Estimates of the global ocean surface dissolved oxygen and macronutrients from satellite data

Abstract

Marine ecosystems are complex and dynamic in nature and influenced by various environmental factors such as temperature, salinity, ocean currents, nutrient availability, light penetration, and anthropogenic activities. Macronutrients (nitrate, phosphate, and silicate) and dissolved oxygen (DO) are crucial properties for determining the health, function, and dynamics of marine ecosystems. There are known limitations with the in-situ measurements that emphasize the importance of satellite-based models for estimating these properties on the required space and time scales. In this study, we present a number of robust Gaussian Process Regression (GPR) models comprising of 16 DO models and 24 macronutrients models for estimating the concentrations of global-scale ocean surface DO and macronutrients. These models were rigorously trained and tested using the large in-situ datasets. Model performance was assessed using independent in-situ data and it was found that the proposed models yielded high accuracies (Root Mean Square Difference (RMSD) in μmol kg⁻¹, Mean Absolute Difference (MAD) in μmol kg⁻¹, and coefficient of determination (R²)): DO: 8.276, 3.802, and 0.984; Nitrate: 0.827, 0.329, and 0.987; Phosphate: 0.068, 0.034, and 0.983; and Silicate: 1.921, 0.757, and 0.982. The optimal input parameters and kernel combinations for GPR models were identified as (i) sea surface temperature (SST), sea surface salinity (SSS), and latitude/longitude for DO, and (ii) SST, SSS, DO, and latitude/longitude for macronutrients. The satellite estimates based on the exponential kernel functions showed good agreement with in-situ data (RMSD, MAD, R², Slope, and Intercept: 9.794, 4.850, 0.948, 0.986, and 4.206 for the DO products, 1.711, 0.652, 0.824, 0.884, and 0.249 for the nitrate products, 0.127, 0.064, 0.805, 0.869, and 0.033 for the phosphate products, and 2.809, 1.067, 0.533, 0.622, and 1.117 for the silicate products). Further tests on World Ocean Atlas (WOA) 2018 SST and SSS data yielded similar results for the DO and macronutrients contents. To realize the importance of this study, we investigated the early and substantial spring bloom occurrences in the Gulf of Alaska in response to the DO and macronutrients contents as well as the monthly and interannual variations and anomalies of SST, SSS, DO, nitrate, phosphate, and silicate caused by the Pacific Decadal Oscillation (PDO) in the California Current System (CCS) and Oceanic Niño Index (ONI) in the Niño-3.4 region using climatological data (2002−2023). The proposed models will have important implications for remote sensing of regional and global biogeochemical properties and marine ecosystem dynamics.