Sensitivity of machine-learning crop-type mapping to feature selection and hyper-parameter tuning.

Abstract

To improve crop yields and economic incomes, farmers consistently adapt their practices to climate and market fluctuations, resulting in highly variable crop field distribution and coverage in space and time. As these dynamics ilustrate, up-to-date crop-type mapping is essential to understand farmers’ needs and supporting them in adopting sustainable practices. With global coverage and frequent temporal observations, remote sensing data are generally integrated in machine learning models to monitor crop-type mapping dynamics. Unlike physical-based models that rely on straightforward use, the implementation of machine-learning approaches depends on deep interaction with users. In this context, the study assesses the output sensitivity of these models to features selection and hyper-parameter calibration, both of wich rely on user consideration. To do so, Sentinel-1 (S1) and Sentinel-2 (S2) features are integrated into five distinct models (RF, SVM, LGB, HGB, XGB), considering different features selection (VIF and SFS) and hyper-parameter calibration set-up. Results show that pre-process modeling VIF feature selection discards features that wrapped SFS feature selection keeps, resulting in less reliable crop-type mapping compared to using SFS. Additionally, hyper-parameter calibration appears to be sensitive to the input feature and its consideration after any the feature selection improved the crop-type mapping. In this context a three-step nested modelling set-up including a first hyper-parameters calibration followed by a wrapped feature selection (SFS) and another hyper-parameter calibration, lead to the most reliable model outputs. Across the considered region, LGB and XGB (SVM) are the most (less) suitable model for crop-type mapping and models reliability improved when integrated S1 and S2 features rather than the consideration of S1 or S2 alone. Finally, crop-type maps are derived across different regions and periods to highlight the benefits of the proposed method to monitor crops’ dynamics in space and time.