Dikes hold back water and protect the land behind it from flooding. Due to rising sea levels, land subsidence and more extreme weather patterns, the function of dikes become increasingly important. To ensure dike safety, dikes are regularly inspected. With about 22,500 kilometers of dikes in the Netherlands, this is a very slow, costly and time consuming process. Remote sensing could contribute to dike inspections as it can screen large areas in a short time period and more continuously monitor inspection parameters. Several studies have already assessed the use of remote sensing for different inspections parameters such as deformation, grass cover quality and seepage detection. An important parameter that affects dike stability is soil moisture, as effective stress and shear strength are directly related to soil moisture content. Intense periods of drought can lead to low soil moisture values which consequently decreases dike stability. On the other hand, excessive soil moisture can lead to excess pore water pressure and to a decrease in shear strength. Remote sensing would be an ideal way to monitor soil moisture within grass-covered dikes on large scale. In this study, it was assessed if remote sensing data can give a proxy for soil moisture for grass-covered dikes. This was investigated by using open- access optical and SAR remote sensing data, as this would be an ideal data source since it is freely available. Remote sensing data was obtained from satellite missions Landsat 7/8 and Sentinel 1/2. The majority of the research was conducted for two grass-covered regional dikes. First of all, it was assessed if a lagged relationship could be found between the average soil moisture value of a pixel, extracted from in-situ soil moisture sensors at 20 cm depth, and retrieved vegetation indices (GRR, MSR, NDVI, RVI and NDII) of a pixel. Pearson correlation coefficients were calculated for the harmonized Landsat 7 and 8 data set as the number of data from the single satellite missions was limited. Results show that (1) at lo- cation Bermweg a weak correlation was found (R=0.32-0.40) for the MSR, NDVI, RVI and NDII when the optimal lag of around 30 days was applied. A negligible correlation was found for the GRR (R=0.19); (2) at location Geer- weg, for one pixel, a negligible (R=0.12-0.16) correlation was found for all vegetation indices, except the NDII, when the optimal lag of 23 days was taken into account. A negative correlation was found for the other pixel. For the NDII a negligible correlation (R=0.13-0.28) was found for the two pixels when the optimal lag of 31 day was applied. The grass-cover at location Bermweg was maintained by grazing whereas at location Geerweg the grass was maintained by both grazing and mowing. Secondly, it was investigated if a (lagged) relationship could be found between SAR backscatter and in-situ soil moisture measurements at 20 cm depth. An increase in soil moisture results in an increase in backscatter. Since SAR measures only the top few centimeters of the soil, a lag was taken into account. In addition, it is known that there is a lagged correlation between root-zone soil moisture and LAI, which is also sensitive to backscatter (Jamalinia et al., 2019). A Pearson correlation analysis was performed to assess if there was a (lagged) relationship between soil moisture and retrieved backscatter. Only negative and negligible positive correlations were found, showing that SAR backscatter cannot give a proxy for soil moisture, as a positive correlation was expected. Lastly, a relationship was examined between cumulative precipitation deficit, which can give a proxy for soil moisture, and vegetation indices. The sample size of Landsat 8 and the harmonized Landsat 7 and 8 data set were large enough to demonstrate statistical significance (N > 31). Results show that (1) at location Bermweg the optimal correlation was found for both data sets when a cumulative precipitation deficit period of around 15 days was taken into account. The correlation was negligible (R=0.24-0.36) for the harmonized Landsat 7 and 8 data set (statistically significant, with the exception of the NDVI and NDII) and moderate (statistically significant) for Landsat 8 (R=0.38-0.56); (2) at location Geerweg the cumulative period resulting in the optimal correlation was different for each satellite mission. A statistically significant weak correlation (R=0.47-0.57) was found for the harmonized Landsat 7 and 8 data set when a cumulative period of 20 days was taken into account. For Landsat 8 a statistically significant moderate correlation (R=0.49-0.62) was found for a cumulative period of 90 days. The overall pattern of the calculated correlation coefficients, when different cumulative periods were taken into account, vary largely for each satellite mission. All in all, no universal relationship could be found. The study has shown that vegetation indices and SAR backscatter cannot give an indication of soil moisture within dikes. No strong relationship was found between soil moisture and vegetation indices which can be assigned to noise introduced by various factors like management practices (i.e. mowing, grazing), other key fac- tors influencing vegetation state (i.e. nutrient availability, radiation), low spatial resolution, and scene-to-scene variability. These factors also influence the vegetation index and overrule the true soil moisture conditions. Moreover, the results show that cloud contamination hinders the use of optical remote sensing data for dike inspections as satellite imagery might not be available for extended periods of time, disabling to gain insight into the dynamics of vegetation indices over time. The insensitivity of SAR backscatter to soil moisture can be assigned to the fact that several parameters (i.e. surface roughness, vegetation, dike slope) can affect backscatter as much, or more than, soil moisture. Furthermore, the backscatter signal was extracted from a relatively small area and thus contains a large amount of noise. This also explains why backscatter was unable to give an indirect proxy of soil moisture by estimating the LAI.