# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 3 Results
### User's and Producer's Accuracies  
The classification results with the highest OAs for both test areas are shown in Figure 8. These are DTW30 for single-band and multi-band for TA1, DTW45 for single-band and DTW30 for multi-band approach for TA2 (Figure 8). For complete error matrices for these four classifications, see Supplementary Tables S1 to S4. For TA1, single-band DTW30 yielded highest UAs for water (100%), alfalfa (98.2%), and onions (88.4%), while the lowest UAs were for vegetables (33.8%), wheat (56.5%), and other hay (65.9%) (Table 6). Vegetables were confused with alfalfa and this is understandable since the vegetables class contains multiple classes (lettuce, carrots, broccoli, or greens) with high intra-class diversity and number of cropping cycles that might resemble the temporal pattern of alfalfa (see Figure 4). For alfalfa, 98.2% of the validation samples were classified correctly, which is explained by the fact that alfalfa had no striking similarities with any other crop in terms of temporal pattern. Additionally, using two different reference patterns for alfalfa helped achieve the high UA value. Moreover, using 30 days flexibility in DTW allowed an accurate alignment of the high-internal diversity of temporal patterns for alfalfa. Regarding PAs, the highest values were obtained by fallow (100%), vegetables (97.8%), and sugarbeets (91.4%), while the lowest PAs were for sod/grass seed (51.2%), water (63.3%),  
Figure 8: Best classification results for single-band (**a**) and multi-band DTW (**b**) for TA1 using in both cases a time constraint of 30 days (DTW30). Best classification results for single-band (**c**) and multi-band DTW (**d**) for TA2 using 45- and 30-day time constraints, respectively (DTW45 and DTW 30). For clarity, the developed/low to medium intensity areas are masked with white. and alfalfa (77.7%) (Table 6). The sod/grass seed class was confused with the other hay/non-alfalfa class. What we classified partially as fallow was a water body and this can be explained by the presence of biomass in the water body which moved its temporal pattern towards that of fallow/idle cropland class (see Figure 4). For most crops, high intra-class heterogeneity of temporal patterns led to confusion with the complex patterns of alfalfa, which led to a PA of 77.7% for alfalfa. Vegetables had only one misclassified validation sample (97.8% PA), sign that its temporal pattern was well differentiated from the others (Figure 4). For multi-band DTW30 for TA1, OA was higher with 6.1% than the single-band DTW30. This increase in value is attributable to a significant increase in UAs and PAs for some classes (Table 6). For example, using multiple vegetation indices increased the UA for wheat with 25.4% by reducing confusions with alfalfa, non-alfalfa, sugarbeets and onions. It also increased the UA for other hay with 8.4%, and sugarbeets with 7.7%. In case of PAs, the highest increase in PA was for water, with 23.3%, sod/grass seed with 13.4%, and alfalfa with 7.7% (Table 6). As expected, water was better classified when using multiple vegetation indices, of which some are more sensitive in detecting water content (e.g., NDWI). For TA2, the single-band DTW45 achieved the highest UAs for wheat (98.1%), corn (96.0%), cotton (88.6%), and alfalfa (87.2%), while the lowest UAs were for fallow (66.7%), grass/pasture (77.3%), and double crop (79.7%) (Table 7). The fallow/idle cropland class was confused with cotton and wheat, while the grass/pasture class was confused with the cotton and fallow classes. For the latter case, this is because the temporal patterns of the three were mostly flat without prominent peaks (see Figure 5). The highest PAs were for alfalfa (100%), grass/pasture (97.7%), corn (92.3%), and wheat (90.6%), while the lowest PAs were for fallow (48.8%), cotton (82.8%), and double crop (85.0%) (Table 7). Notable class confusions occurred between the fallow and grass/pasture classes, and between the corn and cotton and double crop classes.