aalto1 untyped-item.component.html
Tillage and biomass detection for estimating winter-time cropland management practices with satellite remote sensing
Loading...
Access rights
openAccess
CC BY
CC BY
Creative Commons license
Except where otherwised noted, this item's license is described as openAccess
publishedVersion
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)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
Series
European Journal of Remote Sensing, Volume 58, issue 1
Abstract
Supportive policies to promote sustainable agriculture have been implemented across countries and regions. For example, continuous vegetative groundcover and reduced tillage have been advocated for sustainable post-harvest biomass management. Accurate and timely information on cropland management practices is needed for agricultural policy evaluations, evidence-based planning, and agri-environmental assessments. We show that a satellite-based approach can yield off-season cropland management information on preferred spatial and temporal scales from a narrow window of opportunity in early spring after snow melt and before seedbed preparation. Agricultural parcel geometries from an administrative registry were used to extract information on Sentinel-1 backscatter and coherence, and Sentinel-2 spectral reflectance. Based on a large survey-based dataset of 6,623 fields, we show that the highest impact on model performance comes from the spectral regions of near-infrared and upper red edge of the Sentinel-2 mission, whereas Sentinel-1–based features had a relatively small contribution to classification performance. Our proposed method for tillage and biomass detection generalises well in the study area of boreal environmental zone with dominantly mineral soils, as confirmed by the high test set classification accuracy of 85%. The supporting dataset and codes are stored in a publicly accessible repository.
Description
Publisher Copyright: © 2025 Natural Resources Institute Finland (Luke). Published by Informa UK Limited, trading as Taylor & Francis Group.
Other note
Citation
Yli-Heikkilä, M, Klami, A, Wittke, S, Luotamo, M, Mero, P, Pellikka, P, Heiskanen, J, Hiltunen, M, Luojus, K, Prakasam, G, Strahlendorff, M, Törmä, M & Sulkava, M 2025, 'Tillage and biomass detection for estimating winter-time cropland management practices with satellite remote sensing', European Journal of Remote Sensing, vol. 58, no. 1, 2525967. https://doi.org/10.1080/22797254.2025.2525967