The model is slightly different for different satellite collections. We use GEE to implement an empirical model from the literature to estimate the chlorophyll -a (chl -a) concentration in each pixel for each image over the entire 40-year history.

After masking, the remaining pixels represent the extent of the visible water body at the time of the image. We perform quality masking based on metadata included in the Landsat level 2 data quality band. The quality masking excludes pixels that contain clouds, significant atmospheric moisture, or other issues that would affect the accuracy of the data. We present the GEE commands or tools to mask these images to only include pixels in a selected region around a selected water body (spatial masking) pixels that represent the water, as most water bodies change size due to fluctuating water levels (classification masking) and pixels that meet a quality threshold to exclude pixels with issues that affect model accuracy (quality masking). These satellites represent data from 1984 until the present at 16-day intervals: over 1,000 images, though not all images are usable due to weather, ice, and other issues. Our tools collect and process Landsat surface reflectance images from three different Landsat missions: Landsat 5, Landsat 7, and Landsat 8. We demonstrate these tools with a case study on Utah Lake, Utah, USA. We present a set of Google Earth Engine (GEE) tools to process long-term satellite imagery to analyze the time history of algal concentrations in a lake or reservoir.

Google earth c