Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
2022-09
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
24
Series
Remote Sensing, Volume 14, issue 17
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.Description
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
Other note
Citation
Yli-Heikkila, M, Wittke, S, Luotamo, M, Puttonen, E, Sulkava, M, Pellikka, P, Heiskanen, J & Klami, A 2022, ' Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network ', Remote Sensing, vol. 14, no. 17, 4193 . https://doi.org/10.3390/rs14174193