Creation of a Walloon Pasture Monitoring Platform Based on Machine Learning Models and Remote Sensing

Abstract

The use of remote sensing data and the implementation of machine learning (ML) algorithms is growing in pasture management. In this study, ML models predicting the available compressed sward height (CSH) in Walloon pastures based on Sentinel-1, Sentinel-2, and meteorological data were developed to be integrated into a decision support system (DSS). Given the area covered (>4000 km2 of pastures of 100 m2 pixels), the consequent challenge of computation time and power requirements was overcome by the development of a platform predicting CSH throughout Wallonia. Four grazing seasons were covered in the current study (between April and October from 2018 to 2021, the mean predicted CSH per parcel per date ranged from 48.6 to 67.2 mm, and the coefficient of variation from 0 to 312%, suggesting a strong heterogeneity of variability of CSH between parcels. Further exploration included the number of predictions expected per grazing season and the search for temporal and spatial patterns and consistency. The second challenge tackled is the poor data availability for concurrent acquisition, which was overcome through the inclusion of up to 4-day-old data to fill data gaps up to the present time point. For this gap filling methodology, relevancy decreased as the time window width increased, although data with 4-day time lag values represented less than 4% of the total data. Overall, two models stood out, and further studies should either be based on the random forest model if they need prediction quality or on the cubist model if they need continuity. Further studies should focus on developing the DSS and on the conversion of CSH to actual forage allowance.