Droughts are a serious threat to the planet. Especially in agriculture, the consequences are devastating leading to reduced yields or crop failure with further implications for economics, politics and society. Being able to monitor and predict agricultural droughts is an important step to reduce agricultural vulnerability and secure livelihoods. However, few drought indicators measure the actual crop response towards the hydrological conditions. Remote sensing bears great potential for this challenge due to its worldwide continuous coverage and relatively low costs. Vegetation Optical Depth (VOD), a remote sensing method, is a measure of above-ground vegetation water content based on passive microwaves, which are not affected by atmospheric distortion. The present research aims at analysing the potential of VOD as an agricultural drought indicator. A case study on pepper fields in Indonesia was conducted using daily VOD data between 2012 and 2018 at a downscaled spatial resolution of 100 x 100 m. Correlations between VOD anomalies and anomalies of other meteorological drought indicators were calculated. VOD was found to be correlated with the Standardized Precipitation Evaporation Index (SPEI) by 0.47, with the El Niño Southern Oscillation (ENSO) by -0.46 and with the Indian Ocean Dipole (IOD) by -0.46. These correlations indicate that other factors influence the VOD, which should be considered as an independent dataset. This study provides evidence that VOD is capable of capturing agricultural droughts. A cross-comparison was conducted with the Normalized Difference Vegetation Index (NDVI), which is often compared to VOD as an alternative measure. Results in the investigated area suggest that, although VOD has a lower spatial resolution, it performs better than NDVI. Finally, a roadmap was proposed towards developing a drought indicator based on the VOD anomalies. This roadmap aims at modelling future VOD based on the relation between SPEI and VOD by using the added value of VOD that this study reveals. This study is especially relevant for other tropical areas with a high cloud density.

While rainfall is the key input to most hydrological models, its precise characteristics are often uncertain. Runoff generation does not only depend on the measured rainfall resolution but also on the level of detail of land-use and therefore of the runoff generation. This study aims at identifying the influence of rainfall radar resolution and land-use data on the urban water balance in Rotterdam. Results show that the water balance in this study does not close properly, as more volume enters than leaves the system. This is most probably because infiltration is neglected and the reliability of the pumping data is uncertain. Furthermore, the Combined Sewer Overflow (CSO) volumes are overestimated which might be caused by the high uncertainty of the weir parameters and of the water levels at the non-monitored CSO weirs. This error multiplies with an increase in sewer district size, a higher amount of unmonitored CSOs and lower weir levels. When comparing the different resolutions, the water balance degrades remarkably with coarser land-use data detail and improves slightly with higher rainfall radar resolution, until reaching a certain threshold where the error is minimized. After this threshold the water balance closes less again. Possibly, the reduction in noise and in sensitivity to shifts in timing and location of the radar data with coarsening rainfall radar resolutions is responsible for these unexpected results. Furthermore, this study suggests that there might be a relationship between the changes in land-use resolution and the changes in rainfall radar resolution.