat-US is an analysis-ready remote sensing database that includes reflectance values spanning 36 years for 56,792 lakes across > 328,000 Landsat scenes. The database comes pre-processed with cross-sensor standardization and the effects of clouds, cloud shadows, snow, ice, and macrophytes removed. In total, it contains over 22 million individual lake observations with an average of 393 +/- 233 (mean +/- standard deviation) observations per lake over the 36 year period. The data and code contained within this repository are as follows:
HydroLakes_DP.shp: A shapefile containing the deepest points for all U.S. lakes within HydroLakes. For more information on the deepest point see https://doi.org/10.5281/zenodo.4136754 and Shen et al (2015).
LakeExport.py: Python code to extract reflectance values for U.S. lakes from Google Earth Engine.
GEE_pull_functions.py: Functions called within LakeExport.py
01_LakeExtractor.Rmd: An R Markdown file that takes the raw data from LakeExport.py and processes it for the final database.
SceneMetadata.csv: A file containing additional information such as scene cloud cover and sun angle for all Landsat scenes within the database. Can be joined to the final database using LandsatID.
srCorrected_us_hydrolakes_dp_20200628: The final LimnoSat-US database containing all cloud free observations of U.S. lakes from 1984-2020. Missing values for bands not shared between sensors (Aerosol and TIR2) are denoted by -99. dWL is the dominant wavelength calculated following Wang et al. (2015). pCount_dswe1 represents the number of high confidence water pixels within 120 meters of the deepest point. pCount_dswe3 represents the number of vegetated water pixels within 120 meters and can be used as a flag for potential reflectance noise. All reflectance values represent the median value of high confidence water pixels within 120 meters. The final database is provided in both as a .csv and .feather formats. It can be linked to SceneMetadata.cvs using LandsatID. All reflectance values are derived from USGS T1-SR Landsat scenes.
Files
SceneMetadata.csv
Files
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Additional details
Related works
- Is supplement to
- Dataset: 10.5281/zenodo.3838999 (DOI)
- Journal article: 10.1029/2019WR024883 (DOI)
References
- Shen, Z., Yu, X., Sheng, Y., Li, J., & Luo, J. (2015). A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration. PLOS ONE, 10(12), e0144700. https://doi.org/10.1371/journal.pone.0144700
- Wang, S., Li, J., Shen, Q., Zhang, B., Zhang, F., & Lu, Z. (2015). MODIS-Based radiometric color extraction and classification of inland water with the forel-ule scale: A case study of lake Taihu. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 907–918. https://doi.org/10.1109/JSTARS.2014.2360564