Chlorophyll-a Detection in Riverine and Transitional Waters Using UAS Multispectral Imagery: A Systematic Review

River systems and their transitional zones near estuaries are characterized by strong spatial and temporal variability in both hydro-chemical and optical conditions. These dynamics make the monitoring of key water quality indicators such as chlorophyll-a (Chl-a) particularly demanding. Unmanned aerial systems (UASs) equipped with multispectral sensors have increasingly been used to address these challenges, providing high spatial resolution observations in environments where satellite imagery is often constrained by narrow channel widths and complex optical conditions. This systematic review examines the use of multispectral sensors for the detection, estimation, and mapping of chlorophyll-a in riverine, estuarine and transitional environments. Following the PRISMA 2020 framework, sixteen peer-reviewed studies published between 2016 and 2025 were identified and analyzed, focusing on the observation platforms employed, spectral band configurations, radiometric processing procedures, and the modeling approaches used to retrieve chlorophyll-a concentrations. Across the reviewed literature, most applications rely on empirical spectral indices based on red, red-edge, and near-infrared wavelengths, usually calibrated with concurrent in situ measurements. Machine learning methods appear mainly in more recent publications, yet their performance remains strongly tied to site-specific calibration datasets. Notable differences in radiometric correction workflows, validation protocols, and documentation of results complicate direct comparison among studies. This review highlights the strong potential of UAS multispectral observations for resolving small-scale spatial patterns of chlorophyll-a in dynamic river systems, while underscoring the need for greater methodological consistency in future research.