Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

Inter-comparison of data products from simultaneous measurements performed with independent systems and methods is a viable approach to assess the consistency of data and additionally to investigate uncertainties. Within such a context the inter-comparison called Assessment of In Situ Radiometric Capabilities for Coastal Water Remote Sensing Applications (ARC) was carried out at the Acqua Alta Oceanographic Tower in the northern Adriatic Sea to explore the accuracy of in situ data products from various in- and above-water optical systems and methods. Measurements were performed under almost ideal conditions, including a stable deployment platform, clear sky, relatively low sun zenith angles and moderately low sea state. Additionally, all optical sensors involved in the experiment were inter-calibrated through absolute radiometric calibration performed with the same standards and methods. Inter-compared data products include spectral water-leaving radiance L_w (λ), above-water downward irradiance E_d(0⁺,λ) and remote sensing reflectance R_rs(λ). Data products from the various measurement systems/methods were directly compared to those from a single reference system/method. Results for R_rs(λ) indicate spectrally averaged values of relative differences comprised between −1 and +6%, while spectrally averaged values of absolute differences vary from approximately 6% for the above-water systems/methods to 9% for buoy-based systems/methods. The agreement between R_rs(λ) spectral relative differences and estimates of combined uncertainties of the inter-compared systems/methods is noteworthy.