Exploiting Machine Learning to Develop Ocean Color Retrievals from the Tropospheric Emissions: Monitoring of Pollution Instrument

作者：Zachary Fasnacht, Joanna Joiner, Matthew Bandel, Amir Ibrahim, Andrew K. Heidinger, Michael D. Himes, James G. Allen, James L. Carr, Xiong Liu, Heesung Chong, N. A. Krotkov · 年份：2025 · DOI：10.22541/essoar.174326553.36262101/v1 · 研究领域：Water Quality Monitoring and Analysis

Retrievals of ocean color (OC) properties from space are important for understanding the ocean ecosystem, the carbon cycle, and monitoring events such as harmful algal blooms (HABs). NASA’s recently launched Earth Venture Instrument (EVI), the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO), provides a unique opportunity to examine diurnal variability in ocean ecology across coastal waters of North America and prepare for future hyperspectral geostationary OC missions. Although TEMPO does not match the spatial resolution or spectral coverage of planned coastal ocean sensors, such as NASA’s Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or NOAA’s Geostationary Extended Observations Ocean Color Instrument (GeoXO OCX), it provides hourly observations at approximately 5\,km over U.S. coastal regions and the Great Lakes. Here, we apply a newly developed atmospheric correction approach based on principal component analysis combined with machine learning (ML) to retrieve OC properties using TEMPO’s hyperspectral measurements. Principal component coefficients derived from measured reflectances are used to train a neural network to estimate OC properties, including chlorophyll concentration, informed by collocated physically-based retrievals from MODIS, VIIRS, and OLCI. This ML-based approach complements traditional radiative transfer retrievals, particularly under challenging conditions such as glint and moderate cloud coverage. This approach ...