Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

Maria Yli-Heikkila*, Samantha Wittke, Markku Luotamo, Eetu Puttonen, Mika Sulkava, Petri Pellikka, Janne Heiskanen, Arto Klami

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
13 Downloads (Pure)

Abstract

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable.

Original languageEnglish
Article number4193
Number of pages24
JournalRemote Sensing
Volume14
Issue number17
DOIs
Publication statusPublished - Sep 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • crop production statistics
  • yield forecasts
  • object-based
  • remote sensing
  • machine learning
  • agriculture
  • time series
  • CLOUD DETECTION
  • WHEAT YIELD
  • RANDOM FORESTS
  • GRAIN-YIELD
  • LAND-COVER
  • SATELLITE
  • PHENOLOGY
  • PROGRAM
  • MODEL
  • INDEX

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