Soil moisture analysis to optimize farm operations

EOS Data Analytics provides satellite-powered soil moisture analytics for smarter agricultural and environmental decisions to optimize irrigation, prevent drought loss, and improve crop yields.

  • Accurate surface moisture mapping using SMAP and AMSR radiometric data.
  • Regular updates every 1–3 days, and historical data available from 2002.
  • Flexible data delivery, including raster layers, API access, and integration into other crop monitoring software.
soil moisture analysis performed with EOS Data Analytics' solution

Approach and Methodology

Algorithm: The technology is based on the restoration of moisture content from radiometric data, taking into account soil type (clay content as a primary parameter).

Satellite data: The model uses satellite imagery from microwave scanning radiometer sensors (e.g., SMAP and AMSR satellites).

Resolution: Spatial resolution of layers up to 250x250 m for SMAP and up to 100x100 m for AMSR satellites, depending on requirements.

Limitations: The technology may not work well for small objects and coastal areas.

high-level soil moisture analysis processing flow scheme by EOS Data Analytics

Expected project outputs and formats

soil moisture map of Ukraine
  • Raster layers in GeoTIFF format showing soil moisture at a volumetric surface level per pixel.
    • Resolution up to 250 m (SMAP) or up to 100 m (ASMR)
    • Historical data from 2015 (SMAP) and 2002 (ASMR)
    • Data update frequency: 2-3 days (SMAP) and 1-2 days (ASMR)
  • Data delivery via API (user gets data upon request of target polygons and dates)
  • Delivery via Crop monitoring platform as a trend or average value per field.

Required data for any soil moisture analysis project

Data provided by the customer

  • Area of interest: table (xlsx/ csv) or vector format (KML, ESRI shapefile, GPKG, GeoJSON)
  • Soil data (clay, density, sand), if available
  • Ground truth data for validation, if available

Data prepared by EOSDA

  • Satellite imagery (SMAP, AMSR satellites)
  • Soil data (clay, density, sand), from open sources or modeled/ interpolated
  • Validation of soil moisture datasets

Standard project stages

Typical project duration: 2-3 weeks for a new AOI

1

Investigation of AOI, analysis of data coverage

2

Searching for or modeling/interpolating soil maps

3

Launch of current or historical calculations

4

Result verification and preparation of final outputs

Benefits of the soil moisture analysis solution by EOSDA

  • Irrigation management optimization.
  • Crop health and yield optimization (prevention of water stress).
  • Drought monitoring and early warning support (identification of developing drought conditions, allowing for proactive mitigation strategies).
  • Flood risk assessment support and accuracy increase.
  • Better disease and pest control (through management of the conditions that might favor certain diseases or pests, e.g., excessive moisture).