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A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a dynamic range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher and smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

This work presents an overview of the activity developed in the frame of a project funded by the European Space Agency (ESA). The research was focused on the study of the potential applications of GNSS Reflectometry (GNSS- R) over land, with an emphasis on soil moisture (SM) and biomass. A study about the sensitivity with respect to the freeze–thaw dynamics was considered as well. The work started with an analysis of the sensitivity of GNSS-R reflec- tivity collected by the TechDemoSat-1 (TDS-1) experimental satellite, although, to a limited extent, the Cyclone GNSS (CyGNSS) constellation was considered as well. The en - couraging sensitivity outcomes led to the development of retrieval algorithms: three different approaches for SM and one for biomass based on neural networks. A more theoretical investigation was carried out to better understand and predict the signal from a satellite platform which required the updating of two different models. To - pography effects and sensitivity to moisture and roughness of a rough soil were included as well as the effect of vegeta- tion cover. The project was carried out by a large team involving different research groups in Europe. It has led to main con- clusions and recommendations derived from a beneficial collaboration and fertilization of ideas. The primary ap - proaches and outcomes are summarized here, including comparisons to the recent literature. This study has been conducted in the framework of the ESA contract 4000120299/17/NL/AF/hh, “Potential of Spaceborne GNSS-R for Land Applications.” The TDS-1 data have been freely collected from the MERRBYS portal at merrbys.co.uk. The CyGNSS and SMAP data were downloaded from the NASA Earthdata portal at podaac.jpl.nasa.gov and nsidc.org/data/smap, respectively. The SMOS data are made available from the ESA through the smos-diss.eo.esa.int website. This work has also been partially supported by the project of the Ministerio de Ciencia, Innovación y Universidades and EU ERDF ref. RTI2018-099008-B-C21/AEI/10.13089/E01100011083, “Sensing with Pioneering Opportunistic Techniques,” and grant MDM-2016-0600 to the CommSensLab-UPC “María de Maeztu” Excellent Research Unit. We thank the ISMN for the availability of soil moisture data.

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.

A significant quantity of space-borne Global Navigation Satellite Systems-Reflectometry (GNSS-R) data over land was made available in the last decade, leading to an increasing interest in the assessment of the potentialities of this new remote sensing technique for land monitoring. In this frame, an electromagnetic simulator, such as the Soil And VEgetation Reflection Simulator (SAVERS), has the key role to support the understanding of the physical mechanism involved in the bistatic scattering and to identify the surface features mainly contributing to the observed signal. Originally developed for ground and airborne GNSS-R observations over homogeneous areas, in this study, SAVERS was upgraded to account for space-borne systems. The new version of SAVERS takes into account the inhomogeneity characterizing the large area observed from space altitudes, due to a variable surface elevation and land cover. Coherent and incoherent scattering and polarization rotation are computed taking into account the local slope and elevation of the surface. The simulator was validated against TechDemoSat-1 observations over a bare surface with a complex topography and over a forested surface with a gentle topography. The validation results show the capability of SAVERS to correctly estimate the effect of the topography, enhancing the understanding of the observations. Moreover, it was found that the sensitivity to soil moisture is independent of the topography (about 1.5 dB for a 10% variation of soil moisture). Whereas a saturation of the GNSS-R reflectivity over a variable topography is reached for lower values of biomass, earlier than in the flat case.

A comparison of three different electromagnetic scattering models for delay-Doppler maps (DDMs) of global navigation satellite system reflectometry (GNSS-R) from land is performed along a Cyclone Global Navigation Satellite System (CYGNSS) track over a validation site in the San Luis Valley, Colorado, USA. The peak reflectivity profiles of all three models and of the corresponding CYGNSS data are found to be in general agreement and are strongly influenced by topography. An intercomparison of DDM structure for one acquisition is also included. Efforts to refine the model results using a high resolution lidar survey are ongoing.

The monitoring of the freeze/thaw dynamic of high-latitude Earth regions is of paramount importance for the study of the carbon cycle and the climate changes. Current approaches essentially rely on the use of active and passive microwave remote sensing, while limited work has been dedicated to study the potential of the Global Navigation Satellite System Reflectometry technique based on spaceborne platforms. In this contribution, reflectivity values derived from the TechDemoSat-1 data have been collected and elaborated, to be compared against the SMAP freeze/thaw product. The proposed analysis indicates a significant seasonal cycle of freeze/thaw state in the calibrated reflectivity, thus opening new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state.

The quasi-specular reflection collected under the illumination of signals of opportunity can exhibit temporal fluctuations related to the characteristics of the surface roughness. Even in quite flat regions, gentle undulations, i.e., those comparable with the wavelength over the vertical scale and much longer over the horizontal one, can exist and can significantly affect the temporal pattern of the signal at the receiver. In this contribution, a full-wave model based on the Kirchhoff approximation is adopted to study and characterize the fluctuation of the scattered field. Numerical simulations are developed, while measurements based on an airborne experiment are introduced to corroborate the concept in realistic conditions. The analysis can give interesting information to help the interpretation of GNSS data and their intrinsic variability, especially those collected by means of satellite platforms.

We present and discuss an electromagnetic model for the description of the coherent scattering from bare soils illuminated by a radar system under arbitrary bistatic geometries. The scattering problem is solved under the Kirchhoff approximation (KA) accounting for both the sphericity of the wavefront of the incident wave and the radiation pattern of the transmitting and receiving antennas. We propose here a general formulation and solution of the scattering problem applicable to an arbitrary bistatic geometry. We discuss and demonstrate the importance of our extension for the characterization of the coherent scattering generated in bistatic radar systems, both inside and outside the plane of incidence. The model is validated against the numerical solution of the Kirchhoff integral and, in the case of the perfect plane conductor, by comparison with the image theory. The work is intended to provide a simple methodology to characterize the coherent normalized radar cross section (NRCS) of a rough surface to be used within the radar equation for extended targets, similarly to what is done for the incoherent component. It aims at enabling a local characterization of the coherent scattering in realistic conditions (e.g., in the presence of inhomogeneous and mountainous surfaces), a feature that is particularly important for practical applications, such as the modeling and understanding of the bistatic scattering generated by sources of opportunity and specifically for Global Navigation Satellite System Reflectometry (GNSS-R) related applications.

With the recent launch of TechDemoSat-1 and CYGNSS missions, spaceborne GNSS-R data are now available. In order to understand the involved scattering mechanisms, potentialities and limitations of GNSS-R measurements over land, an electromagnetic simulator represents a powerful tool. In this paper, the SAVERS simulator, that was developed and validated on ground based and airborne data, is now upgraded to take account for the important role of the topography in the satellite acquisitions. The Delay Doppler Maps acquired by satellite sensors show a clear topography effect. The reliability of the simulator is here tested over a volcanic area of Chad and compared with the TechDemoSat data.

The reflection of radar echoes collected under both monostatic and bistatic configurations generally undergoes losses of coherence. These can be affected by many factors, depending on both the system features and the illuminated surface, and it should be properly considered for an accurate characterization of the scattering phenomenon. In this contribution, we investigate and discuss the spatial coherence of the bistatic signal scattered by a rough soil when illuminated by sources of opportunity. A simple analytical model accounting for the essential geometry of the system and of the sphericity of the illuminated wavefront is presented, and the spatial decorrelation of the signal collected by a moving receiver is analyzed. The analysis can be of great interest for the design of next-generation satellite bistatic missions and, in particular, for the assessment of the potentialities of earth observation based on Global Navigation Satellite System Reflectometry.

A combined Position-Reflectometry Galileo receiver on board a low cost unmanned aerial platform (UAV) was developed in the framework of the Combined Positioning-Reflectometry Galileo Code Receiver for Forest Management project (COREGAL) with the main aims of evaluating the accurate positioning in areas where no GNSS ground infrastructures are available and of investigating the forest biomass mapping. In this context, the Soil and Vegetation Reflection Simulator (SAVERS) was employed to simulate GNSS-R data in order to analyze the GNSS-R sensitivity to biomass, to create a Look Up Table (LUT) of GNSS-R reflectivity and to carry out a biomass retrieval test. SAVERS simulated the mean power of the reflected GNSS-R signals by applying the integral bistatic equation taking into account the scattering contributions from soil and vegetation. It was shown that the GNSS-R sensitivity to forest biomass can be enhanced by filtering out the incoherent component of the signal, i.e. by using a long coherent integration time. A LUT was created by running SAVERS for a realistic range of the input parameters. Moreover, a GNSS-R synthetic dataset was generated for the case of a cork oak open forest in Portugal and a retrieval test was carried out. A neural network with two hidden layers was trained on the LUT and the forest biomass was estimated from the synthetic data. The biomass mean retrieval error was approximately 10 t/ha.

The applications of spaceborne GNSS reflectometry data over land are investigated in this work using the data collected by the UK TechDemoSat experimental mission. In order to quantify the sensitivity of the GNSS reflections to biomass and soil moisture from real data it is necessary to extract a quantity, like the surface reflectivity, as much as possible independent from the system parameters. At the same time, to understand the scattering mechanisms and potentialities and limitations of GNSS-R over land, an electromagnetic simulator has to be used and compared to the experimental data. The available simulator was tuned and validated on ground based and airborne data. The satellite platform poses additional problems due to the low magnitude of the reflected signal and to topography effects. These are discussed in the paper.

The applications of spaceborne GNSS reflectometry data over land are investigated in this work using the data collected by the UK TechDemoSat experimental mission. In particular, the sensitivity of the reflection (including specular coherent reflection and to some extend diffuse incoherent scattering) to soil moisture and forest biomass are preliminary considered. In order to quantify the biomass and moisture sensitivity it is necessary to extract a quantity, like the surface reflectivity, as much as possible independent from the system parameters. We have tried to exploit the direct signal from the uplooking antenna for this purpose and we show differences with respect to other approaches. To understand the scattering mechanisms and potentialities and limitations of GNSS-R over land, an electromagnetic simulator is used and compared to the experimental data. Although the simulator was tuned on ground based and airborne data, the satellite platform poses additional problems due to the low magnitude of the reflected signal and to topography effects. These are discussed in the paper.

Emissivity and backscattering have different sensitivities to soil and vegetation parameters and provide complementary information on the observed scene. For this reason, the main objective of this study was to investigate the potential of the combined use of active and passive microwave signatures to improve the understanding of emission and scattering mechanisms over land. The discrete scattering model developed by the Tor Vergata University, driven by a single set of input parameters, was used to simulate both emission and backscattering. The model accuracy was assessed by comparing modelled emissivity, multiplied by the effective temperature, with C-band AQUA AMSR-E observations and modelled backscattering coefficient with Metop ASCAT observations. The investigation was carried out using datasets collected over the Maqu area, which is located in the north-eastern part of the Tibetan Plateau and is covered by homogeneous grassland with a thin litter layer. In situ soil moisture measurements and MODIS Terra/Aqua 8-days LAI products served as model inputs, whereas the most sensitive other parameters, such as surface roughness and litter and vegetation parameters, were calibrated to obtain the best match between modelled outputs and both ASCAT and AMSR-E 2009 observations. With the calibrated parameters, measured soil moisture and MODIS LAI as input, the model correctly simulated the temporal variations of ASCAT backscattering coefficient, with R2 of 0.9 and rmse of 0.5 dB. The results obtained in the passive case strongly depended on the adopted effective temperature. The best match with satellite observations was found when the surface temperature was obtained from Ka-band AMSR-E data, leading to R2 of 0.8 and rmse of 6 K for H polarisation data and R2 of 0.5 and rmse of 6 K for V polarisation data. The validation carried out using the 2010 data, without any further refinement of the calibrated parameters, showed a slightly worse agreement between modelled and observed signatures. The results showed that the combined use of active and passive microwave signatures contributed to an unambiguous interpretation of emission and scattering processes over the study area, it allowed us to impose constraints to non-available inputs of the Tor Vergata model and it led to correct simulations of the satellite signatures. These findings are important to better understand the potential of the synergistic use of radiometer and radar data, like those to be provided by NASA's SMAP.

This study presents an analysis of temporal behaviour of in situ and satellite-derived soil moisture data. The main objective is to evaluate the temporal reliability of the satellite products, comparing them with in situ data, for applications that would benefit from the use of consistent time series of soil moisture, such as studies on climate and hydrological cycle. The time series, seasonalities, and anomalies of Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) soil moisture and European Remote Sensing (ERS) satellite soil wetness index data sets were analysed over five test sites. The agreement of temporal behaviours and autocorrelation functions and the correlation with in situ data were investigated. A good agreement between the seasonalities of both satellite data sets and in situ data with high correlations (i.e. 0.9) was found over the sites with a large soil moisture variability range and short vegetation cover. Noisier seasonalities were found over sites with small soil moisture variability ranges, affected by radiofrequency interference (RFI) and characterized by croplands. In spite of ERS soil moisture being characterized by a longer time series, the seasonality is much noisier than the AMSR-E products due to the numerous gaps in the data set. The correlation among the anomalies is lower than 0.6, mainly due to the noise in the satellite products. However, the autocorrelation functions show that the anomalies are not random, although noisy. Although the stability of the anomaly correlograms is affected by the relatively short time series available for this study, the analysis shows that there are statistical similarities between the satellite soil moisture anomalies and the in situ data anomalies. The results show that AMSR-E and ERS products are consistent over long time periods and do contain useful information about soil moisture seasonality and anomaly behaviour, although they are affected by noise. © 2013 Copyright Taylor and Francis Group, LLC.

This study presents a method to assimilate leaf area index retrieved from ENVISAT ASAR and MERIS data into CERES-Wheat crop growth model with the objective to improve the accuracy of the wheat yield predictions at catchment scale. The assimilation method consists in re-initialising the model with optimal input parameters allowing a better temporal agreement between the LAI simulated by the model and the LAI estimated by remote sensing data. A variational assimilation algorithm has been applied to minimise the difference between simulated and remotely-sensed LAI and to determine the optimal set of input parameters. After the re-initialisation, the wheat yield maps have been obtained and their accuracy evaluated. The method has been applied over Matera site located in Southern Italy and validated by using the dataset of an experimental campaign carried out during the 2004 wheat growing season. Results indicate that, LAI maps retrieved from MERIS and ASAR data can be effectively assimilated into CERES-Wheat model thus leading to accuracies of the yield maps ranging from 360 kg/ha to 420 kg/ha.

Several efforts, aimed at developing and refining crop backscattering models, have been done during the last years. Although important advances have been achieved, it is recognized that further work is required, both in the electromagnetic characterization of single scatterers and in the combination of contributions. This work is focused on the description of leaf geometry and of the internal structure of stems. Recently developed routines, able to model the scattering cross sections of curved sheets and hollow cylinders, are adopted for this purpose and run within the multiple-scattering model developed at the University of Rome "Tor Vergata." Input parameters are taken from experimental campaigns. In particular, ground data collected over a maize field at the Central Plain site in 1988, over wheat and maize fields at the Loamy site in 2003, and over wheat fields at the Matera site in 2001 and 2003 are considered. The multitemporal backscattering coefficients at C-band are simulated. The results obtained under different assumptions are compared to each other, and with C-band radar signatures collected over the same fields. The influence of some critical factors, affecting crop backscattering, is discussed. It is demonstrated that a more detailed scatterer characterization may improve the model accuracy, especially in the case of hollow stems.

Soil moisture monitoring of the Tibetan Plateau is of primary importance for understanding land–atmosphere interactions of this region and their effects on the climate of eastern and South-East Asia. Operational satellite-derived soil moisture products, such as those obtained from AMSR-E data by VUA–NASA and ASCAT data by TU-Wien, as well as that to become available in the near future (such as data from SMOS and SMAP), can provide the information required, but their accuracy for this region needs to be evaluated before further application. For this reason, a soil moisture and temperature monitoring network was set up in the water source region of the Yellow River, in the north-eastern region of the Tibetan Plateau (Maqu county). It consists of 20 stations distributed, according to a stratified sampling, over an area of approximately 40 km × 80 km. This study describes the Maqu network and presents the first set of data measured from July 2008 to December 2009, which shows the capability of the network to monitor the spatial and temporal soil moisture variability of the area with a high degree of accuracy. Temporal stability analyses revealed that the soil moisture spatial patterns are not always stable in time. The sites that show the highest and the most variable bias with respect to the average are located in regions with extreme soil properties, covering relatively small areas. The weighted spatial average of measured soil moisture was successfully used as ground reference for the validation of the AMSR-E soil moisture products and ASCAT soil wetness index products. For the monsoon season, overall good agreement was found between in situ time series and AMSR-E products, with a linear fit between the two datasets close to the 1:1 line and a standard error of the regression lower than 0.05. The agreement between ASCAT and in situ data was affected by several large variations of the former corresponding to little changes of the latter, thus the standard error of the regression was higher than 0.07.

A plateau scale soil moisture and soil temperature observatory is established on the Tibetan Plateau for quantifying uncertainties in coarse resolution satellite products of soil moisture and soil temperature. The observatory consists of three regional networks across the Tibetan Plateau and provides reliable measurements of mean and variance in soil moisture and soil temperature of representative areas comparable in size to coarse satellite footprints. Using these in-situ measurements, a analysis is carried out to assess the reliability of several satellite products derived from AMSR-E and ASCAT data by three retrieval algorithms (henceforth the AMSR-E products, the ASCAT-L2 products and the ITC-model retrievals) for the first time. For the cold semiarid Naqu area, AMSR-E and ASCAT-L2 products overestimate significantly the regional soil moisture in the monsoon seasons. The ITC-model retrievals are closer to the in-situ measurements but the dynamics in the retrieved time series needs further investigation. The use of these datasets is therefore not recommended for cold semiarid conditions on the Tibetan Plateau. For the cold humid Maqu network area AMSR-E and ASCAT-L2 products have comparable accuracy as reported by previous studies in the humid monsoon period. AMSR-E products significantly overestimate and ASCAT-L2 products underestimate the soil moisture in the winter period. The ITC-model retrievals underestimate the soil moisture in general. It is concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions – use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.

A plateau scale soil moisture and soil temperature observatory is established on the Tibetan Plateau for quantifying uncertainties in coarse resolution satellite and model products of soil moisture and soil temperature. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) consists of three regional scale in-situ reference networks, including the Naqu network in a cold semiarid climate, the Maqu network in a cold humid climate and the Ngari network in a cold arid climate. These networks provide a representative coverage of the different climate and land surface hydrometeorological conditions on the Tibetan plateau. In this paper the details of the Tibet-Obs are reported. To demonstrate the uniqueness of the Tibet-Obs in quantifying and explaining soil moisture uncertainties in existing coarse satellite products, an analysis is carried out to assess the reliability of several satellite products for the Naqu and the Maqu network areas. It is concluded that global coarse resolution soil moisture products are useful but exhibit till now unreported uncertainties in cold and semiarid regions – use of them would be critically enhanced if uncertainties can be quantified and reduced using in-situ measurements.