Representative spatial patterns of eutrophication variables cannot be produced using traditional in situ sampling techniques. Spatial heterogeneity complicates the study of seasonal and long-term trends and the evaluation of water management policies. Remote sensing, however, with its broad view has the potential to deliver the relevant information. This paper will address the added value of synoptic eutrophication maps to the standard monitoring program of two large, spatially and temporally variable lakes in the Netherlands, Lakes IJssel and Marken. Remote sensing images were obtained from SeaWiFS; and combined with hyperspectral reflectance data from the airborne EPS-a sensor and the shipboard PR-650 spectroradiometer. The PR-650 data were used in selecting the most appropriate algorithms for ScaWiFS and EPS-a. A special algorithm for case 11 waters with high chlorophyll content was applied to SeaWiFS data to obtain chlorophyll concentrations. Synoptic maps of suspended matter were retrieved using inversion of a model for irradiance reflectance. For the airborne sensor inversion of reflectance was used for both suspended matter and chlorophyll. Satellite and airborne sensors clearly are complementary to each other. Comparison of satellite data with the airborne data and the (scarcely available) in situ data reveal underlying problems with: (i) validation of remote sensing images; and (ii) comparing data at different spatial and temporal scales. In our study, we found a reasonable agreement between different data sources at seasonal time scales, but at shorter time scales the differences can be (much) larger. In situ data suffer from poor reproducibility, related to the natural variability at small spatial scales (patchiness), combined with a significant temporal variability. The standard in situ monitoring program in Lakes IJssel and Marken lacks both the necessary spatial coverage as well as an appropriate sampling frequency. This indicates that for reliable monitoring, a synoptic data set, sampled at a high frequency is required. Remote sensing can partially fulfil this demand but still lacks the demanded frequency, mainly due to regular cloud cover. The answer may be in a multiplatform monitoring approach, as used in our study (combining in situ data with shipboard, airborne and satellite optical data) and in combining monitoring data with models. Satellite remote sensing is most powerful in determining properties that are inherent to the whole take system, like the overall mean chlorophyll-a concentration. Computational models may meet the demand for a sufficiently high sampling frequency by deterministic interpolation of the data in time. (C) 2003 Elsevier Science B.V. All rights reserved.