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PhenoSAR DEMMIN

SAR-based monitoring of croplands
PhenoSAR (2)
PhenoSAR (4)
Author: Johannes L?w Created: 2020-10-06T15:53:47 Creator: QGIS 3.4.7-Madeira Keywords: Producer: QGIS 3.4.7-Madeira Subject: Title:
PhenoSAR (3)

The goal of the PhenoSAR Demmin project is to evaluate potential information inherent to the Sentinel-1 time series to monitor farmland. Hereby, a particular focus is set on phenological development. The expected result is a monitoring framework to detect various stages of plant growth and their transition. This framework is based on interferometric and polarimetric Sentinel-1 time series and the underlying information about changes in plant geometry. Additionally, this project focuses on the improved transferability and scalability of its approach. Hence, its primary platform of development is an open data cube environment. With the test site DEMMIN as the study area, the project is connected to ongoing research in the AgriSens DEMMIN 4.0 project as well as the German TERENO and international JECAM Initiatives

Sub-project leader:
Team:
Project partners:
  • Department of Remote Sensing Julius-Maximilian-Universität (JMU), Würzburg
Background and objectives:
  • evaluate potential information inherent to the Sentinel-1 time series to monitor farmland
  • focus is set on phenological development
  • primary platform of development is an open data cube environment
Methodical approach:
  • interferometric and polarimetric Sentinel-1 time series
  • Time series analysis; derivation of break points and extreme values
  • Assimilation of agro-meteorological data

 

Expected results:

The expected result is a monitoring framework to detect various stages of plant growth and their transition 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.
Theses:

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  • Research area
Demmin, Mecklenburg-Vorpommern
  • Project duration
2020-2024
  • Funding body
DLR
  • Project orientation
Research
  • Contact

Johannes Löw