This review paper adopts bibliometric and meta-analysis approaches to explore the
application of supervised machine learning regression models in satellite-based water …

Remote sensing methods have the potential to improve lake water quality monitoring and
decision-making in water management. This review discusses the use of remote sensing …

Numerous algorithms have been developed to retrieve chlorophyll-a (Chla) concentrations
(mg m− 3) from Earth observation (EO) data collected over optically complex waters …

Much effort has been applied in estimating the concentrations of chlorophyll-a (Chl a) in
lakes. The optical complexity and lack of in situ data complicate estimating Chl a in such …

The remote sensing of algal pigments is essential for understanding the temporal and
spatial distribution of harmful algal blooms (HABs). In particular, the vertical distribution of …

The decrease in Arctic sea-ice extent and thickness as a result of global warming will impact
the timing, duration, magnitude and composition of phytoplankton production with cascading …

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be
directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI) …

Abstract Dissolved Organic Carbon (DOC) in inland waters plays an essential role in the
global carbon cycle and has significant public health effects. Machine learning (ML) together …

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on
Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the …

Modeling inland water quality by remote sensing has already demonstrated its capacity to
make accurate predictions. However, limitations still exist for applicability in diverse regions …