# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 2 Remote Sensing for Agriculture
### Spectral Resolutions and Useful Vegetation Indices for Sentinel-2 Features  
Besides temporal and spatial resolution, spectral resolution is a central feature of satellites as it determines the plant physiological properties that may be accurately quantified. In this sense, the number of spectral bands in several sensor systems is shown together with the canopy reflectance spectrum in Figure 2, taking the examples of landscape, canopy, and plant scale sensors placed in  
Figure 1: Current constellation of available satellites for land remote sensing that are highly relevant to crop monitoring and management, presented with their temporal and spatial resolution. satellites (MODIS, Landsat-8, Sentinel-2, and Planet, respectively). Comparing these satellites with Sentinel-2, we observe that all include sensors with visible, near infrared, and shortwave infrared bands; differently, Planet has only the three available bands in the visible (RGB, for red, green, and blue reflected light) and one in the near infrared (NIR). A different feature from Sentinel-2 is that it also captures four narrowband spectral regions of different wavelengths present in the Red Edge (RE) spectral region that are provided at 20 m resolution (Figure 2). The reflectance spectrum between 690 and 740 nm determines the point of maximum slope in the spectral signature of the vegetation [19], such that the RE marks the boundary between the processes of chlorophyll absorption in red wavelengths and within-leaf scattering in near-infrared wavelengths [20; 21] (Figure 2), which provides unique capacities for separating crop photosynthetic activity from biomass, as indicated by some of the spectral indices that have been developed specifically for deployment by Sentinel-2 or other satellites that provide RE spectral band coverage. Moreover, the Sentinel-2 10 m spatial resolution that is achieved in the other broader spectral bands in the visible and near-infrared can provide improved details and allows for obtaining a large variety of spectral information to define crop topologies. In this sense, the big data volume of Sentinel-2 images provides novel opportunities though also challenges towards processing due to the increased complexity of interpreting additional spectral bands and the increased overall file size. While many crop parameters may be estimated or indirectly inferred from their spectral light reflectance properties, some parameters, however, cannot be directly sensed but rather inferred through a combination of measurements of other plant properties. Chlorophyll spectral features are optically discernible within the visible and red-edge parts of the spectrum in a straightforward manner as these are classic plant pigments whose most basic function is the absorption of light for photosynthesis. On the other hand, plant nitrogen (N) content, for example, may not optically be discernible because N absorption features (1450-1940 nm) are obscured by water [22]. However, due to the close relationship between N concentration and chlorophyll content, chlorophyll-related indicators can be used as proxies of crop nitrogen concentration or status [23]. In this sense, the sensitivity of remote sensing data towards specific plant biophysical properties of interest can be estimated using vegetation indexes (VIs, Table 2) [24; 30], which are usually fairly simple spectral wavelength (or spectral  
Figure 2: Crop reflectance spectral features, and representation of MODIS (Moderate Resolution Imaging Spectroradiometer), Landsat-8, Sentinel-2, and Planet multispectral bands and spatial resolution. bands to indicate both the width and the central wavelength of a spectral region as captured by satellite multispectral sensors) measurement combinations.