# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 4 Discussion  
The capacity to map crop types using phenological metrics with a high spatial resolution has been demonstrated in this research study for a heterogeneous, small, and fragmented agricultural system. Multi-temporal information has been demonstrated to increase the crop type classification's accuracy significantly [7]. In the context of crop type mapping and the monitoring of agricultural practices, synthesizing information to fewer phenological metrics facilitates image data processing by reducing the time series' dimensionality [18].  
[PERSON] et al. [1] analyzed the performance of crop classification from multi-temporal Landsat 8 OLI images over a study area in Northern Italy. Four supervised classification algorithms applied to the spectral indices' profiles were tested over different time step datasets to assess the performance of in-season crop classification in the year 2013. The result was a crop type map with seven classes with OA = 86.5% that was produced five months ahead of the end of season, in the middle of July.  
Many studies have confirmed crop classifications with a high accuracy (OA = 82%) for eight crop types [4] and mapped cropland status (cropped or fallowed) with accuracies over 75% [23]. The crop recognition method can lose accuracy, especially when the mapped crops have high intra-class variability [10] or when insufficient knowledge of the field data relating to the phenological cycles of the crops is available [24; 25].  
Despite the overall accuracies and Cohen's kappa coefficient being similar for both the NDVI and the LAI model, comparing the results for individual classes, the latter showed slightly higher performance.  
[PERSON] et al. [26] worked toward crop classification (maize, soybean, sunflower) using the temporal profile of NDVI and radar backscatter (VH, VV, and VH/VV). Regarding the classification of the crop, they concluded that NDVI shows low ability to distinguish summer crops, except for sunflower, during the senescence period in August and September. Besides, during periods of strong cover development, NDVI's sensitivity to biomass is more likely to become saturated. High misclassifications of horticultural crops may be related to the different seeding times of the horticultural species, which could increase the variability in terms of the range of the predictors' values. With respect to soy and rapeseed, it should be noted that the small number of reference crops used for model calibration and validation could be the reason for such a low class accuracy.  
Figure 1: Crop type map for the year 2019 for the area of the city of Pisa (Tuscany, Italy).  
[PERSON] et al. [27] outlined that although converting multi-year land-use data into a crop rotation history is relatively simple in theory, the presence of classification errors can severely compromise the results. Given this fact, they proposed using a matrix of logically forbidden or extremely unlikely year-to-year land use transitions to detect classification errors. Likewise, the use of a priori knowledge of the local rotation practices could be a research avenue for improving crop type identification by constraining the classification models. Future research should consider the use of extended crop type information (e.g., the LUCAS Soil DataBase) in order to increase the number of classification model training points and therefore improve the overall accuracy.