Detection and attribution of cereal yield losses using Sentinel-2 and weather data: A case study in South Australia

Keke Duan*, Anton Vrieling, Michael Schlund, Uday Bhaskar Nidumolu, Christina Ratcliff, Simon Collings, Andrew Nelson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method’s potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R2 of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. The proposed framework provides a basis for early warning of crop damage and attributing the damage to weather extremes in near real-time, which should help to adopt appropriate crop protection strategies.
Original languageEnglish
Pages (from-to)33-52
Number of pages20
JournalISPRS journal of photogrammetry and remote sensing
Volume213
DOIs
Publication statusPublished - 1 Jul 2024

Keywords

  • Satellite image
  • Growing degree days
  • Extreme weather
  • Yield reduction
  • Crop phenology
  • Machine learning
  • ITC-HYBRID
  • ITC-ISI-JOURNAL-ARTICLE
  • UT-Hybrid-D

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