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Global Navigation Satellite System Reflectometry (GNSS-R) is a valuable tool for the remote sensing of forest biomass, which is essential for the understanding of the hydrological and carbon cycles.
The experiments carried out from 2008 to 2012 during the ESA projects GRASS and Leimon demonstrated that the GNNS-R signal is correlated to the Aboveground Biomass (AGB) with a monotonical decrease when AGB increases: the sensitivity to AGB observed during the GRASS experiment was around 1.5 dB/(100 t/ha) with a correlation coefficient of 0.91. Moreover, a saturation effect was not observed within the AGB values considered in the analysis. This does represent a major improvement with respect to conventional monostatic radars, as the backscattering coefficient at L-band is reported to saturate within the 100–150 t/ha range, depending on the type of forest.
This study aims at exploiting the sensitivity of the GNSS-R signal to forest biomass and to assess the possibility of estimating globally the above parameter using data from the satellite missions TechDemoSat-1 (TDS-1), launched by Surrey Satellite Technology Ltd. in 2014, and the NASA’s Cyclone GNSS (CyGNSS), launched in 2017.
For evaluating the sensitivity of the data of these two sensors to forest biomass, some test areas have been identified worldwide, as representative of the most important forest types, from boreal to equatorial forests.
For these areas, two TDS parameters, namely Reflectivity and Signal Noise Ratio (SNR), have been analysed and compared with the Woody Volume (t/ha) derived from ALOS data by using the ANN inversion algorithm proposed by Santi et al. (2017).
A further sensitivity analysis was carried out by comparing TDS Reflectivity and ALOS backscattering to the Vegetation Optical Depth (VOD) obtained from SMAP (Soil Moisture Active Passive) sensor for all the available areas. The same comparison has been repeated at a global scale by using CyGNSS data.
In addition, TDS and CyGNSS sensitivities to AGB have been also evaluated by comparison with the improved pan-tropical biomass map (t/ha) proposed by Avitabile et al. (2016)
Based on the obtained results, the potential of both TDS and CYGNSS in estimating the forest biomass has been exploited, by implementing and testing retrieval algorithms based on Artificial Neural Networks (ANN).
The analysis is still in progress, however the obtained results in the retrieval of both VOD and AGB from data from TDS-1, ALOS2, and CyGNNS, are encouraging, as it has been pointed out by correlation coefficients (R) ranging between 0.81 and 0.92 and RMSE between 0.1 and 0.17 for VOD retrievals and RMSE ≃76 t/ha for AGB retrieval.
References
Santi E., S. Paloscia, S. Pettinato, G. Fontanelli, M. Mura, C. Zolli, F. Maselli, M. Chiesi, L. Bottai, G. Chirici, 2017, The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas, Remote Sensing of Environment, 200 (2017), pp. 63–73.
Avitabile V, Herold M, Heuvelink G, Lewis SL, Phillips OL, Asner GP et al. (2016). An integrated pan-tropical biomass maps using multiple reference datasets. Global Change Biology, 22: 1406–1420. doi:10.1111/gcb.13139.
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