Harvesting Insights from the Sky: Satellite-Powered Automation for Detecting Mowing Based on Predicted Compressed Sward Heights

• Published in: Applied sciences Vol. 14; no. 5; p. 1923
• Main Authors: Dichou, Killian, Nickmilder, Charles, Tedde, Anthony, Franceschini, Sébastien, Brostaux, Yves, Dufrasne, Isabelle, Lessire, Françoise, Glesner, Noémie, Soyeurt, Hélène
• Format: Journal Article Web Resource
• Language: English
• Published: Basel MDPI AG 01.03.2024
• Subjects: Accuracy; Agriculture & agronomie; Agriculture & agronomy; Algorithms; Biodiversity; Climate change; compressed sward height; Computer Science Applications; Datasets; Fourier transforms; Geospatial data; Life sciences; Machine learning; mowing; pasture; Precipitation; Sciences du vivant; Belgium
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14051923

The extensive identification of mowing events on a territory holds significant potential to help monitor shifts in biodiversity and contribute to assessing the impacts of drought events. Additionally, it provides valuable insights into farming practices and their consequential economic and ecological effects. To overcome challenges in obtaining reference grazing information directly from the field, this study introduces a novel methodology leveraging the compressed sward height (CSH) derived from Sentinel-1, Sentinel-2, and meteorological data, boasting an accuracy of 20 mm. Our central hypothesis posits that the mowing status of a parcel can be automatically discerned by analyzing the distribution and variation of its CSH values. Employing a two-step strategy, we first applied unsupervised algorithms, specifically k-means and isolation forest, and subsequently amalgamated the outcomes with a partial least squares analysis on an extensive dataset encompassing 194,657 pastures spanning the years 2018 to 2021. The culmination of our modeling efforts yielded a validation accuracy of 0.66, as ascertained from a focused dataset of 68 pastures. Depending on the studied year and with a threshold fixed at 0.50, 21% to 57% of all the parcels in the Wallonia dataset were tagged as mown by our model. This study introduces an innovative approach for the automated detection of mown parcels, showcasing its potential to monitor agricultural activities at scale.