The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.
ILW is a times series containing 10 years of MERIS (2002-2012) and OLCI from both Sentinel-3a (2016-present) and Sentinel-3b (2018-present). ILW includes Rayleigh-corrected top-of-atmosphere reflectance ($\rho_s(\lambda)$; unitless) at 413, 443, 490, 510, 560, 620, 665, 681, 709, 754, and 885 nm and the Cyanobacteria Index (CIcyano; unitless) to estimate cyanobacteria concentrations. ILW provides daily, weekly, rolling 28-day, monthly and seasonal files and consists of L2 files, L3-binned files, and L3 standard mapped images (SMI).
The image is a 7-day composite of CIcyano data where light tan represents land and the speckling of rainbow, grey and black colors highlights both the location of inland resolvable lakes and the coastal areas where ILW data are available. The black offshore ocean area is not part of the ILW data set. The inset displays lake resolution at a more regional scale.
Direct access links to data are listed below. Look for datasets named by the convention: {SENSOR}-ILW under the Level-2, Level-3 Binned and Level-3 Mapped folders. Currently, we produce ILW datasets for MERIS, S3A-OLCI and S3B-OLCI sensors.
(2021). Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a Remote Sensing of Environment, Volume 266, 2021, 112685. doi: 10.1016/j.rse.2021.112685
