Beyond human-perceived color wavelengths, Sentinel-2 captures reflectance in non-visible near infrared bands useful for vegetation monitoring. By assigning alternative color schemes to these infrared image layers, different aspects of landscape composition and structure are revealed. For example, healthy vegetation appears brighter due to higher near infrared reflectivity than soil or dormant plants. Using such infrared-derived indices facilitates quantitative measurement of biophysical variables like leaf area index and evapotranspiration over time. Appropriate color mappings make the often unfamiliar infrared spectral dimensions more interpretable for visual training data labeling as well. More broadly, leveraging the full spectrum sensing capacities of Sentinel-2 via tailored visualization choices enables generation of scientific insights and derived products not possible from the visible bands alone. Multi-modal visualization approaches which transform the raw sensor observations into more perceptually intuitive formats help translate Earth observation data into actionable intelligence around sustainability challenges.
aws_session = AWSSession(boto3.Session(), requester_pays=True)
with rio.Env(aws_session):
    with rio.open('s3://sentinel-s2-l1c/tiles/10/T/FK/2018/11/1/0/B08.jp2') as src:
        nir = src.read()
vmin, vmax = np.nanpercentile(nir, (5,95))  # 5-95% contrast stretch   
plt.figure(figsize=[10,10],num=1, clear=True)
show(nir, cmap='YlGnBu', vmin=vmin, vmax=vmax) #RdYlGn #YlGnBu
plt.show()