This study presents an optical water type (OWT) classification approach to characterize the spatial and temporal variability of water quality in Mar Chiquita Lagoon, a large inland RAMSAR site in Argentina. Multispectral (Sentinel-2, Sentinel-3) and hyperspectral (PACE) satellite imagery were used to derive three spectral descriptors: apparent visible wavelength, a normalized difference index, and the trapezoidal area under the RGB bands. These metrics were used to classify each water pixel into predefined OWTs based on spectral similarity. The results highlight consistent spatial patterns across sensors, with the central and southern lagoon dominated by moderately turbid, particle-rich waters (class 5a), and more variable classes (4a, 4b, 5b) concentrated near northern tributary inflows. Temporal analysis using PACE revealed seasonal shifts in OWTs, with class 4b becoming dominant during the spring–summer period. This multi-sensor framework demonstrates the effectiveness of spectral classification for monitoring optically complex inland waters in the absence of in situ data. The findings underscore the value of satellite-based remote sensing for tracking water quality and delineating the spatial influence of tributaries across temporal and spectral dimensions.