Research

IGNOS aims to evaluate the potential and limitations of GNSS-R as a scalable, all-weather complementary solution for Leaf Area Index (LAI) estimation. It is particularly important because of the urgent need for consistent observations in tropical areas where Copernicus Sentinel-2 optical satellites encounter significant challenges. By analyzing the relationship between GNSS-R data with Sentinel-2 imagery, IGNOS assesses the potential of the GNSS-R technique to become a complementary technique to Sentinel-2, enhancing vegetation monitoring in cloud-prone regions without additional sensor deployment, thus offering a cost-effective and environmentally friendly strategy. The outcome of this project therefore should evaluate the capabilities for a continuous LAI observation under cloudy conditions, leading to better-informed decisions in sustainable forestry, agriculture, and ecosystem management. The improved LAI data will primarily benefit Earth Observation users (e.g., remote sensing experts working in ecosystem yodelling) and agricultural communities (e.g., farmers and land managers) by enabling precision agriculture and biodiversity monitoring.

This research project is carried out in collaboration with the HBLFA Raumberg-Gumpenstein. The research focuses on evaluating sustainable nutrient management on alpine pastures through site-specific management strategies. These results will inform the HBLFA Raumberg-Gumpenstein's contributions to the Alpine Pasture Evaluation Study (APES), which is funded by the Federal Ministry for Agriculture, Regions and Water Management (BML). BOKU is involved in two work packages related to remote sensing and geospatial analysis for yield estimation and change detection in pasture spatial structures.

The research contract with 'Die Bundesanstalt Statistik Österreich' focuses on developing algorithms and data access structures to apply existing models for calculating annual yields and feed quality (crude protein content) in various grassland cutting systems. The know-how was developed within the FFG ASAP SatGrass project at the Institute of Geomatics. The research services include the extension of the following aspects: - Database Evaluation: Assessing a database for SatGrass model parameters using satellite data at the parcel level (IACS-GIS). - Start of Growing Season (SOS) Estimation: Developing algorithms to determine SOS for mown meadows using alternative satellite data, replacing the limited-time availability of MODIS data, and generating SOS time series. - Cut Detection Enhancement: Advancing cut detection methods based on Sentinel-1 and Sentinel-2 time series.