No abstract

Inland waters' eutrophication is considered a severe global ecological issue. It is associated with rapid phytoplankton and other microorganisms' production. The use of satellite remote sensing has been proposed to determine the primary productivity (PP) of inland waters through the chlorophyll-a (Chl-a) retrieval. However, applying this technique to African inland waters has not made significant progress. In this study, we developed techniques to assess inland waters' PP based on Sentinel-2 Multi-Spectral Instrument (MSI) imagery. The main objective was to conduct monthly trophic state assessments in Lake Malombe from March to October 2019. The field-based survey indicated a strong correlation with the Sentinel-2 MSI, suggesting the model's novelty in predicting PP. The validation results between the measured in situ Chl-a concentrations and the MSI-based Chl-a had a root mean square error and R squared of 2.88 and 0.54, indicating the capability of the NDCI in predicting PP in Lake Malombe. The scatter plot showed that almost all the points were in the 95% confidence interval, suggesting that the coefficients proposed in this paper could be used on NDCI images derived from any of the sensors to predict Lake Malombe PP accurately. The double-logistic function model was well fitted to capture the annual pattern of the variable. The derived end of the season (EOS), the start of the season (SOS), and amplitude were 98, 250, and 7.0, respectively. The EOS almost coincided with the end of a rainy season, identified from the time-series of precipitation data. The SOS did not align with the start of the rainy season; i.e., phytoplankton started to grow before precipitation, suggesting that precipitation probably did not influence phytoplankton growth. This study suggests that integrating remote sensing with in situ data can provide an understanding of ecological dynamics and offers better options for monitoring lake eutrophication.