Validation of a workflow based on Sentinel-2, Sentinel-1 and meteorological data predicting biomass on pastures
文献类型： 会议论文
第一作者： Nickmilder C
作者： Nickmilder C 1 ; Tedde A 1 ; Dufrasne I. 2 ;
作者机构： 1.TERRA Research Centre, Passage des Deportes 2, 5030 Gembloux, Belgium
2.Centre des Technologies Agronomiques, rue de la Charmille, 16, 4577 Stree-Modave, Belgium
关键词： compressed sward height;pasture;remote sensing;prediction. Sentinel;machine learning
会议名称： Symposium of the European Grassland Federation
主办单位：
页码： 95-97
摘要： This study develops the validation of the four best promising models resulting from a workflow processing Sentinel-1, Sentinel-2 and meteorological data through 13 different machine learning algorithms that led to 124 models predicting biomass under the form of compressed sward height on square sub-samples of paddocks (i.e., pixel-based estimation with a resolution of 10 m). The training and validation data were acquired in 2018 and 2019 in the Walloon Region of Belgium with a rising platemeter equipped with a GPS. The cubist, perceptron, random forest and general linear models had a validation root mean square error (RMSE) around 20 mm of CSH. However, the information relevant for the farmer and for integration in a decision support system is the amount of biomass available on the whole pasture. Therefore, those models were also validated at a paddock-scale using data from another farm (117 CSH records acquired with a different rising platemeter) based on input variables expressed at paddock scaleor predictions aggregated at paddock scale. The resulting RMSE were higher than before. To improve the quality of prediction, a combination of the outputs of the models might be needed.
分类号： S812-532
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Deployment of models to predict compressed sward height at a large scale: results and feedback
作者：Nickmilder C.;Tedde A;Dufrasne I.
关键词：machine learning;decision support system;dairy cows;grazing management;pasture
