Measurement of suspended particulate matter concentration (SPMC) spanning large time and geographical scales have become a matter of growing importance in recent decades. At many places worldwide, complex observation platforms have been installed to capture temporal and spatial variability over scales ranging from cm (turbulent regimes) to whole basins. Long-term in situ measurements of SPMC involve one or more optical and acoustical sensors and, as the ground truth reference, gravimetric measurements of filtered water samples. The estimation of SPMC from optical and acoustical proxies generally results from the combination of a number of independent calibration measurements, as well as regression or inverse models. Direct or indirect measurements of SPMC are inherently associated with a number of uncertainties along the whole operation chain, the autonomous field deployment, to the analyses necessary for converting the observed proxy values of optical and acoustical signals to SPMC. Controlling uncertainties will become an important issue when the observational input comprises systems of sensors spanning large spatial and temporal scales. This will be especially relevant for detecting trends in the data with unambiguous statistical significance, separating anthropogenic impact from natural variations, or evaluating numerical models over a broad ensemble of different conditions using validated field data.

The aim of the study is to present and discuss the benefits and limitations of using optical and acoustical backscatter sensors to acquire long-term observations of SPMC. Additionally, this study will formulate recommendations on how to best acquire quality-assured SPMC data sets, based on the challenges and uncertainties associated with those long-term observations. The main sources of error as well as the means to quantify and reduce the uncertainties associated with SPMC measurements are also illustrated.