Neural Network Technique for Global Ocean Color (Chl-a) Estimates Bridging Multiple Satellite Missions
Neural Network Technique for Global Ocean Color (Chl-a) Estimates Bridging Multiple Satellite Missions
Abstract:
A Neural Network (NN) technique is used to produce consistent global ocean color estimates, bridging multiple satellite ocean color missions by linking ocean color variability – primarily driven by biological processes – with the physical processes of the upper ocean. Satellite-derived surface variables – sea-surface temperature (SST) and sea-surface height (SSH) fields – are used as signatures of upper-ocean dynamics. The NN technique employs adaptive weights that are tuned by applying statistical learning (training) algorithms to past data sets, providing robustness with respect to random noise, accuracy, fast emulations, and fault-tolerance. This study employs Sea-viewing Wide Field-of-View Sensor (SeaWiFS) chlorophyll-a data for 1998-2010 in conjunction with satellite SSH and SST fields. After interpolating all data sets to the same two-degree latitude-longitude grid, the annual mean was removed and monthly anomalies extracted . The NN technique wass trained for even years of that period and tested for errors and bias for the odd years. The NN output are assessed for: (i) bias, (ii) variability, (iii) root-mean-square error (RMSE), and (iv) cross-correlation. A Jacobian is evaluated to estimate the impact of each input (SSH, SST) on the NN chlorophyll-a estimates. The differences between an ensemble of NNs vs a single NN are examined. After the NN is trained for the SeaWiFS period, the NN is then applied and validated for 2005-2015, a period covered by other satellite missions — the Moderate Resolution Imaging Spectroradiometer (MODIS AQUA) and the Visible Imaging Infrared Radiometer Suite (VIIRS).