Retrieval of water quality information from satellite imagery can provide resource managers with an improved understanding into the spatial variability of the water body. In light of the increasing availability of ‘analysis ready data’ (ARD) satellite imagery in open datacubes*, either on cloud-based services or on high performance computing environments, development of operational monitoring systems is becoming feasible. Near-surface sensors can assist in more rapid and widespread algal bloom monitoring at a much higher temporal resolution. Remote sensing imagery, whilst cost effective, may not be optimal in terms of spatial or spectral resolution and can be greatly enhanced with the integration of near-surface observations. We describe pathways to use field-based near-surface sensors to calibrate and validate satellite remote sensing. These methods allow early detection of algal blooms and assist in the early warning for management intervention. We have designed and deployed several low-cost, near-surface sensors at several inland water sites around eastern Australia. The data is transferred using mobile networks where it is processed into spectral information. From this data and coincident field bio-optical measurements, we have developed algorithms for quantitative estimation of blue-green algal-specific pigments (phycocyanin) and chlorophyll concentrations. We have tested these algorithms for detection using a number of existing satellite sensors and report on results here. These methods have applied next-generation monitoring technology and when combined with hydrologic modelling will provide aquatic observations and forecasts. These will lead to improved management preparedness to respond to environmental challenges, e.g., a harmful algal blooms.