Description

Synthetic Aperture Radar (SAR) imagery holds the promise of continuous monitoring at field scale. C-band Sentinel-1 SAR is sensitive to soil moisture, vegetation and other environmental factors, and has the potential to be used in agricultural management for the scheduling of farming practices, such as plowing, planting, harvesting and irrigation. SAR data is, however, very noisy, which makes it rather difficult to use deterministic models to extract information. Therefore, the student choosing this M.Sc. research topic is challenged to adopt a stochastic approach and investigate whether a time series analysis techniques can assist us in extracting drought information from Sentinel-1 SAR data.
Since 2009, the Water Resources department has an in-situ measurements programme that targets the characterization of soil moisture at regional and field scale, which includes a network of twenty soil moisture monitoring stations in the Twente region. The M.Sc. candidate will use these data sets to validate/analyse the results from the time series analysis techniques applied to the Sentinel-1 data.

Objectives and Methodology

The main objective of this research is apply time series analysis techniques (e.g. moving average, ARIMA models, machine learning) to Sentinel-1 SAR for the analysis of the 2018, 2019 and 2020 droughts.

Further reading

van der Velde, R., Colliander, A., Pezij, M., Benninga, H-J. F., Bindlish, R., Chan, S. K., Jackson, T. J., Hendriks, D. M. D., Augustijn, D. C. M., & Su, Z. (2019). Validation of SMAP L2 passive-only soil moisture products using in situ measurements collected in Twente, The Netherlands. Hydrology and earth system sciences discussions. https://doi.org/10.5194/hess-2019-471.
Benninga, H. F., Carranza, C. D. U., Pezij, M., van Santen, P., van der Ploeg, M. J., Augustijn, D. C. M., & van der Velde, R. (2018). The Raam regional soil moisture monitoring network in the Netherlands. Earth system science data, 10(1), 61-79. https://doi.org/10.5194/essd-10-61-2018