Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations
[en] Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.
Research Center/Unit :
Centre Wallon de recherche agronomique
Disciplines :
Animal production & animal husbandry Environmental sciences & ecology
Author, co-author :
Nickmilder, Charles ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges
Tedde, Anthony ; Université de Liège - ULiège > Département GxABT > Modélisation et développement
Dufrasne, Isabelle ; Université de Liège - ULiège > Dpt. de gestion vétérinaire des Ressources Animales (DRA) > Nutrition des animaux domestiques
Lessire, Françoise ; Université de Liège - ULiège > Dpt. de gestion vétérinaire des Ressources Animales (DRA) > Nutrition des animaux domestiques
Tychon, Bernard ; Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement
Curnel, Yannick; Centre Wallon de Recherches Agronomiques (CRA-W) > Productions Agricoles > Agriculture, Territoire et Intégration Technologique
Bindelle, Jérôme ; Université de Liège - ULiège > Département GxABT > Ingénierie des productions animales et nutrition
Soyeurt, Hélène ; Université de Liège - ULiège > Département GxABT > Modélisation et développement
Language :
English
Title :
Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations
Publication date :
01 February 2021
Journal title :
Remote Sensing
eISSN :
2072-4292
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Basel, Switzerland
Volume :
13
Issue :
3
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
Roadstep
Funders :
DGA - Région wallonne. Direction générale de l'Agriculture
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