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Active and passive microwave signals over land co-vary depending on their shared scattering and emission characteristics by soil and vegetation media. Estimates of this covariation can be used beneficially to downscale coarse-resolution brightness temperatures with high-resolution backscatter for enhanced-resolution Earth observations. In this study, a forward model of covariation for vegetated soil is derived by combining two well-established models of active and passive microwave interactions. The covariation model is inverted to obtain a single-pass observation-driven estimation of active-passive microwave covariation (β) based on multi-channel radiometer and Synthetic Aperture Radar (SAR) scenes. A key feature of the estimation approach is that it is applicable to co-located spatial data scenes and does not rely on temporal information. We present applications of the estimation with combinations of SMAP (L-band) radiometry and both SMAP (L-band) and multi-angular Sentinel-1 (C-band) backscatter data. We first show that for the period of available SMAP L-band radiometer and radar data, the estimation approach for β, introduced in this study, yields similar results as the statistical time-series approach originally designed for SMAP. The new single-pass approach also allows estimation of active-passive covariation where the statistical approach cannot be applied because dynamic range of brightness temperature and backscatter are too limited to allow regression. We then apply the developed estimation method to SMAP L-band radiometer and multi-angular Sentinel-1 C-band SAR data. Here, the study quantifies the effects of microwave frequency and look-angles on the covariation by applying the estimation globally and analyzing the results as a function of vegetation cover – a key determinant of the strength of the covariation.

Vegetation cover absorbs and scatters L-band microwave emission measured by SMOS and SMAP satellites. Misrepresentation of this phenomena results in uncertainties when inferring, for instance, surface soil moisture in retrieval algorithms that commonly utilize the tau-omega model which is most applicable for a weakly scattering medium. In this study, we investigate the degree to which multiple-scattering is prevalent over a range of land cover classifications (from lightly vegetated grasslands to dense forests) at the satellite scale by explicitly accounting for multiple-scattering in a first-order radiative transfer model, developed here. Even though the tau-omega model with effective parameters can possibly capture higher-order scattering contributions, deliberately partitioning scattering into different components is required to estimate multiple-scattering properties. Specifically, we aim to determine how one can partition between zeroth and first-order radiative transfer terms within a retrieval algorithm without ancillary information, determine whether this method can detect first-order scattering at the SMAP measurement scale without ancillary information, and quantify the magnitude of detected scattering. A simplified first-order radiative transfer model which characterizes single interactions of microwaves with a scattering medium is developed for implementation within retrieval algorithms. This new emission model is implemented within a recently developed retrieval algorithm, the multi-temporal dual channel algorithm (MT-DCA), which does not require ancillary land use information. Scattering parameters as well as SM and vegetation optical depth (τ) are retrieved simultaneously in Africa and South America using the first year of SMAP brightness temperature measurements on a 36 km grid. Specifically, an introduced time invariant first-order scattering coefficient (ω1) is retrieved representing microwave emission interaction with the canopy. We find that ω1 is typically zero in lightly vegetated biomes and non-zero (~0.06) in 74% of the forest pixels. In forest-dominated pixels, the median first-order emissivity is 0.04, or about 4.3% of a given SMAP radiometer brightness temperature measurement. Additionally, explicitly accounting for first-order scattering terms in the radiative transfer model tends to increase SM and τ retrievals by a median of 0.02 m3/m3 and 0.1, respectively, only in forested regions. This study demonstrates the first attempt to explicitly partition higher-order scattering terms in a retrieval algorithm at a satellite scale and ultimately provides a fundamental understanding and quantification of multiple-scattering from grasslands to forests.

Global monitoring of soil moisture and snow is now available through various satellite observations from optical, microwave, and gravitational sensors. However, very few modeling frameworks exist that conjointly use the above sensors to produce mutually and physically consistent earth system records. To this goal, a prototype of multi-sensor land data assimilation system is developed by linking the Community Land Model version 4 (CLM4) and a series of forward models with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global soil moisture and snow by assimilating brightness temperature, snow cover fraction, and daily total water storage observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Moderate Resolution Imaging Spectroradiometer (MODIS), and Gravity Recovery and Climate Experiment (GRACE), respectively. A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to introduce ensemble spread in CLM4 land states and some methods are used to reduce the computational load. Data assimilation with different combinations of sensors is implemented for 2003–2009 to investigate individual contributions from different satellite observations. Evaluation results and cross-comparison of open-loop and data assimilation cases suggest that 1) assimilation of MODIS snow cover fraction slightly improves snow estimation in mid and high latitudes; 2) lower and higher frequencies of AMSR-E brightness temperature play complementary roles in improving global soil moisture and snow estimation; 3) assimilation of GRACE tends to degrade soil moisture estimation but poses potential in improving snow depth estimation in most high-latitude regions. Generally, the combination of MODIS, GRACE, and AMSR-E observations with regard to spatial locations holds promise to provide a robust global soil moisture and snow estimation through the multi-sensor land data assimilation system.

An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer – Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators – based on microwave brightness temperature, land cover types, and long-term averaged vegetation index – are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals.

Most polar-orbit-satellite based passive microwave radiometers, e.g. AMSR-2 and the FY-3B Microwave Radiation Imager (FY3B-MWRI), have the revisit cycles of ~2 days in low latitudes. This indicates that requirement of complete global coverage on the daily basis has not been met. On the other hand, using synergistic observations from different radiometers seems promising to enhance the temporal resolution of observations in such areas, whereas cross-radiometer inter-calibration is essential prior to their synergism. For this purpose, conventional inter-calibration models are usually built based on linear relations of brightness temperature (TB) between different radiometers. However, the linear model has relatively poor performance in low latitudes primarily due to wet surface conditions. In this study, we propose an improved non-linear model for calibrating MWRI TB against AMSR-2 TB at 18.7 GHz and 23.8 GHz channels. Global land surface temperature (LST) is estimated using the post-calibration MWRI TB data and then validated against AMSR-2 based LST, MODIS based LST, and in situ near-surface air temperature datasets in low latitudes respectively. The validation results show that LST outcome based on the improved non-linear inter-calibration method has smaller global Root Mean Square Errors (RMSEs) below 3.5 K, compared with the RMSEs of ~6 K obtained from the conventional linear inter-calibration method and from the results without inter-calibration. Further analysis also shows that synergistic observations between AMSR-2 and MWRI are capable of shortening the maximum global revisit cycle of AMSR-2 LST from ~2 days to less than 1.2 days. These results indicate that the improved inter-calibration method is effective for obtaining daily LST datasets with quasi-complete global coverage and reliable accuracy.

Roughness effect parameterization is critical to accurately simulate brightness temperature (Tb) signals observed by a radiometer over bare soil surface. However, current roughness parameterization schemes usually suffer from severe error, which dominates the error budget in current Tb modeling over bare soil surface. In this study, uncertainty of soil roughness parameterization schemes is comprehensively assessed using data set collected during 2004 to 2006 at the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) bare soil experimental site. To reduce uncertainty from sampling depth mismatch, the soil moisture profile with a 1 cm thickness from a calibrated Hydus-1D (H1D) model is utilized to determine the optimal soil moisture inputs to soil emission model. Uncertainties of 15 literature-based roughness effect parameterization schemes developed for L-band Tb modeling are inter-compared. The “Q/H” model is further calibrated against multi-angle and dual-polarization Tb observations at the SMOSREX bare soil site under different roughness conditions. Our results show that: (1) soil moisture sampling depth varies with soil moisture content and roughness condition. When soil is drier and rougher, the soil moisture sampling depth gets deeper. (2) The 15 roughness schemes generally perform better at vertical polarization than at horizontal polarization and better when soil surface is relative smooth than when soil surface gets rougher. The 15 roughness correction schemes have their own advantages and disadvantages with diverse error and bias characteristics. None of them has a superior performance at all conditions in terms of roughness, polarizations and incident angles. (3) A non-zero Q configuration is preferred in parameter retrieval experiments and the observed linear relationship between ΔN and root-mean-square height (σ) or σ2/LC can only be reproduced when Q is non-zero in parameter retrieval. (4) The effective roughness parameters (Q, Np and h) generally increase when soil get rougher. The calibrated Q, Nh and Nv show exponential dependence on the effective parameter h. The calibrated h still shows dependence on surface soil moisture after accounting the impact from soil sampling depth and also shows strong power-law dependence on Tb at incident angle of 40°. The non-zero-Q fitting models have comparable performance in Tb modeling with zero-Q models but may be more physically realistic.

International audience; L-band radiometer measurements collected over the Dome C area from 2010 to 2015 indicated that the brightness temperature (T B) was relatively stable at vertical (V) polarization (standard deviation lower than 1 K at annual scale), while it was slightly more variable at horizontal (H) polarization. During the 2014-2015 austral summer, an exceptional situation was recorded by both the DOMEX ground radiometer and the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite. From November 2014 to March 2015, T B H showed a progressive and significant increase until 20 March 2015 when it sharply decreased by about 5 K (at 52.5 o incidence angle) within a few days. In parallel to the increase in T B H, glaciological and meteorological in situ measurements showed a wind speed that was lower than usual and a low-density snow layer being progressively set up on the surface. This was consistent with the exceptional hoar event observed, as well as with snow accumulation on the surface. On the other hand, the decrease in T B H was related to the passing over Dome C of a storm that removed or compacted the layer of light snow on the surface. The WALOMIS (Wave Approach for LOw-frequency MIcrowave emission in Snow) snow-emission model was used with in situ measurements of the snowpack as inputs for evaluating the effect of changes observed on the snow surface in T B H. The simulations indicated that the surface snow density variations were sufficient for predicting the increasing and decreasing trends of the T B H. However, the thickness variations of the superficial layer were essential so as to obtain a better agreement with the SMOS observations. This result confirmed that the L-band T B H was affected by the snow properties of the top centimeters of the snowpack, in spite of the large penetration depth (hundreds of meters). Both the surface snow density and the thickness of the superficial layer were relevant, due to coherent interference effects.

Fusing high-level passive microwave (PMW) LST and thermal infrared (TIR) LST products is a promising means of generating high-quality, all-weather land surface temperature (LST) data. In this paper, we propose a new fusion strategy for generating high-quality, all-weather LST data based on cumulative distribution function (CDF) matching and multiresolution Kalman filtering (MKF). CDF matching was employed to improve the quality of the high-resolution (0.01°) LST data downscaled with the retrieved coarse-resolution (0.1°) PMW LST from the Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature (BT) data set. The MKF approach was employed to produce high-quality, all-weather LST data by fusing the retrieved coarse-resolution AMSR2 LST and high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) LST data sets filled with CDF-improved LST data. The spatial patterns in the high-resolution and coarse-resolution LST data were consistent after MKF fusion. The accuracy of coarse-resolution LST data was considerably improved. The biases, root mean square errors (RMSEs) and determination coefficients (R2) were 0.79–1.47 K, 3.0–3.82 K, 0.88–0.96, respectively. The RMSE of the fused coarse-resolution LST data decreased by 1.51 K/1.82 K and 1.81 K/1.39 K in daytime/nighttime in the Tibetan Plateau (TP) and Heihe River Basin (HRB) verification regions compared to the RMSE of the retrieved AMSR2 LST data. The accuracy of high-resolution LST data remained unchanged, but the spatial coverage was complete with exceptions at the orbital gaps. The bias, RMSE and R2 values of high-resolution LST data were − 0.91- -0.81 K, 3.02–3.32 K, and 0.93–0.97 under all-sky conditions in the TP and HRB verification regions, respectively. Compared to methods in the existing LST fusion studies, the proposed strategy has advantages in terms of LST data quality and validation accuracy. When both coarse-resolution and high-resolution LST data are missing, the spatial coverages of fused all-weather LST data are incomplete. We attempted to fill the vacancies in PMW LST data with the DINEOF method in advance and subsequently obtained spatially complete all-weather LST data.

Land surface freeze/thaw (F/T) dynamics impact the surface energy balance, carbon fluxes, and hydrologic processes. Recent and on-going L-Band (≈ 1.4 GHz) spaceborne missions have the potential to provide enhanced information on F/T state over large geographic regions with rapid revisit time. However, the low spatial resolution of these spaceborne observations (≈ 45 km) makes it difficult to isolate the primary contributions to the F/T signal, including the soil, snow, and vegetation states. A ground-based L-Band radiometer measurement campaign was conducted in Saskatchewan, Canada during the winter of 2014–2015 to evaluate brightness temperature sensitivity to F/T processes, snow, liquid water in snow and assess theoretical retrievals of soil permittivity (eG), and snow density from experimental data. The ground-based radiometer was run in multiple configurations. First, temporally continuous measurements were conducted through the winter over an agricultural field, with a comprehensive network of reference snow and soil observations characterizing the F/T state of the soils within or adjacent to the radiometer footprint. Secondly, weekly multi-angular L-Band measurements were made at an undisturbed site of naturally accumulating snow cover, over a site that was kept snow free, and a site with artificially compacted snow. Results from the assessment of the land surface F/T retrieval algorithm showed that L-Band measurements are sensitive to the near surface F/T state of the soil, with the highest level of agreement found between the near surface (2.5 cm) F/T reference measurements of soil temperature and eG (accuracies of 91.1% and 92.9%, respectively). Several mid-winter melt events with air temperatures (Tair) above 0°, and soil temperatures below 0 °C, illustrated that liquid water within the snow dramatically increase the TB, resulting in false retrievals of soil thaw events using existing L-Band F/T retrieval algorithms. However, Tair was also shown to have a high commission errors compared to radiometer observations in detecting snow melt, because of the delay between Tair > 0 °C and the onset of melt resulting in a measurable wet snow signal at L-Band. The retrieval of snow density (ρs), of the bottom 10 cm of the snowpack tended to underestimate high ρs (> 400 kg m− 3), and agreed well for lower ρs (

In this paper, soil moisture retrievals of surface soil moisture was investigated using L-band brightness temperature under diverse conditions and land cover types. The study focused on the PALS (Passive Active L-band System) radiometer data collected during the SMAPVEX12 (Soil Moisture Active Passive Validation Experiment 2012) field experiment which took place in southern Manitoba, Canada in 2012. The experiment domain covers croplands with high clay content as well as croplands and grasslands with sandy soils. A retrieval algorithm was parameterized for these specific land types. The formulation of the retrieval algorithm is based on a traditional surface scattering assumption. Based on this data set we found that for the clayey croplands the surface scattering assumption is inadequate, and that the algorithm needed significant tuning for the sandy soils. Empirically-based parameters for retrieving soil moisture under these conditions were developed. We also applied the parameterized algorithm to the retrieval of soil moisture for the entire experiment domain. We found that the use of sub-grid modeling improves the retrieval performance to a satisfactory level despite the challenging land types encountered.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched on 31st January 2015, with the aim of providing global soil moisture maps at 9 km spatial resolution by combining L-band radar and radiometer observations. However, after the SMAP radar became inoperable, NASA decided to utilize the Sentinel 1A/1B C-band SAR data in its place. The new version of baseline brightness temperature (Tb) downscaling algorithm for SMAP is tested using L-band airborne data to evaluate the capabilities of the C-band Sentinel-1A SAR relative to L-band radar data in downscaling the SMAP Tb for achieving high resolution brightness temperature. In this study, the downscaling algorithm used L-band airborne Synthetic Aperture Radar (SAR) backscatter (σ) collected from the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) in south-eastern Australia to downscale 36 km L-band SMAP radiometer Tb pixels to 3 km and 9 km. The downscaling results were then compared with the published results using Sentinel-1A C-band backscatter, and evaluated against airborne 1 km resolution L-band passive microwave brightness temperature collected from SMAPEx-5. The results show that for vertical polarization the average Root Mean Square Error (RMSE) of downscaled Tb when compared with reference airborne Tb across 4 days at 9 km resolution were 4.9 K for L-band and 6.0 K for C-band, and increased to 9.3 K for L-band and 9.6 K for C-band at 3 km spatial resolution. Moreover, the correlation coefficient (R) of downscaled and reference Tb across the 4 days was 0.92 for L-band and 0.88 for C-band at 9 km, decreasing to 0.75 for L-band and 0.72 for C-band at 3 km spatial resolution. Accordingly, the RMSE increased and the correlation coefficient decreased when using C-band radar data in place of that at L-band. However, overall there is expected to be only a slight decrease in performance of the downscaling algorithm by using the Sentinel 1A data in place of the SMAP radar.

International audience; Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m3/m3 for AMSR-reg and 0.084 m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations.

L-band passive measurements (1.4 GHz) over continental areas are known to be related to surface soil moisture. Two satellite missions were recently launched to measure land surface emissions at this frequency band (SMOS-Soil Moisture and Ocean Salinity in 2009 and SMAP-Soil Moisture Active/Passive in 2015). In order to improve soil moisture retrievals from satellite data, ground-based radiometer systems operating at the same frequency were deployed over specified areas to investigate the L-band emission of various land covers under various climatological conditions. In this study, three years of L-band passive measurements from a radiometer installed on top of a steep mountain in the French Alps were analyzed and compared to L-band passive simulations. The innovative radiometer location led to large footprints due to the distance between the radiometer and the area under study. This experiment also produced microwave measurements affected by various potential difficulties typically encountered in SMOS/SMAP satellite missions: topography, heterogeneous footprints, dry/wet snow events, dew and vegetation litter. Based on in situ and modeling data, this paper investigates the potential of a radiative transfer model (L-band Microwave Emission of the Biosphere, L-MEB) to simulate L-band measurements and analyzes the differences with ELBARA observations. First, it was found that the topography generated a mixing of the horizontal and vertical polarizations. In addition, a large positive bias was found on ELBARA measurements (31 K and 12 K in horizontal and vertical polarizations respectively). Investigations showed that the sky reflection measured by the radiometer was partially substituted by land reflection coming from the surrounding topography. Second, the low-vegetation emission was investigated and highlighted the inability of the MODIS NDVI product to correctly represent the vegetation dynamics. Finally, dry snow conditions were found to have non-negligible impact at L-band and a particular signature was found during snow melting periods, with potential applications at the SMOS/SMAP spatial scales (~ 40 km).

Recent studies showed that machine learning (ML) algorithms (e.g., artificial neural network (ANN) and support vector machine (SVM)) reasonably reproduce passive microwave brightness temperature observations over snow-covered land as measured by the Advanced Microwave Scanning Radiometer (AMSR-E). However, these studies did not explore the sensitivities of the ML algorithms relative to ML inputs in order to determine the behavior and performance of each algorithm. In this current study, normalized sensitivity coefficients are computed to diagnose ML performance as a function of time and space. The results showed that when using the ANN, approximately 20% of locations across North America are relatively sensitive to snow water equivalent (SWE). However, more than 65% of locations in the SVM-based brightness temperature (Tb) estimates are sensitive relative to perturbations in SWE at all frequency and polarization combinations explored in this study. Further, the SVM-based results suggest the algorithm is sensitive in both shallow and deep SWE, SWE with and without overlying forest canopy, and during both the snow accumulation and snow ablation seasons. Therefore, these findings suggest that compared with the ANN, the SVM could potentially serve as a more efficient and effective measurement model operator within a Tb data assimilation framework for the purpose of improving SWE estimates across regional- and continental-scales.

Passive microwave remote sensing at L-band has been widely acknowledged as the most promising technique to observe the spatial distribution of near surface (top ~ 5 cm) soil moisture at regional to global scales. The launch of the ESA's Soil Moisture and Ocean Salinity (SMOS) mission in 2009 now means that global space-borne brightness temperature observations are available at L-band (1.41 GHz) to estimate soil moisture every 2 to 3 days with a target accuracy of 0.04 m 3 /m 3 . Moreover, NASA's Soil Moisture Active Passive (SMAP) satellite has been launched on 31st January 2015, also carrying an L-band radiometer, together with an L-band radar for downscaling the brightness temperature observations to better than 10 km resolution. At the SMOS/SMAP radiometer scale of ~ 40 km, the presence of water bodies potentially induces an overestimation of retrieved soil moisture, if not carefully accounted for in retrieval models. Such water fraction effects on brightness temperature and soil moisture retrieval accuracy were investigated in this study, using airborne L-band brightness temperature data collected during three Australian field experiments. The water induced brightness temperature effect and water fraction were compared under different resolutions, sampling days, and land surface conditions, showing that the water fraction impact on retrieved soil moisture is independent of scale, but heavily dependent on the soil water content status. Subsequently, the highest water fraction threshold that can be tolerated in order to achieve the 0.04 m 3 /m 3 target accuracy without correction has been determined as 0.08 (actual range is from 0.02 for dry bare soil to 0.08 for wet vegetated soil). Using a MODIS derived water fraction dataset, the water fraction dynamics were also studied over Australia during the ten years from 2001 to 2010. The results show that if the mean water fraction map was used as a static water map to flag or correct water effects, the water body induced soil moisture retrieval error would have exceeded the 0.04 m 3 /m 3 target more than once for 13.5% of the Australian land 40 km sized radiometer pixels; only 0.6% Australian land pixels would have exceeded this target with a frequency of 10 times or more per year.

Passive microwave (PMW) remote sensing has proven to be a useful approach to characterize the volume of seasonal snowpack in remote northern regions at the synoptic scale. Modeling emitted microwave brightness temperatures (TB) is made possible using a physical radiative transfer model that takes into account microstructural and stratigraphic structure of the snowpack. However, prescribing the microstructure remains a difficult task. This paper aims to find proper microstructure parametrization and the snow emission model formulation that best optimize TB simulations for Arctic and Subarctic snowpacks. Surfaced-based radiometric measurements in conjunction with in-situ snowpack characterization were used for testing different configurations based on the Snow Microwave Radiative Transfer model (SMRT), with two electromagnetic models (Dense Media Radiative Transfer Quasi Crystalline Approximation, DMRT, and Improved Born Approximation, IBA) and two microstructure description theories (Sticky Hard Sphere, SHS, and Exponential, Exp). We compare the performance of three configurations (DMRT-SHS, IBA-SHS and IBA-Exp) with a unique large dataset (119 snowpits with concomitant microwave ground-based radiometer observations) covering a wide range of Arctic and Subarctic snow types in Northern and Eastern Canada. Results show that the input measured microstructure parameters must be scaled up in order to better match simulated and observed TB at 11, 19, 37 and 89 GHz. We show that the IBA-Exp gives the best results, with a Root-Mean-Square Error (RMSE) lower by up to 30% for Subarctic snow and 24% for Arctic snow compare to the other model configurations we used. In addition, we undertake a complementary experiment on isolated homogeneous snow slabs to investigate the sensitivity of the scaling factor to snow microstructure. The retrieved microwave correlation length appears significantly different than the in-situ Debye correlation length. At high frequencies, the observed variability of these scaling factors with frequency and snowpack types means that density, SSA and estimated correlation length seem insufficient to appropriately fully characterize snow microstructure for microwave modeling purposes.

Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an L-band radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only ~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the high-resolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP active-passive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is ~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at global extent. The assessment of high-resolution product at 3 km and 1 km using the soil moisture calibration and validations sites shows reasonable accuracy of ~0.05 m3/m3. The SMAP-Sentinel1 active-passive high-resolution product is now available to the public (new version released in October 2018) through NSIDC (NASA DAAC). The duration of this product is from April 2015 to current date.

Accurate dynamic measurements of snow depth (SD) on the Tibetan Plateau (TP) are of great importance for understanding local climate systems and surface hydrological cycles. Currently, passive microwave remote sensing is the most effective approach used to monitor the spatiotemporal variations in SD. However, the large uncertainties and low spatial resolution of existing SD products lead to less satisfactory passive microwave remote sensing in regions with complex terrain conditions, strong seasonal transitions and great spatiotemporal heterogeneity, such as the TP. In this study, geographic location, terrain, snow cover days and brightness temperature were evaluated. Then, critical factors were used to develop an Advanced Microwave Scanning Radiometer 2 (AMSR2) SD downscaling model. The results indicated the following: (1) SD is greatly influenced by geographic location, snow covered days, terrain parameters and brightness temperature difference. The surface roughness exhibited the best correlations with ground observations, which reflects 44% of the variation in SD; (2) the novel multifactor power SD downscaling model, which combines AMSR2 brightness temperature data and other auxiliary data, showed higher accuracy and stability and was closely correlated with the SD ground observations, and it reflected 80% of the SD variation; (3) compared with the AMSR2 ascending and descending orbital SD products released by the Japan Aerospace Exploration Agency (JAXA) and the SD datasets in China from the Environmental and Ecological Science Data Center for West China (WESTDC), the proposed downscaled SD datasets for the TP were greatly improved, and the root-mean-square error (RMSE) and average absolute error (MAE) were greatly reduced (2.00 cm and 0.25 cm, respectively); (4) the downscaled SD datasets for the TP showed good accuracy (RMSE = 0.58 cm) in areas with a SD of

This paper investigates the potential for the Soil Moisture Active Passive (SMAP) L-band radiometer to estimate the percentage of frozen soil inside a pixel during fall periods. To evaluate the spatial and temporal variability of the autumn freeze in northeastern Canada boreal forest, a network of compact and self-recording temperature sensors (iButton) along transects averaging 10 km was deployed on the soil surface at two different sites. Results show important spatial variability in soil freezing timing with maximums, for the two sites, spanning from 7.5 to 9.5 weeks to reach a frozen state. It is known that L-band radiometry such as Soil Moisture Active Passive (SMAP) can monitor freeze/thaw (F/T) seasonality at low spatial resolution (~40 km). However, the possible strong surface variability within a 40 km × 40 km pixel can lead to spatial delay in fall freezing. Simulations of brightness temperature (TB) weighted by spatially distributed iButton F/T records and using the ω-τ vegetation model show good agreement with SMAP satellite observations during the fall transition period with an average RMSE of 3.5 K. A new intra-pixel frozen soil percentage retrieval algorithm using only SMAP observations and air temperature is proposed. The percentage of frozen soil in falls 2015 and 2016 from the new algorithm and the in situ measurements are in good agreement with a coefficient of determination (R2) ranging between 0.63 and 0.88.

The Helsinki University of Technology (HUT) snow emission model forms the basis of the European Space Agency's GlobSnow snow water equivalent (SWE) product (Takala et al., 2011). The model applies a semi-empirical radiative transfer calculation to account for the interaction of the snow medium with microwaves; separate components are applied to account for vegetation, the atmosphere, and emission from the ground surface. For the retrieval of SWE, an innovative method is used to account for spatial and temporal variability in snow conditions by retrieving an effective parameter describing the scattering behavior of microwaves in the snow (a proxy indicator of the microwave effective snow grain size). In this study, the influence of differing snow conditions, as well as varying land cover, on the retrieved microwave effective snow grain size was analyzed. Passive microwave measurements were acquired using tower-based, mobile sled-based and airborne radiometers in a mixed forest environment near Sodankyla, Finland. Forward simulations at the tower site over an entire winter period showed that the use of an empirical relation to modify the classical in situ measured grain size produced HUT model bias errors less than 6 K on average at 19 and 37 GHz from a ~ 20–80 cm deep boreal snowpack. Model simulations for airborne and sled-based observations showed that using a simplified 2-layer representation of the snowpack improves simulation biases and RMSE, although modification of the measured grain size was again necessary to achieve these results, regardless of the layering configuration. The microwave effective grain size retrieved from HUT predictions was closely related to a simple average grain size measured in situ, both in terms of magnitude and temporal trend. This is an important finding as the retrieval scheme of Takala et al. (2011) relies on the microwave effective grain size to retain a degree of physical basis for values to be generally applicable over larger areas, which is challenging because this free parameter also accounts for errors unrelated to the effective snow grain size (i.e. vegetation, soil conditions, scene heterogeneity, etc.).

The baseline radiometer brightness temperature (Tb) downscaling algorithm for NASA's Soil Moisture Active Passive (SMAP) mission, scheduled for launch in January 2015, is tested using an airborne simulation of the SMAP data stream. The algorithm synergistically uses 3 km Synthetic Aperture Radar (SAR) backscatter (σ) to downscale a 36 km radiometer Tb to 9 km. While the algorithm has already been tested using experimental datasets from field campaigns in the USA, it is imperative that it is tested for a comprehensive range of land surface conditions (i.e. in different hydro-climatic regions) before global application. Consequently, this study evaluates the algorithm using data collected from the Soil Moisture Active Passive Experiments (SMAPEx) in south-eastern Australia, that closely simulate the SMAP data stream for a single SMAP radiometer pixel over a 3-week interval, with repeat coverage every 2–3 days. The results suggest that the average root-mean-square error (RMSE) in downscaled Tb is 3.1 K and 2.6 K for h- and v-polarizations respectively, when downscaled to 9 km resolution. This increases to 8.2 K and 6.6 K when applied at 1 km resolution. Downscaling over the relatively homogeneous grassland areas resulted in 2 K lower RMSE than for the heterogeneous cropping area. Overall, the downscaling error was around 2.4 K when applied at 9 km resolution for five of the nine days, which meets the 2.4 K error target of the SMAP mission.

The Alps represent an extremely complex environment in which snow properties suffer dramatic spatial variations that cannot easily be followed by space-borne microwave radiometers, due to their coarse spatial resolution: some studies demonstrated that the algorithms developed for global scale monitoring of the snow depth (SD) are unable to retrieve this parameter with a satisfactory accuracy on mountainous areas. An improved method for monitoring the Snow Depth (SD) on Alpine areas is presented here. Equivalent Brightness Temperature Tbeq at an enhanced spatial resolution, corrected for the effects of orography and forest coverage, were computed from the AMSR-E measurements by using ancillary information on land use, surface temperature, and a digital elevation model (DEM). These equivalent Tbeq values were used instead of the original AMSR-E measurements as inputs of an algorithm that estimates SD on a global scale basing on and Artificial Neural Network (ANN) techniques from AMSR-E brightness temperatures at X-, Ku- and Ka-bands, V-polarization. The improvement in the retrieval accuracy using these Tbeq equivalent values was evaluated using data collected during the winters between 2002 and 2011 on a test area located in the eastern part of the Italian Alps.

We exhibit a technique to detect and estimate precipitation over the global oceans using the radar back scattering coefficient and brightness temperature measurements from Oceansat-II scatterometer along with numerical weather prediction model derived rain sensitive parameters via a neural network (NN) based setup. Rain/no-rain labels are generated by analyzing rainfall observations from Tropical Rainfall Measuring Mission (TRMM) and Advanced Microwave Scanning Radiometer for Earth Observation Satellite (EOS) (AMSR-E) which are concurrent (within a spatiotemporal bin 0.25° × 0.25° latitude–longitude and 900 s) to Oceansat-II overpasses. The rain sensitivity of all the parameters is examined. NN is applied in two stages: (1) rain identification and (2) rain quantification with training samples from five different overlapping geographical regions [I(25°N–25°S), II(15°N–45°N), III(35°N–70°N), IV(15°S–45°S) and V(35°S–70°S)]. Rain identification accuracy is about 93%, 87%, 90%, 79%, and 85%, and no-rain detection accuracy of about 97%, 87%, 86%, 84% and 86% for these regions. The missing rain cases are few compared to the size of no-rain samples and are largely from the low rain regime. The RMS error of instantaneous rain estimation for regions I to V (rain rates varying from > 0 to approximately 45, 25, 25, 45, and 20 mm h− 1) is found to be 1.86, 0.69, 0.47, 0.56, and 0.46 mm h− 1, respectively. The qualitative comparisons of instantaneous, 3-days, monthly and seasonal rain rates from scatterometer and AMSR-E demonstrate a good agreement between them. Probability distribution of monthly rain rates from scatterometer and AMSR-E is also compared, indicating the consistency of scatterometer derived rain with AMSR-E rain in a climatic scale.

International audience; The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as ‘Mattar’ and ‘Saleh’). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and ‘Mattar’) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. As no in situ SM data was used, the evaluation made here is relative to the use of this specific reference data set. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the ‘reference’ SM data set derived from the multi-angular observations (R2 ≈ 0.90, RMSE varying between 0.035 and 0.056 m3/m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the ‘Saleh’ methods based on either bi-angular or bipolarization observations (R2 ≈ 0.93, RMSE ≈ 0.035 m3/m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the ‘reference’ SM time series (R2 ≈ 0.75, RMSE ≈ 0.055 m3/m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.

Landsat's continuing record of the thermal state of the earth's surface represents the only long term (1982 to the present) global record with spatial scales appropriate for human scale studies (i.e., tens of meters). Temperature drives many of the physical and biological processes that impact the global and local environment. As our knowledge of, and interest in, the role of temperature on these processes have grown, the value of Landsat data to monitor trends and process has also grown. The value of the Landsat thermal data archive will continue to grow as we develop more effective ways to study the long term processes and trends affecting the planet. However, in order to take proper advantage of the thermal data, we need to be able to convert the data to surface temperatures. A critical step in this process is to have the entire archive completely and consistently calibrated into absolute radiance so that it can be atmospherically compensated to surface leaving radiance and then to surface radiometric temperature. This paper addresses the methods and procedures that have been used to perform the radiometric calibration of the earliest sizable thermal data set in the archive (Landsat 4 data). The completion of this effort along with the updated calibration of the earlier (1985–1999) Landsat 5 data, also reported here, concludes a comprehensive calibration of the Landsat thermal archive of data from 1982 to the present.

Plot-scale brightness temperature (T B ) measurements at 6.9, 19, 37, and 89 GHz were acquired in forest, open, and lake environments near Churchill, Manitoba, Canada with mobile sled-based microwave radiometers during the 2009–2010 winter season. Detailed physical snow measurements within the radiometer footprints were made to relate the microwave signatures to the seasonal evolution of the snowpack, and provide inputs for model simulations with the Helsinki University of Technology (HUT) snow emission model. Large differences in depth, density, and grain size were observed between the three land cover types. Plot-scale simulations with the HUT model showed a wide range in simulation accuracy between sites and frequencies. In general, model performance degraded when the effective grain size exceeded 2 mm and/or there was an ice lens present in the pack. HUT model performance improved when simulations were run regionally at the satellite scale (using three proportional land cover tiles: open, forest, and lake) and compared to Advanced Microwave Scanning Radiometer (AMSR-E) measurements. Root mean square error (RMSE) values ranged from approximately 4 to 16 K depending on the frequency, polarization, and land cover composition of the grid cell. Snow water equivalent (SWE) retrievals produced using forward T B simulations with the HUT model in combination with AMSR-E measurements produced RMSE values below 25 mm for the intensive study area. Retrieval errors exceeded 50 mm when the scheme was applied regionally.

Three Along Track Scanning Radiometers (ATSRs) form a series of space-borne instruments specifically optimised to provide accurate remotely sensed measurements of Sea Surface Temperature (SST), which is a key geophysical parameter required to inform the debate on climate change and global warming. These sensors' well-calibrated, high quality data have wide applicability and are being used in a much wider range of earth observation studies and applications, in addition to the planned SST mission. Each successive instrument has been an incremental improvement over its predecessor. Since early 1991 the ATSR sensors have provided global observations from the European Space Agency's Earth Observation satellites; namely ATSR-1 on ERS-1 (European Remote-sensing Satellite), then ATSR-2 on ERS-2 and then AATSR (Advanced ATSR) on ENVISAT. The missions have been operated with good overlaps between successive sensors; AATSR is currently the operational instrument. The fundamental requirement for each ATSR instrument is a design that is capable of delivering absolutely calibrated infrared data; therefore this paper concentrates on how the sensors provide the calibrated radiometric observations required for the SST retrieval algorithms to work. It does not discuss the validation of the algorithms to produce SST derived from these basic observations. Described are the rigorous pre-launch measurements over a range of simulated flight environments which verify that this aim has been achieved, a calibration which exercises the same brightness temperature algorithm that is then used when calibrating in-flight measurements. Crucially, the ATSRs measure calibration source radiances in-flight without interrupting Earth-viewing which permits continuous gain and offset monitoring and calibration. For clarity where it is thought necessary for a data user's better understanding of the calibration for flight data, some detail is provided concerning the differences between the three different ATSRs.

Vegetation phenology characterizes seasonal life-cycle events that influence the carbon cycle and land–atmosphere water and energy exchange. We analyzed global phenology cycles over a six year record (2003–2008) using satellite passive microwave remote sensing based Vegetation Optical Depth (VOD) retrievals derived from daily time series brightness temperature (Tb) measurements from the Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and other ancillary data inputs. The VOD parameter derives vegetation canopy attenuation at a given microwave frequency (18.7 GHz) and varies with canopy height, density, structure and water content. An error sensitivity analysis indicates that the retrieval algorithm can resolve the VOD seasonal cycle over a majority of global vegetated land areas. The VOD results corresponded favorably (p

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80 × 100 m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of −8.4 K and −8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (−5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R = −0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (−4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.

We develop and evaluate water clear of sea ice (open water following ice cover) detection algorithms that make use of Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (σ°) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (TB) measurements. Algorithm validation was performed within Canadian Arctic waters using the Canadian Ice Service Digital Archive (CISDA) ice charts, NASATeam ice concentration estimates, extended AVHRR Polar Pathfinder (APP-x) albedo data, RADARSAT-1 imagery, and MODIS imagery. Results indicate that the temporal evolution of QuikSCAT σ°, AMSR-E polarization ratio (PR18), and AMSR-E vertical spectral gradient ratio (GR3618) can detect water clear of sea ice events, however mean differences due to frequency dependent characteristics of the data (spatial resolution; sensitivity to open water) were apparent. All water clear of sea ice algorithms are in good agreement with the timing and clearing patterns given by the CISDA. The QuikSCAT algorithm provided a more representative ice edge and more details on the ice clearing process due to higher spatial resolution, however, transient clearing events were better represented by the AMSR-E PR(18) or (GR3618) algorithm. By exploiting the strengths of each sensor, we found that a QuikSCAT and AMSR-E fused algorithm provide improved open water area estimates by as much as 11%. The fusion of QuikSCAT and AMSR-E PR(18) yielded in the most spatially representative open water detection. The residual surface of the water clear of sea ice algorithms was found to provide another measure of the average September minimum pan-Arctic sea ice extent within 6% of the NASATeam algorithm estimates.

Airborne and satellite brightness temperature (TB) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37 GHz–19 GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70 × 120 m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37 GHz TB versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130 mm at which point emission from the snowpack creates a positive but noisier relationship between 37 GHz TB and SWE. The change from snowpack scattering to emission was also evident in the temporal evolution of 37 GHz TB observed from satellite measurements. AMSR-E brightness temperatures (2002/03–2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37V|) in vertically polarized 37 GHz TB was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37V| was significantly related to greater (lower) ground measured SWE (r2 = 0.77 with monthly averages; r2 = 0.67 with pentad averages). Σ|Δ37V| was only weakly correlated with lake fraction: monthly r2 values calculated for January through April 2003–2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present).

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190–199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.

Four seasons (2004–2007) of snow surveys across the boreal forest of northern Manitoba were utilized to determine relationships between vertically polarized Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (TB) and ground measurements of snow water equivalent (SWE). Regression analysis identified moderate strength, yet statistically significant relationships between SWE and TB differences (36.5–18.7; 36.5–10.7; 18.7–10.7) for individual seasons. When multiple seasons were considered collectively, however, the 36.5–18.7 and 36.5–10.7 differences were insignificant because the seasonal linear relationships shifted from year to year over the same TB range regardless of SWE. This inter-seasonal consistency in TB was explained through significant correlations with vegetation density as characterized by a MODIS-derived forest transmissivity dataset. More encouraging results were found for the 18.7–10.7 difference: the relationship with SWE remained statistically significant when multiple years were considered together, and the 18.7–10.7 difference was not significantly associated with vegetation density. Additional snow survey data from the Northwest Territories (2005–2007) were used to verify the 18.7–10.7 relationship with SWE across the northern boreal forest. These results suggest use of the 18.7–10.7 TB difference, rather than the traditional 36.5–18.7 TB difference, is necessary to capture inter-seasonal SWE variability across forested regions.

In this paper a method for evaluating land surface temperature (LST) algorithms over heterogeneous areas is presented. The evaluation was made for a set of 12 algorithms derived by using the split-window (SW) and dual-angle (DA) techniques for estimating sea and land surface temperature (SST and LST) from Advanced Along-Track Scanning Radiometer (AATSR) data. A validation of the proposed algorithms was carried out over a heterogeneous region of Morocco in the framework of the WATERMED (WATer use Efficiency in natural vegetation and agricultural areas by Remote sensing in the MEDiterranean basin) project. AATSR data and in situ measurements over this heterogenous region were compared by implementing a classification based strategy over a higher spatial resolution Landsat image. Three reference classes were considered when performing the classification from the Landsat image. Ground based measurements where then used to assign an effective surface radiometric temperature to each of these three classes. Finally, an averaging procedure based on class proportion was implemented for deriving surface radiometric temperature at the AATSR pixel scale. For this heterogeneous site, the results showed that LST can be obtained with a root mean-square error (RMSE) lower than 1.7 K from the split-window algorithms. Dual-angle algorithms, on the other hand, provided greater RMSE due to the different surfaces observed in the nadir and forward views. The results suggest that to retrieve LST from 1 km pixels over heterogeneous surfaces spatial averaging is required to improve accuracy on temperature estimation.

Remote sensing based on active and passive microwave data represents a useful tool for studying the state of the cryosphere at high temporal resolution and large spatial scale. In particular, retrieving snow parameters from space-borne data can benefit hydrological, meteorological, and climatological applications. In this paper, we analyze the trend of Ku band backscatter coefficients measured by the NASA's Quick Scatterometer (QuikSCAT) and K and Ka band brightness temperatures measured by the Special Sensor Microwave Imager (SSM/I) with respect to snow depth values at different locations in the Northern hemisphere during the period 1999–2004. We also quantify, for the first time, the dynamic range of space-borne Ku band scatterometer data over snow covered areas at very large spatial scale in comparison to the range in passive microwave brightness temperatures. Also for the first time, we quantify the improvement on the snow depth retrieval related to the combined use of active and passive data and compare the results with those obtained using either only active or passive data. Finally, we report first results regarding an analysis involving X-band brightness temperatures, collected by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), aiming at understanding whether the improvements derived from using a combination of active and passive data are related to the use of a low frequency or to the different techniques used (e.g., active or passive).

Observations from visible, infrared and microwave satellite instruments are integrated to detect dust storm over northwestern China. Microwave measurements are used to detect the dust storm underneath ice clouds, while visible and infrared measurements are utilized for delineating the cloud-free dust systems. Detection is based on microwave polarized brightness temperature differences (Δ T b = T bv − T bh ) among two channels of 89 GHz and 23.8 GHz and infrared brightness temperature difference (BTD) between channels at 11 and 12 μm. It is shown that the integrated approach is better than the method solely based on infrared BTD in storm detection, especially for those dust systems covered by ice clouds. This approach is applied for the Asia dust storms cases using the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer (AMSR-E) onboard Aqua satellite.

Field experiments (SMEX02) were conducted to evaluate the effects of dense agricultural crop conditions on soil moisture retrieval using passive microwave remote sensing. Aircraft observations were collected using a new version of the Polarimetric Scanning Radiometer (PSR) that provided four C band and four X band frequencies. Observations were also available from the Aqua satellite Advanced Microwave Scanning Radiometer (AMSR-E) at these same frequencies. SMEX02 was conducted over a three-week period during the summer near Ames, Iowa. Corn and soybeans dominate the region. During the study period the corn was approaching its peak water content state and the soybeans were at the mid point of the growth cycle. Aircraft observations are compared to ground observations. Subsequently models are developed to describe the effects of corn and soybeans on soil moisture retrieval. Multiple altitude aircraft brightness temperatures were compared to AMSR-E observations to understand brightness temperature scaling and provide validation. The X-band observations from the two sensors were in reasonable agreement. The AMSR-E C-band observations were contaminated with anthropogenic RFI, which made comparison to the PSR invalid. Aircraft data along with ancillary data were used in a retrieval algorithm to map soil moisture. The PSR estimated soil moisture retrievals on a field-by-field comparison had a standard error of estimate (SEE) of 5.5%. The error reduced when high altitude soil moisture estimates were aggregated to 25 km resolution (same as AMSR-E EASE grid product resolution) (SEE ∼ 2.85%). These soil moisture products provide a validation of the AMSR retrievals. PSR/CX soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions. The dynamic range of the PSR/CX observations indicates that reasonable soil moisture estimates can be obtained from AMSR, even in areas of high vegetation biomass content (∼ 4–8 kg/m2).

The monitoring of snow water equivalent (SWE) and snow depth (SD) in boreal forests is investigated by applying space-borne microwave radiometer data and synoptic snow depth observations. A novel assimilation technique based on (forward) modelling of observed brightness temperatures as a function of snow pack characteristics is introduced. The assimilation technique is a Bayesian approach that weighs the space-borne data and the reference field on SD interpolated from discrete synoptic observations with their estimated statistical accuracy. The results obtained using SSM/I and AMSR-E data for northern Eurasia and Finland indicate that the employment of space-borne data using the assimilation technique improves the SD and SWE retrieval accuracy when compared with the use of values interpolated from synoptic observations. Moreover, the assimilation technique is shown to reduce systematic SWE/SD estimation errors evident in the inversion of space-borne radiometer data.

Optimal deconvolution (ODC) utilizes the footprint overlap in microwave observations to estimate the earth's brightness temperatures (TB). This paper examines the accuracy of ODC-estimated TB compared with a standard averaging technique. Because brightness temperatures cannot be independently verified, we constructed synthetic True TB for accuracy assessment. We assigned TB at a high spatial resolution (1 km) grid and computed the True TB by spatial averaging of the assigned TB to a lower resolution earth grid (25 km), selected to match the resolution of products generated from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). We used the sensor antenna response function along with the 1-km assigned TB to generate synthetic observations at AMSR-E footprint locations. These synthetic observations were subsequently deconvolved in the ODC technique to estimate TB at the lower resolution earth grid. The ODC-estimated TB and the simple grid cell averages of the synthetic observations were compared with the True TB allowing us to quantify the efficacy of each technique. In areas of high TB contrast (such as boundaries of water bodies), ODC performed significantly better than averaging. In other areas, ODC and averaging techniques produced similar results. A technique similar to ODC can be effective in delineating water bodies with significant clarity. That will allow microwave observations to be utilized near the shorelines, a trouble spot for the currently used averaging techniques.

An experimental site was set up in a large, flat and homogeneous area of rice crops for the validation of satellite derived land surface temperature (LST). Experimental campaigns were held in the summers of 2002–2004, when rice crops show full vegetation cover. LSTs were measured radiometrically along transects covering an area of 1 km 2 . A total number of four thermal radiometers were used, which were calibrated and inter-compared through the campaigns. Radiometric temperatures were corrected for emissivity effects using field emissivity and downwelling sky radiance measurements. A database of ground-based LSTs corresponding to morning, cloud-free overpasses of Envisat/Advanced Along-Track Scanning Radiometer (AATSR) and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. Ground LSTs ranged from 25 to 32 °C, with uncertainties between ± 0.5 and ± 0.9 °C. The largest part of these uncertainties was due to the spatial variability of surface temperature. The database was used for the validation of LSTs derived from the operational AATSR and MODIS split-window algorithms, which are currently used to generate the LST product in the L2 level data. A quadratic, emissivity dependent split-window equation applicable to both AATSR and MODIS data was checked as well. Although the number of cases analyzed is limited (five concurrences for AATSR and eleven for MODIS), it can be concluded that the split-window algorithms work well, provided that the characteristics of the area are adequately prescribed, either through the classification of the land cover type and the vegetation cover, or with the surface emissivity. In this case, the AATSR LSTs yielded an average error or bias of − 0.9 °C (ground minus algorithm), with a standard deviation of 0.9 °C. The MODIS LST product agreed well with the ground LSTs, with differences comparable or smaller than the uncertainties of the ground measurements for most of the days (bias of + 0.1 °C and standard deviation of 0.6 °C, for cloud-free cases and viewing angles smaller than 60°). The quadratic split-window algorithm resulted in small average errors (+ 0.3 °C for AATSR and 0.0 °C for MODIS), with differences not exceeding ± 1.0 °C for most of the days (standard deviation of 0.9 °C for AATSR and 0.5 °C for MODIS).

The airborne Millimeter-wave Imaging Radiometer (MIR) and MODIS Airborne Simulator (MAS) measurements over the Arctic region and the Midwest region of the US are used to derive surface emissivities ξ ( ν ) for three frequencies, ν =89, 150, and 220 GHz, as well as Normalized Difference Snow Index (NDSI) and R87 (0.87-μm reflectance). These derived parameters are compared with parameters estimated from near concurrent measurements made by the SSMI and SSM/T-2 over snow-covered areas. It is shown that the MIR-estimated ξ ( ν ) values at ν =89 and 150 GHz agree well with those estimated from the SSM/T-2 at ν =91 and 150 GHz, respectively. Low MIR-estimated ξ ( ν ) values are generally associated with high NDSI and R87 over the snow-covered areas. Over forested areas, more fluctuations in the values of MIR-estimated ξ ( ν ), NDSI and R87, as well as a reduction in polarization index (PI) at 37 and 85 GHz are observed. Both observations and results from radiative transfer calculations show a change in the difference between brightness temperatures ( T b ) at 19 and 37 GHz, as well as PI at 37 and 85 GHz, when measured at satellite altitudes and at the surface. The amplitude of the T b difference and PI is reduced by about 10–15% from surface to high altitudes when integrated water vapor is ≤1.5 g/cm 2 . This effect is readily correctable and requires consideration when validating satellite retrieval algorithms based on surface and low-elevation aircraft measurements.

Motivated by the importance to weather and climate of the Indo-Pacific seas, we present a new calibration of the Visible Infrared Spin-Scan Radiometer (VISSR) on the geostationary meteorological satellite, GMS-5. VISSR imagery has significant potential for exploring the dynamics of the ocean and air–sea interactions in this poorly characterized region, by virtue of the VISSR's surface temperature retrieval capability and hourly sampling. However, the calibration of the thermal imagery supplied by the Japanese Meteorological Agency (JMA) is inconsistent with the spectral characteristics of the channels, and published details of the JMA calibration procedure are scant. We use the well-characterized Along-Track Scanning Radiometer 2 (ATSR-2) as a reference, and determine calibration corrections for GMS-5 VISSR. We obtain more credible VISSR brightness temperatures and demonstrate sea surface temperature (SST) retrieval that validates well against in situ measurements (bias ∼0.3 and scatter ∼0.4 K). Comparison with a widely used sea surface temperature analysis shows that the GMS-5 VISSR SST fields capture important spatial structure, absent in the analysis.

The MODIS/ASTER Airborne Simulator was developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) projects. ASTER and MODIS are both spaceborne imaging instruments on the Terra platform launched in the fall of 1999. Currently MASTER is flown on the Department of Energy (DOE) King Air Beachcraft B200 aircraft and the NASA DC-8. In order to validate the in-flight performance of the instrument, the Jet Propulsion Laboratory and the University of Arizona conducted a joint experiment in December 1998. The experiment involved overflights of the MASTER instrument at two sites at three elevations (2000, 4000, and 6000 m). The two sites: Ivanpah Playa, California, and Lake Mead, Nevada, were selected to validate the visible–shortwave infrared and thermal infrared (TIR) channels, respectively. At Ivanpah Playa, a spectrometer was used to determine the surface reflectance and a sun photometer used to obtain the optical depth. At Lake Mead contact and radiometric surface lake temperatures were measured by buoy-mounted thermistors and self-calibrating radiometers, respectively. Atmospheric profiles of temperature, pressure, and relative humidity were obtained by launching an atmospheric sounding balloon. The measured surface radiances were then propagated to the at-sensor radiance using radiative transfer models driven by the local atmospheric data. There was excellent agreement between the predicted radiance at sensor and the measured radiance at sensor at all three altitudes. The percent difference between the channels not strongly affected by the atmosphere in the visible–shortwave infrared was typically 1–5% and the percent difference between the TIR channels not strongly affected by the atmosphere was typically less than 0.5%. These results indicate the MASTER instrument should provide a well-calibrated instrument for Earth Science Studies. It should prove particularly valuable for those studies that leverage information across the electromagnetic spectrum from the visible to the TIR.

There is an increasing need for data on land-cover changes at broad spatial .scales. In this study, a remote sensing-based technique for land-cover-change analysis was applied to the African continent for the last decade. Ten years of daily, continental-.scale satellite remote sensing data (front the National Oceanic and Atmospheric Administration's advanced very-high resolution radiometer) were analyzed. Deviations in. the seasonal trajectory of the land surface—characterized by its brightness temperature and a vegetation index—were interpreted in terms of land-cover change on a yearly basis, from 1982 to 1991. Land-cover-change magnitudes were measured and mapped for every year. The major categories of seasonal patterns of changes were identified. Multiyear patterns of change also were categorized, and all areas affected by a continuous decrease and gain in vegetation cover over the decade were snapped. Results were related to published high-resolution remote sensing-based studies, rainfall data, and other evidence of land-cover changes. This study's results demonstrate that inte-rannual land-cover changes in Africa mostly involve erratic variations in land-corer conditions due to interannual climatic variability and temporary nwdifncations in seasonality. Continuous, unidirectional change processes (decrease or gain in vegetation cover) affected less than 4% of sub-Saharan Africa during the study period. Althongh such linear changes are just a fraction of the total land-cover changes detected, their effect on ecosystems and sustainability of livelihood might be significant. In the semiarid regions, temporary modification of the biophysical attributes of the surface driven by variations in rainfall is the dominant process. Future work will focus on finer-scale interpretation and validations of patterns of changes.