Derived from the travel path of photons, Yelu Zeng proposed for the first time the FluorRTER, an analytical 3D fluorescence radiative transfer model, and the NIRv-based practical approach for the normalization of fluorescence satellite view-angle effect, which can reduce the uncertainty caused by the change of satellite view-angle effects from about 30% to within 5%. This breakthrough result was regarded as a key innovation for the remote sensing theory and method by NASA Jet Propulsion Laboratory and the California Institute of Technology and was adopted as one of the three standard methods for satellite fluorescence view-angle corrections.
This method can be used in view-angle corrections for multiple fluorescent satellites, thus making substantial contributions to the application of the new generation of satellites.
Plant photosynthesis monitored by satellite not only enables estimation of the fixed carbon and the global carbon sink distribution but also promotes smart agriculture and rural revitalization by diagnosing plant health, thus providing early warning for national food security.
Zeng’s method is based on satellite fluorescence and surface reflectance monitors and evaluates agricultural disasters such as drought and scorching weather, therefore can provide a great reference value for relief projects. It can detect major events threatening food security, such as the “non-food” and “non-agricultural” phenomena of cultivated lands. In addition, it can also serve an important role in dealing with biological invasions of alien species.