How much does multi-temporal Sentinel-2 data improve crop type classification?

Abstract

The Sentinel-2 mission of the ESA’s Copernicus programme is generating unprecedented volumes of data at high spatial, spectral and temporal resolutions. The objective of this short communication is to assess the value of multi-temporal information for crop type classification using Sentinel-2 data. The analysis is carried out in an agricultural region in Austria and considers nine crop types during two years (2016 and 2017). To assess the impact of multi-temporal information, we applied a Random Forest (RF) classifier and analysed the results by using the RF out-of-bag error to calculate the overall accuracy (OA) and F1 score. The models were also validated using an independent reference dataset. Results show how the addition of multi-temporal information increases the crop type classification accuracy with similar trends for 2016 and 2017. At the very beginning of the crop growing season (March–April), the classification achieves relatively low accuracies (OA: ∼0.50). Significant increases in OA can be obtained between May and June, until the OA reaches its highest value in July. The final RF model was able to predict with very high confidence nine crop types for both years (OA: 0.95–0.96 and F1 score: 0.83–1.00). The independent validation dataset with more than 5000 reference plots showed comparable results (OA: 91–95% and F1 score: 0.74–0.99). We conclude that the multi-temporal crop type classification efficiently mitigates negative effects observed when using single-date acquisition within sub-optimal temporal windows.