# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 3 Sentinel-2 for Precision Agriculture  
The ESA Sentinel-2 satellite's potential applications in agriculture are multi-fold, as described above with regard to its specific spatial, temporal, and spectral capacities. Regarding the definition of the International Society of Precision Agriculture, precision agriculture is a management strategy that uses temporal and spatial information to support management decisions according to estimated variability for improved resource use efficiency and sustainability of agricultural production. This section gathers examples of Sentinel-2 capacity to sense variations at several levels of agricultural production and the subsequent management decisions that can be taken to address any disruption on crop development. The examples discussed are summarized in Table 3.  
One very notable use of Sentinel-2 features at field monitoring is the ESA Sen2-Agri system which generates modules of time series along the growing season. These different products consist of a monthly cloud-free composite of surface reflectance at 10-20 m resolution; monthly dynamic cropland masks, delivered from the agricultural mid-season onwards; a cultivated crop type map at 10 m resolution for main crop groups; and time series of vegetation status indicators, NDVI and LAI [44]. This field monitoring tool has been successfully used not only in developed countries with homogenous and well delineated fields, but as well in challenging (in terms of farm fields boundaries) countries such as Mali, successfully differentiating crop types (80% of overall accuracy) [45]. In Europe S2-Agri is going to be used among other initiatives within the Sen4-CAP ([http://esa-sen4 cap.org/](http://esa-sen4 cap.org/)) to advance towards an integrated administration and control system of the Common Agriculture Policy (CAP), which will be reformulated in 2020. CAP subsidies play a central role in European Union policies as they account for around 30% of EU total budget [46]. Hence, the European Union is taking advantage of Sentinel-2 features to monitor croplands aiming to optimize resources by determining actual crop type and cropland accurate area. Following the coming 2020 CAP framework, several case studies in European regions for cropland classification using Sentinel-2, such as the one published by [PERSON] et al. [47] for Valencia (Spain), will provide scientific tools necessary for the upcoming policies. The precise agricultural monitoring will follow adequate fertilization or irrigation guidelines, among others. The advantage of Sentinel-2 multi-temporal data for crop-type classification is very relevant as it reaches up to 91-95% overall accuracies in various crop classifications [48] while single-date images show limited results. Besides the revisit time, the higher spatial and spectral resolution also improves area detection and estimations as shown in small size rice fields classification in Taiwan [49]. Additionally, in complex landscapes Sentinel-2 imagery was able to map sugarcane fields in China with over 90% of accuracy [50]. Regarding classification techniques, beside pixel based, object based has showed to be an accurate classification approach using Sentinel-2 imagery [51]. In this sense, object-based dynamic time warping was demonstrated to be more efficient in classifying crops than the pixel-based approach when using multitemporal Sentinel-2 imagery [52], nonetheless random forest seems to be more efficient for crop type classification when crop spectral variability is high [53]. In general terms, the combination of Sentinel-2 imagery and various algorithmic approaches, such as random forest, k-nearest neighbor, and support vector machine have also shown optimal classification accuracy, resulting with over 90% of overall accuracy for the various approaches [54]. Understanding the context of the crop, namely crop type spectral features and surface extension, is central to accurately making classification method decisions when using Sentinel-2 imagery.