A constellation of small satellites has rapidly emerged in recent years, and PlanetScope is a promising data source for the advanced generation of Earth Observation products, including high-resolution water quality retrieval applications. Planet’s SuperDove (PSD) provides eight spectral bands in the visible and near-infrared wavelengths with 3-meter spatial resolution and near-daily temporal resolution. However, no studies have thoroughly investigated the consistency of aquatic reflectance derived from these sensors over multiple inland aquatic systems. In this study, we conducted, for the first time, a comprehensive validation of PSD images over Brazilian fresh and coastal waters using a large dataset of remote sensing reflectance (Rrs) spectra (n = 480) collected near-concomitantly with PSD’s images (±3 h). This radiometric dataset contains a significant spectral variability with different Optical Water Types (OWTs) and was used to assess the radiometric accuracy (i.e., aquatic reflectance) of PSD data using two different atmospheric correction methods (ACOLITE and 6SV-PSR). Furthermore, we compared a Chlorophyll-a (Chl-a) time-series derived from Sentinel-2/MSI and PlanetScope/PSD for consistency analysis in a eutrophic reservoir in Brazil. The results demonstrated that blue (490 nm), green I (531 nm), and green II (565 nm) bands achieved the best performance (median symmetric accuracy errors lower than 33.17 %) considering both atmospheric correction methods. The yellow (610 nm), red (665 nm), and red-edge (705 nm) bands presented errors lower than 46 % when compared to in-situ spectra. NIR and coastal blue bands exhibited lower accuracy, with errors higher than 89 %. Regarding OWTs, we observed that the best results were accounted for OWT-1 (clear water) using 6SV-PSR and OWT-7 (high turbid water) using ACOLITE, considering all bands. Errors lower than 30 % were observed for OWT-1 (ACOLITE), OWT-3 (6SV-PSR), OWT-6 (ACOLITE and 6SV-PSR), and OWT-7 (ACOLITE). The Chl-a time series demonstrates good consistency between PSD and Sentinel-2/MSI (with an error of 21.98 %). This result highlights the advantage of higher temporal resolution in PSD for detecting algal blooms, although PSD overestimates Chl-a for higher concentrations. This research suggests that SuperDove has the potential to provide reliable Rrs estimations across various aquatic ecosystems, facilitating the retrieval of key parameters such as chlorophyll-a, c-phycocyanin, and suspended sediments. However, further work is required to address important processing challenges, including glint and adjacency corrections, to improve the temporal consistency and accuracy in aquatic monitoring. Additionally, a broader global-scale evaluation is necessary to fully understand the performance and limitations of these sensors for aquatic science and applications.