Abstract
In this study, the capability of Global Navigation Satellite System Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and from the Cyclone Satellite System (CyGNSS) mission of NASA. The analysis has been first conducted using TDS-1 data on a local scale, by selecting five test areas located in different parts of the Earth's surface. The areas were chosen as examples of various forest coverages, including equatorial and boreal forests. Then, the analysis has been extended by using CyGNSS to a global scale, including any type of forest coverage. The peak of the Delay Doppler Map calibrated to retrieve an “equivalent” reflectivity has been exploited for this investigation and its sensitivity to forest parameters has been evaluated by a direct comparison with vegetation optical depth (VOD) derived from the Soil Moisture Active Passive L-band radiometer, with a pantropical aboveground biomass (AGB) map and then with a tree height (H) global map derived from the Geoscience Laser Altimeter System installed on-board the ICEsat satellite. The sensitivity analysis confirmed the decreasing trend of the observed equivalent reflectivity for increasing biomass, with correlation coefficients 0.31 ≤ R ≤ 0.54 depending on the target parameter (VOD, AGB, or H) and on the considered dataset (local or global). These correlations were not sufficient to retrieve the target parameters by simple inversion of the direct relationships. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., the incidence angle, the signal to noise ratio, and the lat/lon information in case of global maps) to the algorithm. Although not directly correlated to the biomass, these inputs helped in improving the retrieval accuracy. The algorithm was tested on both the selected areas and globally, showing a promising ability to retrieve the target parameter, either AGB or H, with correlation coefficients R ≃ 0.8.
Highlights
G LOBAL Navigation Satellite System (GNSS) Reflectometry (GNSS-R) represents a valuable tool for remote sensing key bio-geophysical parameters [1], [2]
The scatterplot of Γ from both Cyclone GNSS (CyGNSS) and TDS-1 as a function of aboveground biomass (AGB) obtained by grouping the data from the three areas is shown in Fig. 5: Despite the aforementioned limitations inherent the “static” AGB map, the results shown in Fig. 5(a) can be considered encouraging
The capability of GNSS reflectometry from space to retrieve forest biomass and other related parameters has been evaluated by analyzing data from the TDS-1 and the NASA
Summary
G LOBAL Navigation Satellite System (GNSS) Reflectometry (GNSS-R) represents a valuable tool for remote sensing key bio-geophysical parameters [1], [2]. In [24], the authors analyzed the relationship between biomass and tree height by computing the zero Doppler waveform trailing edge (TE) from CyGNSS data Based on these results, this study aims at further investigating the sensitivity of the GNSS-R signal collected from space to vegetation biomass, and especially at assessing the possibility of estimating this parameter using data from satellite, namely those delivered by TDS-1 and CyGNSS. The local analysis considered the peak of the Delay Doppler Map (DDM) from which two observables were derived, i.e., an equivalent reflectivity (Γ) and the signal to noise ratio (SNR), confirming that Γ is the parameter more correlated to the vegetation biomass, as previously demonstrated for soil moisture (e.g., [25]).
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