Certificate course in

HYDROSAT: Observing the Water Cycle from Space

The lack of near-real time hydrological data constrains the understanding of hydrological and ecological processes and their interaction with natural and anthropogenic forcings. The main objective of this course is to educate hydrologists to work with the state of the art satellite optical and microwave remote sensing algorithms for quantifying the hydrological cycle components including water quality.

For whom is the course relevant?

The course is relevant to scientists or professionals who desire to extend their knowledge on deriving hydrological state variables from remote sensing data. The course therefore covers wide scientific backgrounds:  hydrology and/or water engineering (hydrodynamic, hydrobiology, fluid mechanics, atmospheric physics, soil physics, ground water, surface hydrology, oceanography, marine optics, water quality).

What will be achieved?

• Students will obtain a broader perspective of remote sensing applications to hydrology.
• The course provides in-depth knowledge on remote sensing methods for the quantification of hydrological state variables.

What is the course content?

The course consists of three weeks; the first week focuses on remote sensing of surface energy balance and water quality. The second week will focus on deriving soil moisture and precipitation from microwave and radar measurements. The last week is reserved to detecting changes in terrestrial water storage from gravity measurements and finalizing the end-module project. The course will also involve introducing programming skills so the students can read and process satellite data themselves.

Week 1
During the 1st half of this week the students will be introduced to the principles of land and atmosphere interaction. The emphasis here will lie on the estimation of surface heat flux with evapotranspiration as the key variable and the Surface Energy Balance System (SEBS) as the main tool and using a programming language.
During the second half of the week the students will be introduced to remote sensing inversion algorithms to estimate water surface parameters. The emphasis of this part will rely on radiative transfer modelling to estimate water quality variables. First, the students will be busy with the principles of radiative transfer of water body, with emphasis on the QAA radiative transfer model. After the lectures the students will be simulating emitted radiation over water bodies and estimating water quality variables from remote sensing imagery.

Week 2
During the 1st half of this week, the Tau Omega model will be introduced to derive soil moisture from microwave data. Here we will work with microwave data. The students will work on reading these data and processing them programmatically to soil moisture and land surface temperature products.
An introduction to precipitation and field based radar will be followed in the second half of week 2. Rain intensity will be estimated from radar data by calibrating and validating a semi analytical model using gauged data. A comparison with earth observation products will then be performed focusing on the spatial variation of both estimates.

Week 3
Monthly gravity field estimates are made by the twin Gravity Recovery and Climate Experiment (GRACE) satellites. Gravity estimates are obtained as variations relative to a priori gravity model. Consequently, the GRACE gravity estimates can be largely attributed to surface water and ground water changes (terrestrial water storage). The first two days of the third week will provide understanding to GRACE data, and working experience with GRACE products of water equivalent thickness. In addition the Global Land Data Assimilation System will be introduced.

PROJECT: the last 3 days of week 3 are reserved for the realization of the end-module project. Global Land Data Assimilation System (GLDAS) provide important information on the hydrological cycle components. In this project you will:

1. Compare earth observation soil moisture values (obtained from SSM/I) with GLDAS product and perform CDF matching to correct for the bias between modeled and observed values.
2. Compare earth observation precipitation values with groundbasedground based product and perform CDF matching to correct for the bias. 
3. Use GRACE data with GLDAS, and earth observation products to derive the variation in ground water storage. 
4. Study this variation with time using standardized anomalies and trend analysis.

Why choose this course?

This course is a must for each hydrologist who desire to build up her/his knowledge on deriving hydrological state variables from satellite data. The course will provide a deep understanding on how remote sensing retrieval models are built and work and covers all types of sensing technologies used in hydrology: visible, microwave-active microwave-passive and gravity. So it also forms an introduction to hydrologists on the capabilities of the state-of-the-art remote sensing technologies.

Admission requirements

Academic level and background

Applicants for the Certificate programme should have a Bachelor degree or equivalent from a recognised university in a discipline related to the course, preferably combined with working experience in a relevant field.

Some courses in the Certificate programme or separate modules require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.

Skills in taught or related subjects are a prerequisite for some courses in the Certificate programme or separate modules. Even if the applicant satisfies the overall admission requirements, acceptance is not automatic.

English language

As all courses are given in English, proficiency in the English language is a prerequisite. If you are a national of one of the countries in this list (PDF), you are exempted from an English language test. Please note: the requirements when applying for fellowships may vary according to the regulations of the fellowship provider.

English language tests: minimum requirements

Only internationally recognised test results are accepted.

Computer skills

Applicants lacking computer experience are strongly advised to follow basic courses in their home countries.