Nutrient and Dissolved Oxygen (DO) Estimation Using Remote Sensing Techniques: A Literature Review

Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH3), ammonium (NH4+), ammoniacal nitrogen (AN), nitrite (NO2−), nitrate (NO3−), dissolved inorganic nitrogen (DIN), phosphate (PO43−), dissolved inorganic phosphorus (DIP), silicate (SiO2) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability.