Vegetation canopy height (CH) is one of the important remote-sensing parameters related to forests’ structure, and it can be related to the biomass and the carbon stock. Global navigation satellite system-reflectometry (GNSS-R) has proved capable to retrieve vegetation information at a moderate resolution from space (20–65 km) using L1 C/A signals. In this study, data retrieved by the airborne microwave interferometric reflectometer (MIR) GNSS-R instrument at L1 and L5 are compared to the Global Forest CH product, with a spatial resolution of 30 m. This work analyzes the waveforms (WFs) measured at both bands, and the correlation of the waveform width and the reflectivity values to the CH product. A neural network algorithm is used for the retrieval, showing that the combination of the reflectivity and the waveform width allows to estimate the CH information at a very high resolution, with a root-mean-square error (RMSE) of 4.25 and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual CH.

The Microwave Interferometric Reflectometer (MIR) is an airborne GNSS-R instrument developed by Universitat Politècnica de Catalunya. In 2018, it was flown twice over the agricultural Yanco area, New South Wales, Australia, once after a very dry period, and a further time the day after a strong rain event. This rain event resulted in many crop fields being entirely flooded, producing a saturation in the GNSS-R reflectivity value. In this work, the received data set is processed to identify the optimum integration time with the goal to minimize pixel blurring. This issue is assessed for airborne conditions, and then extra-polated to the spaceborne case. The presented results show that the blurring of the GNSS waveform is produced even from an airborne sensor with short integration times. Following the determination of an optimal integration time for the platform in use, the surface roughness term in the reflectivity equation can be isolated due to the signal saturation during very wet surface conditions. The final results from the two channels (L1 C/A and L5) are subsequently presented. In this case, it is shown that most reflectivity variations in GNSS-R measurements are linked to surface roughness and Speckle noise fluctuations rather than soil moisture changes.

In this article, an algorithm for snow depth (SD) and snow water equivalent (SWE) retrieval is proposed based on a polarimetric synthetic aperture radar (SAR) decomposition model and field measured snow data. The field campaigns were conducted at the Dhundi observatory (in the Indian Himalaya) in January 2016 and 2018. The field-measured data are used here to build a linear regression between wetness ( w) and the imaginary part of the snow permittivity ( ε''), and the validation of retrieved SD and SWE. The snow density ( ρs) and w are calculated with a generalized volume parameter derived using a theoretical model and SAR data (coherency matrix). These snow parameters and the field-based regression relating w and ε''are eventually used for the SD and SWE retrieval. Three TerraSAR-X scenes of the quad-polarization X-band data acquired in January 2016 are used to study the effect of the snow conditions on the accuracy of the proposed algorithm. The mean absolute error (MAE), root-mean-square error (RMSE), and index of agreement (IOA) for SD are 4.84 cm, 5.12 cm, and 0.71, respectively. On the other hand, for SWE, it is 1.42 cm, 1.53 cm, and 0.71, respectively.

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set.

As compared to the using L1C/A signals, L5/E5a Global Navigation Satellite System - Reflectometry (GNSS-R), gives improved resolution over the Earth's surface due to the sharper auto-correlation function. Furthermore, the larger transmitted power (+3dB with respect to L1 C/A), and correlation gain (+40dB) allows the reception of weaker reflected signals. If high directivity antennas are used, very short incoherent integration times are needed to have enough signal-to-noise (SNR) ratios, allowing the reception of multiple specular reflection points such as crest of consecutive waves without the blurring induced by long incoherent integration times. This study presents for the first time experimental evidence of the wind and swell waves signatures in the GNSS-R waveforms, and compares them with models.

Global Navigation Satellite Systems Reflected (GNSS-R) signals exhibit an incoherent and a coherent components [1], [2]. Current models assume that one or the other are dominant, and the calibration, and geophysical parameter retrieval (eg. wind speed, soil moisture ...) are developed accordingly. Even the presence itself of the coherent component of a GNSS reflected signal has been a matter of discussion in the last years. In this work, the method used in [3] to separate the leakage of the direct signal from the reflected one is applied to a set of GNSS signals reflected collected over the ocean by the MIR [4], [5], an airborne dual-band (L1/E1 and L5/E5a), multi-constellation (GPS and Galileo) GNSS-R instrument with two 19-elements array with 4 beam-steered each. The results presented demonstrate the feasibility of the proposed technique to untangle the coherent and incoherent components in GNSS reflected signals. This technique allows the processing of these components separately, which will increase the calibration accuracy (as today both are mixed together), and allows high resolution applications since the spatial resolution of the coherent component is determined by the size of the first Fresnel zone [6] (300-500 meters from a LEO satellite), and not by the size of the glistening zone (~25 km from a LEO satellite).

Spatial fire spread models simulate the progression of a wildfire across the land under given meteorological conditions. As such, they can improve the fire-fighting real-time response and the rural planning of fire prone areas. However, fire-spread models require high-resolution information of vegetation, which is often difficult to acquire, as spatially variant growth, but also past fire occurrences impact on the spatial variability of the vegetation. Hence, forests often consist of a patchwork of vegetation in different growth stages, which is impossible to consistently map from the ground. In order to address this problem, the Vegetation Structure Perpendicular Index (VSPI) is introduced, here, which is a spatially and temporally continuous proxy for fuel structure derived from Landsat data. Forest age maps are derived by fitting post-fire VSPI time series to an exponential decay curve. The forest age maps derived from VSPI are then used as input into a rate-of-spread model to predict the fire spread of the 2003 Mt Cooke wildfire in Western Australia, as a proof-of-concept.

Over the last decade, many snowpack parameter retrieval algorithms utilizing C- and X-band Synthetic Aperture Radar (SAR) data have been proposed. These algorithms are capable of retrieving snow density and snow wetness accurately, but not Snow Water Equivalent (SWE). In this paper, a novel approach of SWE estimation is proposed based on TerraSAR-X full-polarimetric data. A unique relationship between the extinction coefficient of the snowpack and snow wetness is built and verified with field data. This relationship enables the retrieval of Snow Depth (SD) and SWE using existing snow density algorithm and volume scattering power. The validation of SD and SWE is carried out with near-real-time ground truth measurements. The accuracy assessment for SD showed Mean Absolute (MAE) and Root Mean Squared (RMSE) Errors of 15 cm and 16 cm, respectively. On the other hand, SWE retrievals resulted in MAE and RMSE of 22.7 mm and 30 mm, respectively.

The intensity of a wildfire depends in large part on the fuel available to the fire, including surface bark, leaf litter, and over- and understorey vegetation. The amount and configuration of these fuels are affected by cycles of destruction during fires and slow regrowth. Current fuel amounts, required for predictions of future fires, are therefore difficult to quantify as past fire history and fuel recovery must be taken into account. Most fire services rely on visual inspections of forest recovery, however, these are often subjective and very labour-intensive. Remote sensing offers an automated alternative with the potential for rapidly assessing fuel state over large areas allowing for continuous monitoring over time. This paper describes a new index for measuring temporal fuel state, called the Vegetation Structure Perpendicular Index (VSPI). The index provides an alternative to the Normalized Burn Ratio (NBR), which is prone to potentially large uncertainties due to large levels of seasonal dynamics. The VSPI estimates a vertically integrated burn severity and ecosystem recovery measurement for forested areas. The method is applied in this paper to a fire in Western Australia, the 2005 Perth Hills fire to demonstrate the capabilities and benefits of this new index. For this fire the VSPI provides a better estimate of fire severity and shows fuel disturbance levels for two years longer than the NBR, providing an improved estimation for post-fire vegetation recovery and vegetation condition assessment suitable for fire predictions.

Global Navigation Satellite Systems - Reflectometry (GNSS-R) has shown his potential for remote sensing. Since new wider band signals are being broadcasted, and new systems are becoming available, a performance comparison in fair conditions between signals and system is required. For this purpose, the Universitat Politecnica de Catalunya has been developing the MIR instrument, an airborne GNSS reflec-to meter that mimics PARIS-IOD, and that also aims to better understand some GNSS-R techniques, the RFI analysis and mitigation, and the use of beamforming techniques to improve the GNSS-R capabilities. This work presents the advances in the instrument, the end-to-end tests, and the field campaign being planned in Australia for Spring, 2018.

In this study, a methodology to retrieve Snow Water Equivalent (SWE) information for shallow snow coverage is proposed, something where traditional methods fail, as they require more snow depth to function properly. The proposed algorithm uses the thermal resistance of snowpack to retrieve the SWE by linearly relating the thermal resistance to the backscattering ratios of winter and autumn (snow-free) images acquired with C-band SAR (Sentinel-lA). The SWE (absolute value) is then estimated from the thermal resistance. The results are verified using field data collected at Dhundi observatory maintained by the Snow and Avalanche Study Establishment (SASE), located in the Himalaya (Himachal Pradesh, India; 32°21'N and 77°07'). One winter and two autumn images were used to check the feasibility of the algorithm. The RMSE of SWE estimated using the retrieval algorithm is 3 cm for Pair-l (30 Jan 2016 and 02 Oct 2015), and 2.8 cm for Pair-2 (30 Jan 2016 and 20 Oct 2016).

A number of satellite missions have the capability to provide surface soil moisture information at a range of spatial and temporal scales. However, the validation of such products heavily relies on point measurements from permanent stations, which may or may not be representative of the larger scale soil moisture conditions. Hence, methods need to be developed that allow the sampling of surface soil moisture on the ground across large scales over a reasonably short time scale, in order to capture the spatial variability within a footprint, or to provide spatially sufficiently large data sets to validate high-resolution products, be they at their native resolution or downscaled. In this study, two field-scale ground sampling techniques, namely stationary and roving Cosmic Ray Probes, are compared against a high-resolution satellite product. The data are compared for their temporal performance as well as with a focus on capturing the correct spatial variability. The challenge is the inherently different sensing depth of the various technologies. It is shown that this may largely be overcome through scaling the products.

A time series multi-angular method was presented towards combining space-borne radar data acquired from both descending and ascending orbits with different observation modes in soil moisture retrieval. Inherit from multi-temporal based retrieval methods, the method assumes time-invariant roughness and vegetation, but not requires incidence angle normalization. The numerical Maxwell model of three-dimensional simulations and distorted Born approximation (NMM3D-DBA) were used to build a set of multi-angular data cubes (3 dimension look up table). Genetic algorithm (GA) was used to minimize the difference between data cubes and radar observations with the constraint of drying down soil moisture. Evaluation based on the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) dataset shows an overall root mean square error (RMSE) of 0.07 cm3/cm3at the 50-m pixel scale.

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there has been no comprehensive intercomparison of the alternate approaches. This research undertakes an assessment of the different downscaling approaches using the SMAPEx-4 field campaign data.

Wireless sensor network (WSN) plays an important role in monitoring applications in many areas. This paper presents a WSN called EN-Nets which is based on a newly designed small-size and low-power sensor node (EN-Node) for monitoring real-time environmental conditions. Detailed design and implementation of the sensor node for sensing and communicating environmental parameters are described. A power management method utilizing transistor switch connections to sensors to control their operations was used to reduce the overall power consumption of each sensor node. The important testing results for the proposed sensor board are discussed, such as routing method, transmission packet delay and sensors' performance. A comprehensive and interactive graphical user interface program has been developed to allow users to interact with the sensor network system. A real-time deployment has been performed in a campus area and presented in here.

Three active-passive soil moisture downscaling algorithms are tested to demonstrate the feasibility of each for application to NASA's Soil Moisture Active Passive (SMAP) mission launched in January 2015. These algorithms include the official baseline and optional downscaling algorithms, and a change detection method. These synergistically use 1-km synthetic aperture radar backscatter to downscale 36-km brightness temperature to 9 km, which is then converted into soil moisture at 9 km, or downscale soil moisture directly to 9-km resolution. While these algorithms have been tested previously, this was mostly using synthetic data sets. Moreover, there has never before been a direct comparison of the alternate methods using the same data sets. Thus, it is imperative that they be tested against each other for a comprehensive range of land surface conditions prior to global application. Consequently, this letter evaluates these three algorithms using data collected from the soil moisture active passive experiments (SMAPExs) in Australia, designed to closely simulate the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. Results suggested that the average root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.019, 0.021, and 0.026 cm3/cm3for the baseline, optional, and change detection method, respectively. While there was a little difference in the RMSE, the optional method showed the best correlation between the downscaled soil moisture and the reference soil moisture map. Therefore, the optional algorithm is recommended for global implantation by SMAP.

The National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) mission, launched in January 2015, was designed to provide a global soil moisture product at medium resolution (~9 km), by combining observations from its radar and radiometer. Several downscaling methods have been proposed by the SMAP team for this purpose. This paper evaluates another candidate downscaling method, namely, the Bayesian merging approach. While this has been tested using a synthetic data set across the USA, it is imperative that it can also be tested using the experimental data for a comprehensive range of land surface conditions (i.e., in different hydro-climatic regions) prior to a global application. Consequently, this paper applies this method using the data collected from SMAP experiments field campaigns in southeastern Australia that closely simulated the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. The method studied here differs from the linear downscaling methods of the SMAP mission, in that it uses a nonlinear method based on Bayes' theorem. The medium-resolution soil moisture product is obtained using background soil moisture estimates that are updated according to the difference between the observed and predicted brightness temperatures and backscatter coefficients, relating the highand low-resolution data. Results were assessed against a reference soil moisture map derived from high-resolution airborne radiometer observations. The rootmean-square-error and R2for the Bayesian merging method were found to be 0.02 cm3/cm3and 0.55, respectively, at 9-km resolution, being similar to the SMAP's “optional” downscaling method.

The L-band (1 - 2 GHz) microwave remote sensing has been widely acknowledged as the most promising method to monitor regional to global soil moisture. Consequently, the Soil Moisture Active Passive (SMAP) satellite applied this technique to provide global soil moisture every 2 to 3 days. To verify the performance of SMAP, the fourth and fifth campaign of SMAP Experiments (SMAPEx-4 & -5) were carried out at the beginning of the SMAP operational phase in the Murrumbidgee River catchment, southeast Australia. The airborne radar and radiometer observations together with ground sampling on soil moisture, vegetation water content, and surface roughness were collected in coincidence with SMAP overpasses. The SMAPEx-4 & -5 data sets will benefit to SMAP post-launch calibration and validation under Australian land surface conditions.

For Earth observation data to be useful for a wide range of land surface applications, a kilometer or finer resolution is required. Unfortunately, passive microwave observations at low microwave frequencies (1-10 GHz), already providing important information on soil moisture and vegetation dynamics, are generally only available at a resolution of tens of kilometers. This letter presents a new downscaling method relating L-band radiometer and C-band radar observations for downscaling purposes. The data were obtained from two extensive airborne field experiments across a 80 000-km2catchment in south-eastern Australia and coinciding Envisat Advanced Synthetic Aperture Radar acquisitions, performed during the Austral summer and spring of 2010. The novel approach of this study is in the downscaling of coarse-scale emissivities as observed by the radiometer with a new interpretation of the change detection methodology for the radar signal to relate the spatiotemporal changes of those two types of observations at 1 km. It is shown that, for most land surface conditions, a good spatial representation at high resolution is achieved, without considering land surface specific parameterizations, which is promising for using very high resolution radar data from the Sentinel-1 platform for downscaling of passive microwave data from current missions, such as National Aeronautics and Space Administration's Soil Moisture Active Passive and European Space Agency's SMOS.

Fires are a concerning topic in Mediterranean areas. They are increasing in number and extension, probably due to the anomalous dry and hot conditions experienced in this region in the last decade. In this study, more than 2,000 fires that took place in the Iberian Peninsula (2010-2014) were analyzed. The new all-weather version of SMOS-derived soil moisture product at fine scale resolution, as well as ERA-Interim Skin Temperature datasets, were used. Soil moisture and temperature anomalies based in these datasets were computed and included in the database. These information allowed analyzing prior-to-fire conditions. Results reported that more than 70% of fires started under dry and hot conditions, and this percentage rose till 94% in the anomalous conditions prior to the biggest fires. A relation between soil moisture, temperature and burned area is found which could set the basis for a fire risk index based on SMOS data and temperature information.

A brightness temperature (Tb) downscaling algorithm based on the synergy between active and passive microwave observations is tested using airborne data that simulate the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration scheduled for launch in January 2015. While this algorithm has been adopted as the baseline for SMAP, it has only been tested on a limited variety of land uses and vegetation types. Consequently, this study evaluates the SMAP active/passive downscaling algorithm using data with varied conditions. The SMAP experiment conducted in Australia has been used for this purpose. The algorithm was applied over several 9 km × 9 km pixels with different land covers, so as to evaluate the accuracy of this algorithm under different heterogeneity levels. Brightness temperatures were downscaled from 9 to 3 km (approximating the resolution ratio of SMAP downscaling approach) across nine days of data. Results show that the root-mean-square error of Tb in grassland could meet the 2.4-K target accuracy of SMAP, while in cropping, it was 2 K higher than the target. The influence from water bodies was also assessed and confirmed to have a significant impact if not removed prior to downscaling.

NASA's Soil Moisture Active Passive (SMAP) mission will provide a ~10-km resolution global soil moisture product with a 2-3-day revisit by exploiting the synergy between active and passive observations. However, soil moisture downscaling techniques required to exploit this synergy have not yet received extensive testing, being limited to mostly synthetic data. Consequently, airborne field campaigns such as the SMAP Experiments (SMAPEx) have been designed to provide experimental data to fill this gap. The objective of this study is to assess the reliability of SMAP prototype data stream derived from airborne observations, with the aim of providing a simulated SMAP data set for prelaunch algorithm development of SMAP. Specifically, the reliability of incidence-angle normalization and spatial resolution aggregation for airborne observations was assessed for this purpose. The impact of azimuthal angle on active-passive observations was analyzed to assess the potential influence of SMAP rotating antenna on observations. Results showed that the accuracies of angle normalization were ~0.8 dB for active and 2.4 K for the passive observations (1-km resolution), while the uncertainties associated with spatial upscaling were 2.7 dB (150-m resolution) and 2 K (1-km resolution). Although azimuthal signatures associated with the variable orientation of surface features were observed in the high-resolution observations, these tended to be smoothed when aggregating to coarser resolution. As these errors are expected to decrease further at the coarser resolution of SMAP, results suggested that data from SMAPEx can be reliably used to simulate SMAP data for subsequent use in active-passive soil moisture algorithm development.

Microwave remote sensing has been widely acknowledged as the most promising technique to measure the spatial distribution of near-surface soil moisture. However, due to a strong incidence angle dependence in microwave radiometer and radar data, airborne observations typically have an across-track variation in incidence angle that needs to be normalized to a fixed angle for the purposes of data visualization and aggregation to spatial resolutions that mimic spaceborne data. There are two normalization methods commonly used, often resulting in a noticeable stripe pattern along the flight direction. This paper develops a 2-D cumulative distribution function (CDF)-based normalization method, which normalizes the variable-angle observations to a reference angle by matching the CDF of observations for each nonreference angle, using the information content from multiple partially overlapped swaths. The performance of this method is tested using an airborne microwave radiometer and radar observations collected during three Australian field experiments. The normalization results show that the stripe pattern problem over heterogeneous land surfaces when not any prior knowledge of land surface types is primarily attributed to the linearity of the commonly used normalization methods, and that the nonlinear 2-D CDF-based method produced the least noticeable stripe pattern and the highest normalization accuracy when compared with independent data. Compared with the two linear methods, a root-mean-squared error improvement of up to 2 K was obtained using 1-km radiometer data, and a correlation coefficient improvement of 0.2 and RMSE improvement of ~0.2 dB were achieved for the 7-m resolution radar data.

Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) has proved to be a useful technique for the estimation of Soil Moisture (SM). In the past 10 years, different techniques such as the Interference Pattern Technique (IPT), the Interferometric Complex Field (ICF) or power measurements of direct and reflected GNSS signals have been used. This work presents a reflectometer concept that can be used for air-borne, Unmanned Aerial Vehicle (UAV), car-borne, and ground-based measurements. It also presents the hardware implementation and the data acquisition scheme. An algorithm for the estimation of the reflectivity of the surface under observation has been developed and compared to concurrent radiometric measurements. Initial results from an airborne field experiment have shown a good correlation between both data sets.

Since 2003 several field experiments using Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) have demonstrated the feasibility of retrieving Soil Moisture (SM) from GNSS-R observations. Different techniques such as the power difference between direct and reflected signals, the Signal to Noise Ratio (SNR)-analysis method, the Interference Pattern Technique (IPT) or the Interferometric Complex Field (ICF) have been used. The conventional IPT was first proposed in 2008, and consisted on forcing a single multi-path using a vertically polarized GNSS antenna with a rotationally symmetric pattern pointing to the horizon. In this work the conventional IPT is extended to dual-polarization, horizontal (H-pol) and vertical (V-pol), in attempt to increase the accuracy in the SM retrievals. In this case, the Brewster angle is estimated from the phase difference between the Hand V-Pol interference patterns. The use of dual-polarization measurements is not sensitive to surface roughness and it is more precise in the determination of the Brewster angle position. Results from a field experiment at the Yanco site, New South Wales, Australia, are shown to demonstrate the concepts proposed in this work.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and its freeze/thaw state. The SMAP measurement approach is to integrate L-band radar and radiometer as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were flown on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites. The obtained data sets are used as a test-bed for the analysis of existing and development of new active-passive fusion techniques.

Soil moisture (SM) is a key parameter in the climate studies at a global scale and a very important parameter in applications such as precision agriculture at a local scale. The Global Navigation Satellite Systems Interference Pattern Technique (IPT) has proven to be a useful technique for the determination of SM, based on observations at vertical polarization (V-Pol) due to the Brewster angle. The IPT can be applied at both V-Pol and horizontal polarization (H-Pol) at the same time, observing the Brewster angle only at V-Pol. This letter presents a measurement technique based on tracking the phase difference between V-Pol and H-Pol interference patterns to improve the accuracy of the Brewster angle determination and, consequently, that of the SM retrievals. This technique benefits from the different phase behavior of the reflection coefficients between H-Pol and V-Pol in the angular observation range. To be sensitive to the phase difference, the Rayleigh criterion for smooth surfaces must be accomplished. This technique is not sensitive to topography as it is intrinsically corrected. Experimental results are presented to validate the proposed algorithm.

For the first time, airborne passive microwave data were collected at 1 km resolution over parts of Central Australia coinciding with spaceborne active data, allowing a comparison of such data sets acquired at medium (1 km) spatial resolution. L-band airborne passive microwave scenes were compared with C-band scenes and temporal parameters from the Advanced Synthetic Aperture Radar. It was found that the radar-returned signal, as well as the “sensitivity” and “correlation” parameters derived from the long time-series of the ASAR GM data, is similar to spatial patterns in the passive microwave data, suggesting that similar physical interactions are underlying both data sets, especially across heterogeneous landscapes. Comparable patterns found over the dry Lake Eyre salt bed (r2= 0.37) suggest that very high-resolution C-band radar data may be used to describe subpixel heterogeneity within coarse resolution radiometer data, such as the future Soil Moisture Active Passive mission.

Radiometric observations from the Soil Moisture and Ocean Salinity (SMOS) mission are processed to Level 1 brightness temperature (Tb) with ~ 42-km spatial resolution and reported on a 15-km hexagonal Discrete Global Grid (DGG). While these data should be used at the 42-km resolution which the oversampled DGG represents, this paper poses the question of whether they can be used directly at 15-km resolution without undertaking downscaling or implementing multiscale-type procedures when used in data assimilation. To assess the error associated with using the 42-km SMOS Tbdata at 15-km resolution, this study employs 1-km Tb data from the Australian Airborne Cal/Val Experiment for SMOS (AACES). The study compares SMOS-like data derived from AACES at 42-km resolution with Tbvalues actually observed on the 15-km DGG. These 15-km DGG data are subsequently interpolated to a regular 12-km model grid and compared with actual observations at that resolution. The results show that the average root mean square differences in Tbbetween the 15- and 42-km footprints are 4.5 K and 3.9 K for horizontal (H) and vertical (V) polarizations, respectively, with a maximum difference of 12.9 K. The errors when interpolating the 42-km data onto the 12-km model grid were estimated to be 3.3 K for H polarization and 2.9 K for V polarization under the assumption of independence or 4.5 K and 3.9 K for H and V polarizations, respectively, with 4.0 K in H polarization and 3.6 K in V polarization from the 15- to 12-km interpolation process alone. An evaluation of the Tbdifferences for 42-km data assumed on the 15-km DGG found no correlation with vegetation based on leaf area index and only slight correlation with the spatial variance of SMOS data and topographic roughness. Given these differences and the noise that currently exists in SMOS Tbat 42 km, the 15-km DGG data can be used directly on the hexagonal grid or interpolated onto a regular grid of equivalent spatial resolution without further degrading the data quality.

NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products: i) high-resolution from radar (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1 km) and radar backscatter (~10 m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of${<}{4}~{\rm K}$under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact.

This study investigates the potentialities offered by active and passive simultaneous acquisitions at L band for monitoring of soil moisture in forested areas. Airborne data, acquired over the moderately dense Gillenbah forest in the framework of SMAPEx-3 project, have been analyzed to derive the sensitivity of emissivity and backscattering coefficient to soil moisture variations during the campaign, considering a full set of ground measurements characterizing the forest environment.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture by fused active and passive L-band microwave measurements. With an airborne campaign conducted in the Rur catchment, Germany, in 2012, an active and passive L-band microwave data set is generated. The passive Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were installed on the DLR Dornier DO 228 aircraft. Despite of the differences between the airborne and the future spaceborne sensors, the data set will serve as a test bed for the analysis of existing and development of enhanced future active/passive data fusion techniques. Moreover, the derivation of (vegetation) parameters for the fusion approaches is planned.

The recently launched Soil Moisture and Ocean Salinity (SMOS) satellite is providing soil moisture observations at continental scales by measuring L-band microwave radiation emitted from the land surface. While its retrieval algorithms will correct for factors such as vegetation and surface roughness, it will not correct for soil salinity. This letter tests the assumption that soil salinity will have a negligible impact on L-band brightness temperature (Tb) at SMOS scales using field data; airborneTbobservations were collected in a saline groundwater discharge area near Nilpinna Station, South Australia. At the 500-m scale, the airborne observations ofTbcould not be reproduced using the baseline algorithm of the SMOS Level 2 retrieval scheme, without accounting for soil salinity in the model. The analysis in this letter shows that soil moisture retrieval errors of at least 0.04 m3m-3(i.e., the entire SMOS error budget) will occur due to salinity alone in SMOS footprints with saline coverage as low as 25% (possibly even much less). Consequently, fractional salinity coverage cannot be considered a negligible factor by microwave soil moisture satellite missions.

Disaggregation based on Physical And Theoretical scale Change (DisPATCh) is an algorithm dedicated to the disaggregation of soil moisture observations using high-resolution soil temperature data. DisPATCh converts soil temperature fields into soil moisture fields given a semi-empirical soil evaporative efficiency model and a first-order Taylor series expansion around the field-mean soil moisture. In this study, the disaggregation approach is applied to Soil Moisture and Ocean Salinity (SMOS) satellite data over the 500 km by 100 km Australian Airborne Calibration/validation Experiments for SMOS (AACES) area. The 40-km resolution SMOS surface soil moisture pixels are disaggregated at 1-km resolution using the soil skin temperature derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, and subsequently compared with the AACES intensive ground measurements aggregated at 1-km resolution. The objective is to test DisPATCh under various surface and atmospheric conditions. It is found that the accuracy of disaggregation products varies greatly according to season: while the correlation coefficient between disaggregated and in situ soil moisture is about 0.7 during the summer AACES, it is approximately zero during the winter AACES, consistent with a weaker coupling between evaporation and surface soil moisture in temperate than in semi-arid climate. Moreover, during the summer AACES, the correlation coefficient between disaggregated and in situ soil moisture is increased from 0.70 to 0.85, by separating the 1-km pixels where MODIS temperature is mainly controlled by soil evaporation, from those where MODIS temperature is controlled by both soil evaporation and vegetation transpiration. It is also found that the 5-km resolution atmospheric correction of the official MODIS temperature data has a significant impact on DisPATCh output. An alternative atmospheric correction at 40-km resolution increases the correlation coefficient between disaggregated and in situ soil moisture from 0.72 to 0.82 during the summer AACES. Results indicate that DisPATCh has a strong potential in low-vegetated semi-arid areas where it can be used as a tool to evaluate SMOS data (by reducing the mismatch in spatial extent between SMOS observations and localized in situ measurements), and as a further step, to derive a 1-km resolution soil moisture product adapted for large-scale hydrological studies.

The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m3/m3. Further analysis found the following: 1) The roughness valueHRwas too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameterstthandttvrequired optimization, yielding new values oftth= 0.2 andttv= 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.

With the recent launch of the Soil Moisture and Ocean Salinity (SMOS) mission, the passive microwave remote-sensing community is currently planning and undertaking airborne validation campaigns. Given the financial and logistical constraints on the size of validation area that can be covered by airborne simulators and the experiments underway that cover only a part of a satellite footprint, timely and scientifically sound advice on fractional footprint coverage requirements by campaigns for these low-resolution sensors is of paramount importance. Using high-resolution airborne data from an extensive airborne campaign in Southeast Australia, the fractional coverage requirement for L-band passive microwave satellite missions is assessed using a subsampling technique of flight lines through a passive microwave footprint. It is shown that minimum 50% coverage of the total footprint size will typically be required, given a spatial variability value of 20 K at 1-km resolution, to ensure that the footprint mean is estimated with an expected sampling error of less than 4 K, which is the design sensitivity of SMOS.

A downscaling approach to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture estimates with the use of higher resolution visible/infrared (VIS/IR) satellite data is presented. The algorithm is based on the so-called “universal triangle” concept that relates VIS/IR parameters, such as the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (Ts), to the soil moisture status. It combines the accuracy of SMOS observations with the high spatial resolution of VIS/IR satellite data into accurate soil moisture estimates at high spatial resolution. In preparation for the SMOS launch, the algorithm was tested using observations of the UPC Airborne RadIomEter at L-band (ARIEL) over the Soil Moisture Measurement Network of the University of Salamanca (REMEDHUS) in Zamora (Spain), and LANDSAT imagery. Results showed fairly good agreement with ground-based soil moisture measurements and illustrated the strength of the link between VIS/IR satellite data and soil moisture status. Following the SMOS launch, a downscaling strategy for the estimation of soil moisture at high resolution from SMOS using MODIS VIS/IR data has been developed. The method has been applied to some of the first SMOS images acquired during the commissioning phase and is validated against in situ soil moisture data from the OZnet soil moisture monitoring network, in South-Eastern Australia. Results show that the soil moisture variability is effectively captured at 10 and 1 km spatial scales without a significant degradation of the root mean square error.

A physically based six-channel land algorithm is developed to simultaneously retrieve global soil moisture (SM), vegetation water content (VWC), and land surface temperature. The algorithm is based on maximum-likelihood estimation and uses dual-polarization WindSat passive microwave data at 10, 18.7, and 37 GHz. The global retrievals are validated at multispatial and multitemporal scales against SM climatologies,in situnetwork data, precipitation patterns, and Advanced Very High Resolution Radiometer (AVHRR) vegetation data.In situSM observations from the U.S., France, and Mongolia for diverse land/vegetation cover were used to validate the results. The performance of the estimated volumetric SM was within the requirements for most science and operational applications (standard error of 0.04 m3/m3, bias of 0.004 m3/m3, and correlation coefficient of 0.89). The retrieved SM and VWC distributions are very consistent with global climatology and mesoscale precipitation patterns. The comparisons between the WindSat vegetation retrievals and the AVHRR Green Vegetation Fraction data also reveal the consistency of these two independent data sets in terms of spatial and temporal variations.

A long term profile soil moisture data acquisition effort is currently under way at 12 monitoring stations across southwestern France. The spatial distribution and set-up of those sites was specifically designed for validating remote sensing and model soil moisture estimates [1], [2]. Those m-situ measurements are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter using a time constant, T, is used to compute a profile soil water index from the surface observations. Through optimisation, a unique value for T of 6 days is found to result in a satisfactory correlation between observation and estimate for all stations. The surface soil moisture observed is also used to evaluate the normalized surface soil moisture estimates derived from coarse resolution (25 km) active microwave data of the ASCAT C-band. For 11 stations, significant correlation levels are found. The best correlation between a soil water index derived from ASCAT and the in-situ observations is obtained for a T of 14 days.

The community microwave emission model (CMEM) is the low frequency forward observation operator developed at ECMWF. It is used in this paper to simulate brightness temperatures at local and regional scales over SMOSREX (France) and AMMA (West Africa), respectively. Background errors in simulated brightness temperatures are quantified at different frequencies and incidence angles for these two sites.

To retrieve soil moisture from L-band microwave radiometry, it is necessary to account for the effects of temperature within both vegetation and soil media. To compute the effective soil temperatureTG, several simple formulations accounting for soil temperatures at the surface and at depth and surface soil moisture have been developed. However, the effects of the soil physical properties in terms of texture, density, or structure, which all may be important variables in the modeling ofTG, have never been investigated. In this paper, several simple formulations ofTGat L-band, accounting for or ignoring the effects of soil texture and density, were developed and compared based on a very large simulated data set. The best configurations and parameterizations of these simple formulations were computed and could be directly used for operational applications in future soil moisture retrieval studies. For instance, we showed that the use of the surface temperature in the estimation ofTGcan be significantly improved by using additional information on the soil temperature at depth (the average error in the estimation ofTGdecreased from ~ 4 to ~ 1.8 K). On the contrary, almost no improvement was obtained if air temperature was used instead of surface temperature. Also, it is shown that the use of additional information on the soil properties, mainly the soil clay content and density, led to improved results by about 0.2 K in the estimation ofTG. The improvement was found to be larger for sandy and dry soils: simplified formulations accounting for soil properties are able to represent the fact thatTGis closer to the soil temperature at depth for these soil conditions.

For the preparation of the Soil Moisture and Ocean Salinity (SMOS) mission, due for launch in 2008, a synthetic study for the aggregation and disaggregation of L-band brightness temperature fields is currently being undertaken for a 5-year period (2000–2005) over south-western France. The observed soil moisture is derived from offline simulations obtained from the Météo-France land surface model ISBA-A-gs. The radiative transfer model L-MEB is used to estimate the corresponding brightness temperature at 8km resolution, with a large number of possible incidence angles for each time step. Aggregation of the distributed information is then undertaken by simulating overpasses of SMOS over the region. Finally, the disaggregation method is an extension of the approach presented by Merlin et al. (2005).