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PhenoSAR DEMMIN

SAR-based monitoring of croplands

The PhenoSAR-Demmin project investigates the derivation of phenological times and periods from remotely sensed time series according to the current state of scientific knowledge. Data from the Sentinel-1 mission are the main focus. In recent years, numerous studies have shown that these data provide an excellent basis for phenological monitoring. In particular, radar’s sensitivity to physiognomic changes in plants and its greater independence from the weather compared to optical systems are of great advantage. Based on these findings, PhenoSAR aims to develop a monitoring approach that enables the detection of individual points in time as well as time periods, such as the transition from the vegetative to the reproductive phase. This will be done in particular with regard to the work already underway at the Demmin site. In the national context, the AgriSens Demmin 4.0 project should be mentioned here. The integration of the site into the JECAM initiative, on the other hand, ensures cooperation at an international level and serves as a starting point for the planned spatial transfer of the monitoring approach. Furthermore, the project is closely linked to the data cube infrastructure of the Chair of Remote Sensing in order to create technical possibilities for transfer and scalability.

 

Project lead:
Project partner:

  • Earth Observation Research Cluster Julius-Maximilian-Universität (JMU), Würzburg

Goals:
 
Methodischer Ansatz:

  • Interferometric and polarimetric time series derived from Sentinel-1 data

  • Time series analysis; inference of turning points and extremes

  • Agro-meteorological data assimilation

Expected results:

The expected outcome is a monitoring framework for detecting different stages of plant growth and their transitions, with a focus on improved transferability and scalability.

Publications:
1
Mahmood, T., Loew, J., Poehlitz, J., Wenzel, J.L. and Conrad, C. (2024) Estimation of 100 m Root Zone Soil Moisture by Downscaling 1 Km Soil Water Index with Machine Learning and Multiple Geodata. Environmental Monitoring and Assessment, 196, 823. https://doi.org/https://doi.org/10.1007/s10661-024-12969-5.
1
Loew, J., Hill, S., Otte, I., Thiel, M., Ullmann, T. and Conrad, C. (2024) How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits. Remote Sensing, 16, 2791. https://doi.org/10.3390/rs16152791.
1
Loew, J., Hill, S., Thiel, M., Ullmann, T. and Conrad, C. (2024, June 20) How Does Phenology Shape Crop- And Orbit-Specific InSAR Coherence And PolSAR-Signatures Of Sentinel-1. Manchester, UK.
1
Friedrich, C., Loew, J., Otte, I., Hill, S., Schierghofer, C., Gessner, U., Truckenbrodt, S., Schonert, E., Piernecke, T., Conrad, C. and Thiel, M. (2024, March 21) DataCube Architecture for Integrating, Processing and Presenting Big Geodata to End Users. Berlin.
1
Loew, J., Hill, S., Thiel, M., Ullmann, T. and Conrad, C. (2024) Tracking Crop Phenology across Different Sentinel-1 Orbits by Combining PolSAR Features with Growing Degree Data. Remagen.
1
Loew, J., Hill, S., Thiel, M., Ullmann, T. and Conrad, C. (2024, March 15) Tracking Crop Phenology across Different Sentinel-1 Orbits by Combining PolSAR Features with Growing Degree Data. Remagen.
1
Friedrich, C., Loew, J., Otte, I., Hill, S., Förtsch, S., Schwalb-Willmann, J., Gessner, U., Schierghofer, C., Piernecke, T., Truckenbrodt, S., Schonert, E., Assmann, D., Böttcher, F., Conrad, C. and Thiel, M. (2024, February 28) A Multi-Talented Datacube: Integrating, Processing and Presenting Big Geodata for the agricultural End User. Stuttgart-Hohenheim.
1
Friedrich, C., Loew, J., Otte, I., Hill, S., Förtsch, S., Schwalb-Willmann, J., Gessner, U., Schierghofer, C., Piernecke, T., Truckenbrodt, S., Schonert, E., Assmann, D., Böttcher, F., Conrad, C. and Thiel, M. (2024) A Multi-Talented Datacube: Integrating, Processing and Presenting Big Geodata for the agricultural End User. Informatik in Der Land-, Forstund: Biodiversität Fördern Durch Digitale Landwirtschaft, Stuttgart-Hohenheim.
1
Lobert, F., Loew, J., Schwieder, M., Gocht, A., Schlund, M., Hostert, P. and Erasmi, S. (2023) A Deep Learning Approach for Deriving Winter Wheat Phenology from Optical and SAR Time Series at Field Level. Remote Sensing of Environment, 298, 113800. https://doi.org/10.1016/j.rse.2023.113800.
1
Lobert, F., Loew, J., Schwieder, M., Gocht, A., Schlund, M., Hosert, P. and Erasmi, S. (2023) Deriving Winter Wheat Phenology From Combined Optical And SAR Time Series With Deep Learning. The 42nd EARSeL Symposium, Bucharest.
1
Loew, J., Hill, S., Ullmann, T. and Conrad, C. (2022) Assessing the Applicability of Interferometric and Polarimetric Time Series Derived from Sentinel-1 for Tracking Phenological Developments of Crops at the JECAM Site DEMMIN (Germany). Arbeitskreis Fernerkundung, Halle (Saale).
1
Loew, J., Hill, S., Ullmann, T. and Conrad, C. (2022) Tracking the Phenological Developments of Crops by a Complementary Set of Interferometric and Polarimetric Time Series Derived from Sentinel-1 at the JECAM Site DEMMIN (Germany). ESA Living Planet Symposium, Bonn.
1
Loew, J., Ullmann, T. and Conrad, C. (2021) The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany). Remote Sensing, 13, 2951. https://doi.org/10.3390/rs13152951.
Abschlussarbeiten:

News
  • Website

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  • Research area
Demmin, Mecklenburg-Vorpommern
  • Project duration
2020-2026
  • Funding body
DLR; MLU; BMEL; BMBF
  • Project orientation
Research
  • Contact

Johannes Löw