GOME-2A and GOME-2B solar induced fluorescence (SIF) noise reduced datasets (daily orbital and monthly averages)

These datasets were created using a machine learning algorithm that reduces noise and artifacts in the original noisy satellite data product, solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment 2 (GOME-2) instruments on the European operational Meteorological Satellites A and B (METOP-A and METOP-B). Noise reduction is important because SIF retrievals are typically noisy, and the noise limits their ability to be used for diagnosing plant health and productivity. Our results show substantial improvement in SIF retrievals that may lead to new applications. However, users should be aware that there could be some biases with respect to the original data sets so the data should be used with caution. These data sets were not designed to be used in trend studies as the calibration could contain errors.