## 6 Conclusions  
RapidEye multispectral sensors were primarily designed for land applications, however, in this study they are used satisfactorily for bathymetric mapping in a representative coastal embayment. This study presents a promising statistical methodology for predicting coastal bathymetry from the water-leaving radiance signal and field calibration data (_i.e._, water depth measurements and seabed classification). The results confirm that RapidEye datasets are well suited to infer depths up to 12 m using spatial prediction models calibrated with a limited number of water depth measurements (50 depth measurements /km 3. The best model's prediction accuracy, within a range of \(\pm\)1 m, is 95.0%.  
Our assessment reveals that the four spatial models of this study show better adjustments in the basic non-spatial model in the predictions. Prediction accuracy results indicate that a kriging with an external drift model using local kriging neighbourhoods is clearly the best predictor, stressing the importance of catering for both spatial autocorrelation and non-stationary relationships. For this predictor, the correlation coefficient between the actual and predicted water depth data is very strong at 0.985.  
In coastal areas, ship-based multibeam or LiDAR bathymetry surveys attaining 100% coverage are sometimes unavailable or prohibitively expensive, while the dynamic nature of the coast makes precision and repeatability a challenge. Spatial prediction models can provide reasonable and error controlled water depth predictions in an inexpensive and efficient manner from a relatively low number of groundtruthing points.The prediction errors presented in this paper are too large to be used in high accuracy products such as navigational charts; however, with the right quality controls applied, they can be applied in a range of important fields from environmental monitoring to seabed mapping and coastal management.  
Research presented in this paper is supported by: (a) a Strategic Research Cluster grant (07/SRC/I1168)--StratAG, awarded to the National Centre of Geocomputation by Science Foundation Ireland under the National Development Plan; and (b) the North Wyke Farm Platform supported by the Biotechnology and Biological Sciences Research Council of the UK (BBSRC BB/J004308). The authors wish to thank the INFORMAR program, the Irish Marine Institute and the Geological Survey of Ireland for the datasets provided. RapidEye datasets were provided by the European Space Agency under Blackbridge Ltd copyright. This publication has emanated in part from research conducted with the financial support of Science Foundation Ireland under Grant \"SFI 13/IF/I2778\". We acknowledge the constructive input provided during review of this manuscript by three anonymous reviewers.  
[PERSON], [PERSON] and [PERSON] conceived the research outline. [PERSON] and [PERSON] coordinated the image and bathymetry acquisition and analysis procedure for the study site. [PERSON] carried out the spatial statistical analysis. [PERSON], [PERSON] and [PERSON] analysed the data and interpreted the results. All the co-authors contributed equally to writing the paper.  
The authors declare no conflict of interest.