Postdoctoral Position in Remote Sensing of Crop Lund University

Subject Description

The position is a two-year postdoctoral appointment (with the possibility of extension to three years) to work within the project Near-real-time monitoring of crop growth and yield, funded by the Swedish National Space Agency.

The project aims to develop an integrated, satellite-based system for near-real-time monitoring of crop growth and early yield prediction, combining remote sensing, machine learning, and crop modeling to support sustainable agriculture. Within the project, we will estimate key soil properties such as clay content, organic matter, and soil moisture using radar and optical satellites (Sentinel‑1 and Sentinel‑2). To enable near-real-time monitoring of crop growth, the work includes processing satellite time-series data and applying advanced time-series methods for early estimation of seasonal growth curves.

Vegetation parameters, such as Leaf Area Index (LAI), will be retrieved from satellite data through modeling and vegetation indices. These satellite‑derived data will be integrated into crop growth models, such as SAFY and Daisy, using data assimilation techniques. The models will be validated and calibrated against field observations and UAV-based measurements. Finally, scalable and cost-effective methodologies will be developed for operational crop monitoring and yield prediction, applicable to both Swedish and international agriculture.

The main duties of the postdoctoral position are to conduct research. Teaching may be included, but no more than one-fifth of the working time. The position also includes the opportunity to complete three weeks of higher education pedagogy training.

• Perform field measurements in agricultural fields as input for remote sensing modeling.
• Develop methods to estimate soil parameters (clay content, organic matter, and moisture) from optical and radar data, e.g., using machine learning.
• Develop near-real-time methodology for estimating crop growth, including testing methods for extracting smooth seasonal curves and uncertainty estimation.
• Integrate remote sensing data with crop growth models through data assimilation.
• Develop practically applicable methods for monitoring agricultural crops based on data from various sources, including satellite sensors.
• Communicate project results appropriately to both researchers and the general public.

The work will be carried out within a group of remote sensing and modeling experts at the Department of Earth and Environmental Sciences (MGeo), Lund University.