Estimating estuarine primary production using satellite data and machine learning

Keywords: primary production, machine learning, remote sensing, estuaries, MODIS, Tampa Bay
Abstract Type: Virtual Paper Abstract

Abstract

Net primary production (NPP) plays an important role in estuarine carbon cycling, which has been increasingly impacted by human activities and global climate change. Spatiotemporal trends of NPP in the open ocean have been well studied using satellite data and standard primary production algorithms such as the Vertically Generalized Production Model (VGPM), but these algorithms are generally not suitable for estuarine and coastal waters. Here we make a first attempt to use machine learning algorithms to estimate NPP in an estuarine environment from satellite measurements. Tampa Bay, the largest estuary in Florida, has abundant in situ NPP measurements, though its spatiotemporal variability remains unrevealed. Combining these data with concurrent MODIS/Aqua image data, we developed and evaluated seven machine learning algorithms, and applied the one with the least estimation error and highest correlation coefficient to establish a time-series NPP record for Tampa Bay (2002 to 2020). MODIS NPP shows temporal variations that are largely driven by temperature: lowest values in winter, highest values in summer, and an increasing trend from 2003 to 2020, highlighting the impact of climate warming on estuarine NPP. The spatial distribution of MODIS NPP shows higher values in Hillsborough Bay, Middle and Lower Tampa Bay, and relatively lower values in Old Tampa Bay, a pattern that likely reflects differences in river discharge. The long-term NPP product derived from machine learning algorithms and satellite data can complement existing field-based monitoring programs and help to understand estuarine responses to climate changes and human impacts, and design relevant mitigation strategies.