# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 2 Research Status
### Spaceborne GNSS-R Observation Experiment  
The TechDemoSat-1 (TDS-1) satellite was successfully launched on 8 July 2014. It is a scientific demonstration satellite with low time resolution (~6 months), and it has now finished its work. The satellite is equipped with a GNSS reflected signal receiver SGR-ReSI (Space GNSS Receiver Remote Sensing Instrument). [PERSON] et al. have used TDS-1 to qualitatively analyze the effects of soil moisture, roughness, terrain, and volume scattering on GNSS-R data [34]. At the same time, the sensitivity of soil moisture on different ground surfaces around the Earth and large-scale NDVI (Normalized Difference Vegetation Index) to GNSS-R scattering energy was analyzed. They pointed out the feasibility of using spaceborne GNSS-R for vegetation monitoring [34]. [PERSON] et al. used SMAP's reflectometer model, i.e., SMAP-R (SMAP-Reflectometry) for the first time to study biomass and soil moisture [35]. The GPS L2 (fc = 1226.7 MHz) scattered signal is received by the SMAP dual-polarization (H polarization and V polarization) radar receiver, and the DDM (Delay Doppler Map) waveform of the corresponding observation area is obtained through signal processing. It should be noted that the final equation to get the GNSS-R signal is obtained from the integral form of bistatic radar equation and the output of correlation power is DDM waveform. They used the DDM derived observables, such as SNR, polarization ratio, waveform leading-edge LES (Leading Edge Slope), and trailing edge slope TES (Trailing Edge Slope), to analyze the terrain and biomass sensitivity in the Amazon and in northern forests. The Pearson linear correlation coefficient between the soil moisture retrieved by SMAP-R and the soil moisture obtained by the SMAP radiometer is r \(\approx\) 0.6; the average value of the H-polarized SNR is different for different surface types. The waveform derived parameters are sensitive to seasonally changing surface parameters (NDVI and SWI (Soil Water Index)); as the biomass increases (100 ton/ha to 350 ton/ha), the waveform widths of the LES and TES are reduced. Compared with NDVI, biomass is more sensitive to GNSS-R signals (SNR, polarization ratio, LES, and TES). In areas with rough ground or high biomass, GNSS signals will depolarize. [PERSON] et al. used CYGNSS L1 data to simulate and analyze the relationship between GNSS-R observations and forest biomass [36]. The comparative analysis data are the AGB map and the forest canopy height data of ICESat-1/GLAS data. The results of the polynomial regression in the Congo and Amazon regions show that when the forest biomass is lower than ~400 ton/ha, when the biomass increases, the TES and reflectance both decrease. The article also considers the influence of elevation angles on biomass retrieval [36]. They analyzed the relationship between reflectivity and TES and biomass at 10\({}^{\circ}\) intervals and pointed out that biomass was affected by elevation angle due to the effect of different scattering mechanisms. It also points out the influence of surface scattering and volume scattering on TES and effective reflectivity in different biomass. However, it only analyzes from a large amount of observation data, and only uses a 10\({}^{\circ}\) interval analysis for the angle, without considering the influence of the scattering azimuth angle. [PERSON] et al. used TDS-1 and CYGNSS data to study forest biomass: the corrected DDM peak was used to determine the equivalent reflectance, and the data were used to analyze the sensitivity of local and global forest parameters and believed that this parameter includes not only coherent scattering but also incoherent scattering [37]. Research and analysis show that as the forest biomass increases, the equivalent reflectance decreases. The inversion method is based on a machine learning method, specifically an artificial neural network. The use of incident angle and SNR data can help to improve the inversion accuracy.