Active 3D Imaging of Vegetation based on MultiWavelength Fluorescence LiDAR. Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3Dstate information. Active laserinduced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new MultiWavelength Fluorescence LiDAR (MWFL) system. The system extended the multichannel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetationmonitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the systemxe2x80x99s performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.

The SolarInduced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) Onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS1): Specifications and Prospects The global monitoring of solarinduced chlorophyll fluorescence (SIF) using satellitebased observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670xe2x88x92780 nm was recommended to obtain the SIF at both the red and farred bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signaltonoise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a rootmeansquared (RMS) diff* value of 0.15 mW mxe2x88x922 srxe2x88x921 nmxe2x88x921 at the farred band and 0.43 mW mxe2x88x922 srxe2x88x921 nmxe2x88x921 at the red band. This compares with 0.20 and 0.26 mW mxe2x88x922 srxe2x88x921 nmxe2x88x921 at the farred band and 0.62 and 1.30 mW mxe2x88x922 srxe2x88x921 nmxe2x88x921 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW mxe2x88x922 srxe2x88x921 nmxe2x88x921 at the farred band and 0.47 mW mxe2x88x922 srxe2x88x921 nmxe2x88x921 at the red band. Therefore, the SIFIS onboard TECIS1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space.