Executive Summary This report uses a series of five case studies to demonstrate the added-value of in situ measurements and how these can be used to validate and improve the quality and utility of satellite-derived water quality data products. Five case-studies were undertaken within the context of the H2020 MONOCLE project or used instruments developed under MONOCLE and focus on the following topics: Combining water and air radiometry to improve the accuracy and stability of in situ reference observations Using mobile radiometers on ships to characterise spatial variability in reflectance to better understand satellite validation uncertainties Micro-scale observations from drone platforms used to validate satellite products and inform spatial sampling strategies Determining key atmospheric conditions that lead to uncertain water colour observations Adjusting the choice of atmospheric correction models guided by in situ radiometry Each topic is discussed in detail in the following chapters. Our key findings are: Simultaneous deployment of hyperspectral radiometers collecting water (So-Rad) and atmospheric radiance (HSP) improves the precision of remote sensing reflectance measurements for satellite validation. Coincident in situ measurements from the Hyperspectral Pyranometer (HSP) and the Solar-Tracking Radiometry Platform (So-Rad) were used to assess whether observed direct and diffuse atmospheric irradiance properties could improve estimates of water-leaving reflectance. Direct measurements replaced model-optimized terms in the 3C (three-glint component)water-leaving reflectance algorithm. The stability of reflectance, over 20-min observation periods, improved by up to 50 % in the blue part of the spectrum, when combining the HSP and So-Rad systems. These results support that the HSP sensor can fulfil a dual role in aquatic ecosystem monitoring by improving precision inRrs(λ)from autonomous spectroradiometer platforms such as So-Rad, alongside its primary function to characterize aerosols. The scientific results from this section have recently been published in Jordan et al. (2022). Mobile radiometers characterising spatial variability in reflectance provide insight into satellite validation uncertainties We investigated spatial scales of in situ remote-sensing Reflectance (Rrs(λ)) variability at Lake Balaton in 2019, using the So-Rad spectroradiometer platform mounted on a car ferry repeatedly crossing 1 km of an optically and hydrologically dynamic part of the lake. Approximately a third and up to half of the variability in Rrs(λ) within the scope of a 300-m satellite pixel was due to the spatial separation of the Rrs(λ) measurements, i.e. the optical gradients observed between shores. If we had measured from used a fixed position instead of the shipborne instrument, this fraction of the variability would be attributed to random error between in situ and satellite observation. At scales up to around 300 m, in situ Rrs(λ) were spatially auto-correlated (observation within this vicinity tended to look similar). Therefore, in situ Rrs(λ) in neighbouring 300-m satellite pixels were sufficiently separated to be treated as spatially independent. Satellite sensors with finer spatial resolution could take this behaviour into account by adjusting the number of independent satellite vs in situ matchups, to represent the number of spatially independent measurements rather than using all available pixels. Drone-based reflectance measurements complement validation of satellite products in the micro-scale By including drones equipped with imaging sensors, we find that using standardized procedures, drones are complementary to water quality monitoring. These procedures include flight protocols and an automated way of data processing. Drones particularly fill data gaps left by satellites, as they observe at a higher temporal frequency (e.g. hourly basis for intertidal monitoring) and acquire higher spatial resolution data. When collecting in situ measurements for validation and/or calibration of algorithms and EO products, drone data can help to understand fine-scale dynamics. This complementary information benefits monitoring the impacts of inshore and offshore operations e.g. dredging or construction, or the monitoring of inland water bodies including for water utilities. Determining key atmospheric conditions that lead to uncertain water colour observations, a comparative analysis of atmospheric correction approaches The effects of the atmosphere are a major source of uncertainty when estimating optical water quality from satellites. Many algorithms have been developed to remove atmospheric effects from satellite data but their efficacy is known to vary widely so choosing an appropriate algorithm is important. This study uses data from the So-Rad and HSP instruments deployed on vessels in six different regions, to investigate how well a range of atmospheric correction algorithms work in different settings and explore the reasons behind these differences. We find that algorithm performance varied widely within and between regions with no clear best algorithm for all settings. Initial investigations into drivers of algorithm performance suggest that for some algorithms the optical thickness of the atmosphere is significant. Atmospheric correction model selection can be based on in situ reflectance in highly dynamic areas We demonstrate how a fixed positioned radiometry system (WISPstation) measuring water reflectance spectra Rrs(λ) can be used to compare atmospheric correction models for Sentinel-3 OLCI data enabling the identification and optimisation of the best-performing model. In situ measurements were compared to OLCI results after atmospheric correction with the models C2RCC, C2X and Polymer. The comparison showed that all models underestimate water reflectance. Two of the models had problems producing the correct spectral shape between 620 and 709 nm. Since chlorophyll-a is calculated using a band centred around 665 nm, this would seriously affect the operational product. Therefore, the best performing atmospheric correction model (C2RCC) was further adjusted by scaling coefficients per spectral band as derived from the in situ data. Atmospheric correction models are continuously evolving, so continuous validation using in-situ reflectance data is important to ensure best quality of operational turbidity and chlorophyll-a products. Our firm recommendation is to implement a combination of observational approaches. This should fill the immediate data gap which exists for hyperspectral water-leaving reflectance in coastal waters, lakes and estuaries. A global network of sensors to consistently ground-truth satellite observation approaches of 100 inland water sites may already suffice to operationally determine the quality of atmospherically corrected satellite products, to guide appropriate use (application-specific) and thereby build trust in derived biogeochemical products. Highly automated data flows are now available to make these efforts sustainable. We recommend that water and atmospheric radiance signals are collected simultaneously wherever feasible (using HSP1, So-Rad, WISPstation systems). In the short term, this can aid selection of the most appropriate atmospheric correction model, for which no single ideal solution currently exists. In the longer term, this will support R&D into better performing atmospheric correction techniques or better a priori predictions of data quality. For strategic site-specific monitoring including environmental or industrial activities, drones provide a highly versatile method to map out problem areas. When collecting radiometric data from these platforms, operators need to be properly guided to record observations under suitable viewing geometry and light conditions. Following this, handling the large volumes of imagery collected from drone-mounted sensors benefits from established data flows such as those featured here in the MapEO water service.