PhD position– Remote Sensing of soil moisture dynamics (f/m/x) Helmholtz-Zentrum für Umweltforschung

PhD position– Remote Sensing of soil moisture dynamics (f/m/x) Helmholtz-Zentrum für Umweltforschung - UFZ

https://recruitingapp-5128.de.umantis.com/Vacancies/2020/Description/2

The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

The newly established Department of Remote Sensing in UFZ in tandem with the Remote Sensing Centre for Earth System Research (RSC4Earth) - a joint initiative of UFZ and the Faculty of Physics and Earth Sciences at Leipzig University - conducts innovative research to advance the understanding of the Earth system via the integration of various remote sensing, data science, and process-oriented modelling techniques. It has extensive research experience in quantifying land surface dynamics from multi-source Earth observations across scales.

Within the PhD framework "MoDEV - Towards novel model-data fusion for understanding environmental variability in space and time from high-resolution remote sensing" we are seeking to appoint a highly motivated candidate for the PhD project "Remote Sensing of soil moisture dynamics".

PhD position– Remote Sensing of soil moisture dynamics (f/m/x)

Working time 65% (25.35 hours per week), limited for 3 years

Your tasks:

The PhD project aims to synergize multi-source remote sensing observations to retrieve surface soil moisture dynamics with quantified uncertainties across scales. Robust methods will be developed to estimate root zone soil moisture via the integration of the retrieved surface soil moisture, land surface model and Cosmic-Ray Neutron Sensing (CRNS). Key research questions include:

Will the synergy of multi-source remote sensing data lead to a more accurate soil moisture estimate?

How can we tackle the scale mismatch in comparison of soil moisture from remote sensing, CRNS, and land surface modelling?

How to best integrate satellite surface soil moisture, model simulation, and filed soil moisture to obtain root zone soil moisture dynamics?

Your profile:

Master degree (or equivalent) in earth system science, remote sensing, meteorology, hydrology, physical geography, environmental sciences, physics, statistics, computer sciences, or a related field

Good programming skills (e.g., Python, Fortran, R, or Matlab)

Interest in microwave remote sensing and radiative transfer modelling

Willing to publish results in peer-reviewed journals and present at scientific meetings

Good communication skills in English, and strong interest to work in an interdisciplinary research team

We offer:

Excellent technical facilities which are without parallel

The freedom you need to bridge the difficult gap between basic research and close to being ready for application

Work in interdisciplinary, multinational teams

Excellent links with national and international research networks

Excellent support and optimal subject-specific and general training with our HIGRADE graduate school

Remuneration in accordance with the TVöD public-sector pay grade 13 (65%)

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl...

Landsat band composition

Short-Wave Infrared (7, 6 4) The short-wave infrared band combination uses SWIR-2 (7), SWIR-1 (6), and red (4). This composite displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation has lighter shades. Urban areas are blue and soils have various shades of brown. Agriculture (6, 5, 2) This band combination uses SWIR-1 (6), near-infrared (5), and blue (2). It's commonly used for crop monitoring because of the use of short-wave and near-infrared. Healthy vegetation appears dark green. But bare earth has a magenta hue. Geology (7, 6, 2) The geology band combination uses SWIR-2 (7), SWIR-1 (6), and blue (2). This band combination is particularly useful for identifying geological formations, lithology features, and faults. Bathymetric (4, 3, 1) The bathymetric band combination (4,3,1) uses the red (4), green (3), and coastal bands to peak into water. The coastal band is useful in coastal, bathymetric, and aerosol studies because...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination. Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com

Vineesh V, Geography

Search This Blog