In a team of German research centers and universities, we aim at the development of algorithms to use satellite data for (i) mapping crop types, (ii) deriving basic spectral, vegetation, and soil parameters, iii) identifying crop phenological phases, and (iv) complex parameters such as crop yields or evapotranspiration. The vegetation parameters include LAI, biomass, cover fraction, and crop height. Soil moisture is our major soil parameter. We utilize data from optical and SAR satellite systems and combine it with meteorological information.


Project Objectives

Mapping Agricultural Areas

Operational Implementation Plan

A progressive classification algorithm was developed that identifies major crop types of the DEMMIN study site based on their phenological development and their corresponding reflectance characteristics in multitemporal satellite images of the four sensors Landsat-7 and -8, Sentinel-2A and RapidEye.



Land Use Types
  • Crop Land:
    • Spatial Resolution:
    • Temporal Resolution:

Measuring Phenological Events

Operational Implementation Plan

Phenological Events
  • Seeding
  • Vegetative Growth
  • Flowering
  • Fruit Development
  • Maturity
  • Harvest

Estimation of Biophysical Variables

Operational Implementation Plan

The potential of multi-temporal dual polarimetric TerraSAR-X data for the estimation of the leaf area index was explored for three different agricultural crops (winter wheat, barley and canola), among others using multivariate regression, i.e., stepwise regression, and the semi-empirical water cloudmodel (WCM).

Biophysical Variables
  • Vegetation Indices (NDVI, EVI, SAVI, etc.)
  • LAI (Leaf Area Index)
  • Biomass
  • Plant height

Forecasting Agricultural Variables

Operational Implementation Plan

In the DEMMIN test site there are 45 autonomous operating automated stationary environmental measurement stations with soil moisture probes (up to 1m depth,10cm resolution) and 65 soil moisture gauging stations below agricultural used fields (depths 50cm, 70cm). The measuring interval is 15 minutes.

In addition, different models for large-scale derivation of crop biomass and yield of winter wheat were tested. Therefore, high resolution optical time series and the meteorological observations from the environmental measurement stations were utilized. The optical time series are based on the fusion of moderate resolution MODIS time series with high resolution Landsat data.

Agricultural Variables (large scale)
  • Yield
  • Soil Moisture

Site Description

Landscape TopographyTypical Pleistocene landscape with hilly, loamy to clayey moraines, sandy plains, and peaty floodplains. The terrain is flat to undulating. Maximum elevation amounts to 179 m above sea level.
Typical Field Size80 ha
Climatic ZoneTemperate, transition zone from maritime to continental climate
Major Crops and Calendars

Winter Wheat (Early):

Winter Barley (Early):

Winter rapeseed (Early):

Winter Rye (Early):

Maize (Normal):

Sugar beet (Normal):

Potatoes (Normal):

Soil Type & Texture


  • Sand
  • Loamy Sand
  • Sandy Loam
  • Clay
Soil Drainage Class
    Irrigation Infrastructure
      Other Site Details

      The typical agricultural landscape of the German JECAM site DEMMIN

      In Situ Observations

      Survey at the landscape scale

      • Crop Type(s): Winter Wheat, Canola
      • Collection Protocol:

        GFZ in-house protocol for regular sampling of vegetation and soil parameters on selected fields in the DEMMIN site

      • Frequency: 11 days (March – October, since 2012)

      Survey at the field level

      • Crop Type(s): Winter Wheat
      • Collection Protocol:

        With our students we collect spectral, vegetation and soil parameters during field campaigns in the vegetative phase of winter wheat. These campaigns analyse elementary sampling units with each 14 secondary sampling units in a 70 m x 70 m rectangle. The parameters include spectra and solar radiation information, LAI, SPAD, crop height, vegetation density, phenological phase, plant orientation, fresh and dry biomass, and soil moisture. A preview to our data can be found here.

      • Frequency: Up to five sampling campaigns during the winter-wheat season (2018, 2019, 2021-ongoing)

      EO Data

      Optical Data Requirements

    • Approximate Start Date of Acquisition: 01/02
    • Approximate End Date of Acquisition: 30/11
    • Spatial resolution: Medium resolution (20-60m)High resolution (5-20m)Very high resolution (below 5m)
    • temporal_frequency: Weekly
    • Level of Expertise: Advance
    • Latency of Data Delivery: More than 5 days
    • Challanges:
    • SAR Data Requirements

    • Approximate Start Date of Acquisition: 01/02
    • Approximate End Date of Acquisition: 30/11
    • Spatial resolution: High resolution (5-20m)
    • temporal_frequency: < 6 days
    • Frequency: XCL
    • Incidence Angle: Medium
    • Polarisation:
    • Level of Processing: Level 2
    • Orbit: Any
    • Level of Expertise: Intermediate
    • Latency of Data Delivery: 2-5 daysMore than 5 days
    • Challanges:
    • Passive Microwave Data Requirements

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    • Thermal Data Requirements

    • Approximate Start Date of Acquisition:
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    • Results

      Documents and Files

      Links to paper

      • Dhillon, M. S., Dahms, T., Kuebert-Flock, C., Borg, E., Conrad, C., & Ullmann, T. (2020). Modelling crop biomass from synthetic remote sensing time series: Example for the DEMMIN test site, Germany. Remote Sensing, 12(11).
      • Hosseini, M., McNairn, H., Mitchell, S., Robertson, L. D., Davidson, A., Ahmadian, N., … Becker-Reshef, I. (2021). A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing, 13(7), 1348.
      • Ahmadian, N., Ullmann, T., Verrelst, J., Borg, E., Zölitz, R., & Conrad, C. (2019). Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 87(4).
      • Ahmadian, N., Borg, E., Roth, A., & Zölitz, R. (2016). Estimating the leaf area index of agricultural crops using multi-temporal dual-polarimetric TerraSAR-X data: A case study in north-eastern Germany. Photogrammetrie, Fernerkundung, Geoinformation, 2016(5–6), 301–317.
      • Heupel, K., Spengler, D., & Itzerott, S. (2018). A Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(2), 53–69.

      Project Reports

      Study Team

      Team Leader

    • Name: Dr. Daniel Spengler
    • Affiliation: Helmholtz-Center Postdam (GFZ) German Research Center for Geosciences
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    • Other Team Members

    • Name: Prof. Dr. Christopher Conrad
    • Affiliation: Martin Luther University of Halle-Wittenberg
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    • Position: Professor of Geoecology
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    • Role: Co-Lead
    • Name: Prof. Dr. Erik Borg
    • Affiliation: German Remote Sensing Data Center (DFD) at the German Aerospace Center (DLR)
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    • Role: Co-Lead