Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

Monitoring of the phytoplankton pigment chlorophyll-a is often used as an indicator of eutrophication in coastal waters. Improved water quality monitoring using data sourced from MODIS (Moderate Resolution Imaging Spectroradiometer)-sourced data allows for infrequently sampled sites to be interrogated for long-term trends. Despite the wide availability and good spatial and temporal coverage of MODIS data, these data have had little use in operational coastal monitoring of chlorophyll-a in New Zealand. This is in part due to the poor performance of global oceanic algorithms applied in the coastal waters. Accessible algorithm tuning methods that can be validated by in situ measurements may assist the uptake of satellite data for coastal monitoring. This study presents results from regional tuning and validation of two empirical algorithm approaches, including a new simple exponential model, to estimate chlorophyll-a for two coastal locations in New Zealand. A novel method of training chlorophyll-a models using smoothed in situ data to match spatial scales of satellite observations was applied, and shows promise for improving tuned model performance. This approach shows potential for lowering barriers for researchers and coastal managers wishing to make use of the growing satellite data resource in their coastal environments.