Combining Sentinel 1, Sentinel 2 and MODIS data for major winter crop type classification over the Murray Darling Basin in Australia

Crop type classification is an essential task in agriculture that has been studied widely since the emergence of remote sensing technologies. Accurate crop mapping can help assist decision-making related to storage, marketing, and other production tasks. This study has designed robust crop classification models to classify two major crop types (cereals and canola) in the Murray Darling Basin (MDB) in Australia. These models combined Sentinel 1 and 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Three methods were applied to test classification quality: the holdout method, leave-one-season-out cross-validation (LOSOCV), and leave-one-cluster-out-cross-validation (LOCOCV). The holdout method evaluated the model's performance on data representing the entire population. The LOSOCV method was used to test the model's ability to extrapolate over time (unseen data from new seasons), and the LOCOCV method was used to test the ability to extrapolate over space (data from a new site). Crop type labelled data (n = 193 for cereals and n = 113 for canola) were extracted from high-resolution yield maps derived from grain yield monitors mounted on harvesters, and this was used to train and validate the classification models. The results showed that the holdout validation approach achieved the highest accuracy (overall and Kappa scores > 0.98), but this might be caused by spatial autocorrelation of the sampling strategy implemented, which leads to an overoptimistic scenario. The overall accuracies and Kappa scores achieved from the LOSOCV method varied depending upon the difference in crop phenology from season to season, with overall accuracy ranging from 0.70 to 0.92 and Kappa scores from 0.61 to 0.90. The overall accuracies and Kappa scores for the LOCOCV ranged from 0.87 to 0.99 and 0.84 to 0.98, respectively. The gross primary productivity (GPP) and the chlorophyll red edge index (CIr) were the most important features across the three models. The outputs of this research can potentially complement models that require crop type information, such as carbon cycling and crop yield models.