Remote Sensing

The overall aim of the remote sensing activities at IBG-3 is to gain knowledge about land surface processes, especially regarding the hydrological cycle. This includes the spatial and temporal dynamics of hydrological state variables such as soil moisture, but also the characterization of the vegetation is important as it defines infiltration and evapotranspiration. To monitor the land surface at multiple scales, we are working with different platforms, e.g. drones, towers, aircrafts and satellites.

Remote Sensing
Scales and platforms for remote sensing at IBG-3.

The estimation of soil moisture in the root zone is of high importance for improving short- and medium-term meteorological modeling, agricultural production, the monitoring of plant growth, as well as for forecasting of hazardous events such as floods. We develop passive (radiometer) as well as active (radar) microwave methods and validate them against in situ observations from wireless soil moisture sensor networks, cosmic ray probes, airborne campaigns and model simulations. Another aim is to optimally combine active and passive microwave measurements of soil moisture by developing new fusion and downscaling algorithms to derive high-resolution near-surface soil moisture fields. One important task in this context is the utilization of the remote sensing data in hydrological models. A sound integration is assured by data assimilation methods. Those sequential Monte Carlo techniques, such as the particle filter, are integrated in an operational way into the processing chains for higher level remote sensing products. We utilize multi-sensor (multi-spectral, LiDAR, thermal IR) drone observations to retrieve vegetation characteristics such as leaf area index and biomass to estimate high resolution evapotranspiration.

Remote Sensing
Drone-based LiDAR intensities for the patch crop field Dahmsdorf, Brandenburg.

We recently started research to utilize the previously described variables observed from drones, aircrafts and satellites to better characterize the carbon cycle and the estimation of gross primary productivity.


Dr. Carsten MontzkaSenior Scientist in Remote Sensing of Hydrological and Biophysical VariablesGebäude 16.6z / Raum R 3049+49 2461/61-3289

Dr. Carsten Montzka

Dr. Bagher Bayat

Dr. Shirin Moradi

Dr. Rahul Raj

Dr. Viktoriia Lovynska

Dr. Mehdi Rahmati

Dr. Hao Chen

David Mengen

Yuquan Qu

Jordan Bates

Wenqin Huang

Visakh Sivaprasad

Nick Kupfer

Xuerui Guo

We are involved in the following projects, please visit the websites for further reads:

You will find our remote sensing publications under

Letzte Änderung: 08.11.2023