# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 3 Sentinel-2 for Precision Agriculture
Sentinel-2-derived research has approached yield predictions in two different fashions. On the one hand empirical models using fitting techniques of the obtained data sets (VIs and climatological data correlated with actual yields) show robust results with machine learning and random forest techniques [55; 56] as well as relatively high correlation, with an R\({}^{2}\) over 0.70, when using simple regressions with the most suitable VIs (i.e., NDVI) [57; 58]. On the other hand, the radiative transfer models approach uses the calculated biophysical parameters such as LAI or FAPAR to model crop growth and predict yields [59; 60; 61; 62]. The estimation of yields is a central issue in order to ensure food security and stabilize market demands. These performance models from Sentinel-2 imagery developed once images are available are not only aimed to provide final grain yield estimations, but they can also be used to monitor sudden changes in crop potential yields. This is especially central in taking agricultural management decisions as information is worth a little if it comes too late. Soil is the element that facilitates the assimilation of both fertilizers and water through the roots, namely the two central factors for a proper development of crops. Studies of Sentinel-2 potential for soil monitoring have been undertaken. Soil organic carbon (SOC) can be successfully mapped in croplands using Sentinel-2 bands [63; 64; 65]. SOC prediction models were found to be overall better when formulated using the visible B4, B5, near infrared B8A, and two SWIR bands (B11, B12) [67]. Furthermore, cropland soil degradation with classification methods have also been studied [68; 69]. Detecting agricultural fields affected by soil degradation or poor SOC can drive the intervention of farmers in order to prevent the failure of their fields. Thus, the decision to apply organic matter to improve soil structure or to improve field edges buffers (e.g., revegetating or reshaping them) can be driven with these Sentinel-2 data. Irrigation and water requirements of crops are related to cultivars evapotranspiration (ET), where thermal bands are very suitable to estimate this parameter. However, in the case of Sentinel-2 only optical bands are available. In this sense, different strategies have been performed in order to determinate evapotranspiration and propose proper irrigations based on Sentinel-2 information. The combination of Sentinel-2 data with models such as FAO-56 Penman-Monteith and Aquacrop in the case of tomato have shown to be successful [70; 71]. Moreover, the combination of FAO-56 Penman-Monteith with Sentinel-2 derived LAI have also shown relatively high accuracy in ET retrieval [72]. Several studies also take advantage of the red and red-edge bands to predict the crop coefficient (Kc) and thereafter use it together with ET\({}_{0}\)[73; 74]. The combination of accurate crop type mapping and ET models allows establishing regional and field-level water requirements to drive a sustainable use of water resources. Regarding precision fertilization management, two different approaches, both fairly similar to those described above for yield, have been developed. On one hand, the use of radiative transfer models to obtain chlorophyll content [75; 76; 77], and on the other hand, using empirically correlated vegetation indexes and bands to assess the nitrogen status of the crop [78; 79; 80; 81]. This strategy generally includes the three Sentinel-2 RE bands (705, 740, and 783 nm, Figure 2), which are very important for the retrieval of chlorophyll content [82]. The Belgian collaborative agriculture platform ([[http://www.belcam.info/](http://www.belcam.info/)]([http://www.belcam.info/](http://www.belcam.info/))) is taking advantage of these Sentinel-2 features to provide freely nitrogen status information at parcel level for farmers growing potato and winter wheat in Belgium.