Landsat-8 (L8) and Sentinel-2 (S2) terrestrial satellite missions have shown to contain useful information for aquatic applications. However, quantitative retrieval of water quality parameters, such as turbidity, strongly depend on the performance of atmospheric correction algorithms. Among available processors, ACOLITE (https://odnature.naturalsciences.be/remsem/software-and-data/acolite) is simple to incorporate in imagery processing routines and has shown to have better performance than other processors for sediment-rich waters. Recently (in 2018), it incorporated a new default atmospheric correction approach, the dark spectrum fit (DSF), which remains to be tested in most of the Southern hemisphere coastal areas. In this work, we present new in-situ radiometric measurements collected in the northern coast of the Río de la Plata estuary, South America, during field campaigns along a 2-year period. The data set was used to evaluate the performance of ACOLITE’s DSF and exponential extrapolation (EXP) methods with L8 and S2 imagery, and to recalibrate a turbidity algorithm. The DSF did not perform very well, giving particularly poor results in the near infrared (NIR) bands. However, its performance was greatly improved with an optional sun glint correction (DSF+GC), although some positive bias was still present in the NIR bands. A GC seemed to be most important in dates with higher sun elevation (austral spring and summer), and should be strongly considered for other water bodies in the region and in similar or lower latitudes (35°S). Additionally, the EXP method gave good results in the green-NIR spectral region when a low (5th) percentile aerosol type was selected. Finally, the effect of the atmospheric correction on turbidity retrieval from satellite imagery was assessed: if the red and a NIR band were combined, the effect of the bias in the NIR region was negligible for the DSF+GC method; however, some impact was noticed for the lowest turbidity levels if a single NIR band was used.

Coastal waters have high ecological and economic relevance and are globally threatened by intense human activities leading to eutrophication. The decameter resolution of Sentinel-2 Multispectral Instrument (S2-MSI) provides an advantage to detect spatially heterogeneous phenomena that are limited in extent, such as harmful cyanobacterial blooms (cyanoHABs). Chlorophyll-a is typically used in remote sensing of blooms, however, it remains to be evaluated in several coastal regions of the world. The Río de la Plata estuary (South America) provides a key case study due to its highly variable concentrations of suspended sediments, and the increasing frequency of cyanoHABs. Here we evaluate the potential and limitations of S2-MSI indices to retrieve chlorophyll-a in these optically complex waters, obtaining regional algorithms and comparing them to previously available ones. We propose an approach to follow the evolution of chlorophyll-a thresholds (10 and 24μg/L) that can contribute to monitoring programs and early warning strategies of cyanoHABs.

The observed multi-annual variability of streamflow for three rivers (Paraná River (PR), Uruguay River (UR) and Negro River (NR)) in La Plata Basin (LPB) during the twentieth century is analysed. Several spectral methods (singular spectrum analysis, maximum entropy method and multi-taper method) are applied to annual and seasonal run-off time series in order to capture low frequency variability (LFV) modes and pseudo-periodic patterns. Very robust quasi-periods in the three to six years band are detected for the three rivers, suggesting a strong link with El Niño-Southern Oscillation. Pseudo-cycles of eight to nine years appear in NR and UR. No quasi-periods above 10 years are obtained for any of the rivers. The three rivers exhibit significant LFV components associated with increasing trends that show a clear seasonality. For PR, LFV modes appear between May and December while for UR this occurs during December to April. Both periods cover the whole annual cycle, giving rise to complementary behaviours in the timing of trends of these two rivers. Remarkably, for the three rivers, the most intense increasing trend and the minimum streamflow occur at the same time of the annual cycle. Six global climate indices are selected in order to analyse the relationship between the multi-year streamflow variability modes in LPB and those of global climate: Niño 3.4 (N3.4), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), Southern Annular Mode (SAM) and Global Temperature Trend (GTT). Similar to the rivers, almost all these indices present very robust modes of variability in the 3–4.5 years band. Except for N3.4 and PDO, the indices show significant LFV modes that exhibit a pronounced increase at the common end of their records. The NAO, AMO and SAM seem to behave in a nearly cyclic way with periods longer than 60 years while the GTT increases steadily along the whole register.

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide considerations for future field campaigns to improve coordination with remote sensing data collection and ensure that field data is well suited for use in model or algorithm development.