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Personal photograph

Lars Eklundh

Professor

Personal photograph

Using sentinel-2 data to quantify the impacts of drought on crop yields at local and regional scales in Sweden

Author

  • Mitro Müller
  • Shangharsha Thapa
  • El houssaine Bouras
  • Per Ola Olsson
  • Sadegh Jamali
  • Lars Eklundh
  • Jonas Ardö

Summary, in English

A causal inference framework was developed to investigate crop responses to agricultural drought by integrating meteorological data, Sentinel-2-derived data, and soil property maps. To account for crop rotation, soil, and topographical variables, propensity score matching was employed to estimate drought-induced yield losses at the field level for selected periods. The Plant Phenology Index (PPI) and the derived Total Productivity (TPROD) parameter enabled monitoring of crop development and productivity. TPROD showed high regional accuracy (R² = 0.93) and field-level accuracy for estimating crop yields (R² = 0.42–0.73, varying by crop type). The monitoring of common production crops in Sweden during the 2018 drought revealed that all crops had a shortened growing season, with spring-sown crops experiencing greater yield losses. The influence of soil texture variables, which act as indicators of water holding capacity, on the variability of drought-induced yield losses was assessed, and seasonal dynamics were examined, thereby improving the comprehension of the interactions among soil-plant-atmosphere dynamics at a local scale. We conclude that applying propensity score matching combined with satellite remote sensing can provide site-specific information on crop selection and timing and facilitate economically efficient irrigation planning. Nevertheless, further improvements are recommended, such as incorporating more detailed field-level data on yields and management practices, to enhance the approach's robustness and applicability for drought preparedness and adaptive agricultural management.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • MERGE: ModElling the Regional and Global Earth system
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • eSSENCE: The e-Science Collaboration
  • Department of Technology and Society
  • Geodetic Surveying
  • LU Profile Area: Nature-based future solutions

Publishing year

2025-10

Language

English

Publication/Series

Agricultural and Forest Meteorology

Volume

373

Document type

Journal article

Publisher

Elsevier

Topic

  • Agricultural Science
  • Earth Observation

Keywords

  • Agricultural drought
  • Causal inference
  • Machine learning
  • Matching
  • Propensity score
  • Satellite remote sensing
  • Yield modelling

Status

Published

Research group

  • Geodetic Surveying

ISBN/ISSN/Other

  • ISSN: 0168-1923