Soil moisture content (SMC) and above-ground biomass (AGB) are key parameters for the understanding of both the hydrological and carbon cycles. From an economical perspective, both SMC and AGB play a significant role in the agricultural sector, one of the most relevant markets worldwide. This paper assesses the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to soil moisture and vegetation biomass from an experimental point of view. For that, three scientific flights were performed in order to acquire GNSS reflectometry (GNSS-R) polarimetric observations over a wide range of terrain conditions. The GNSS-R data were used to obtain the right-left and right-right reflectivity components, which were then georeferenced according to the transmitting GNSS satellite and receiver positions. It was determined that for low-altitude GNSS-R airborne platforms, the reflectivity polarization ratio provides a highly reliable observable for SMC due to its high stability with respect to surface roughness. A correlation coefficient of 0.93 and a sensitivity of 0.2 dB/SMC (%) were obtained for moderately vegetated fields with a surface roughness standard deviation below 3 cm. Similarly, the copolarized reflection coefficient shows a stable sensitivity to forest AGB with equal to 0.9 with a stable sensitivity of 1.5 dB/(100 t/ha) up to AGB values not detectable by other remote sensing systems.

The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.

Remote sensing of vegetation by using active microwave sensors is important for the management of land and water resources. Microwave radiation at X-band penetrates only the upper part of the canopy; thus, radar backscattering comes mainly from the top vegetation layer, making the scattering from soil almost negligible. Fourteen in situ measurement campaigns were carried out during which sixteen SAR images of COSMO-SkyMed and TerraSAR-X were acquired on the test site of Merguellil Basin, in the centre of Tunisia, from March to May 2012. A clear sensitivity of the backscattering coefficient, measured by both sensors, to the leaf area index (LAI) of green plants of wheat and barley (at both HH and VV polarizations) was observed, and it did not seem to be greatly affected by the variations in soil moisture, even in HH polarization. As expected, the sensitivity to LAI of dry plants was, instead, almost negligible. The decreasing trend of backscattering as vegetation grows, which has already been observed in past investigations, also at lower frequencies, was confirmed. Due to the similar geometry and dimensions of the two crop types, none of the sensors succeeded in separating the two crop types.

In this study, the capability of GNSS Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of SSTL and from the 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. The analysis has been extended by using CyGNSS to a global scale. The reflectivity has been confirmed as the most suitable parameter and its sensitivity to forest has been evaluated by a comparison with vegetation optical depth (VOD) from the SMAP L-band radiometer, with a pan-tropical aboveground biomass (AGB) map and with a tree height (H) global map derived from the GLAS laser altimeter on-board ICEsat. The analysis confirmed the decrease of reflectivity for increasing biomass, with correlation coefficients 0.31 ≤ R ≤ 0.54 depending on the parameter (VOD, AGB or H) and on the considered dataset. These correlations were not sufficient to retrieve the parameters by simple inversion. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., incidence angle, SNR). Although not directly correlated to AGB, 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.