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A new multilayer (ML) cloud detection algorithm based on three shortwave infrared (SWIR) and two longwave infrared (LWIR) channels is developed and applied to the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. The algorithm identifies ML clouds, i.e., ice clouds overlying water clouds, based on satellite multispectral observations in the 1.38, 1.6, 2.25, 8.5, and 11 μm channels. We perform synthetic radiative transfer simulations to understand the sensitivities of the aforementioned channels on ML and single-layer (SL) clouds. Active CALIOP observations are used to evaluate the algorithm. Compared with the collocated CALIOP results, the algorithm can determine SL and ML clouds correctly with success rates of approximately 80% and 60%, respectively, and has similar performance to that of the current MODIS operational ML cloud detection algorithm. The misclassification of ML clouds as SL clouds is primarily caused by thin ice clouds that are practically undetectable using LWIR tests. Furthermore, the algorithm is extended to analyze data from radiometers onboard the geostationary Himawari-8 and FengYun-4A satellites, and results similar to those of VIIRS are obtained. • A new algorithm for multilayer cloud detection is developed for VIIRS radiometer. • Accurate radiative transfer is performed for model understanding and development. • The model is evaluated using collocated MODIS and CALIOP observations. • It is extended for multilayer cloud detection of two geostationary radiometers. [ABSTRACT FROM AUTHOR]

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. • A single-pass estimation of active-passive microwave covariation is established. • The approach allows estimation of covariation without the need for time series. • It is observation-driven and does not require site-specific calibration. • It enables global downscaling of SMAP radiometer with Sentinel-1 radar data. [ABSTRACT FROM AUTHOR]

L-band brightness temperature (TB) has been shown to provide the best sensitivity to soil moisture (SM) although C- and X-band based products offer a longer time-series from satellite-based measurements. Currently, global coverage SM is routinely produced from spaceborne measurements using all three frequency bands, but despite continued validation efforts of the products, the relative characteristics and performance of these observations have not been fully established. Therefore, this study focused on the parametrization of SM retrieval algorithms at L-, C- and X-bands using TB observations from the L-band radiometer on NASA's SM Active Passive (SMAP) mission and the C- and X-band channels of JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite. These can be applied in global SM retrieval algorithms using either one of the frequencies or a combination of them. The reference in situ SM data was obtained from 12 core validation sites across various land cover types around the world. The investigation highlighted the known challenges of retrieving SM from C- and X-band data compared to the higher sensitivity of the L-band data. Even with a site-specific retrieval algorithm parameterization, the mean correlation of the C- and X-band retrievals for the core validation site SM measurement were much lower than that for L-band, being 0.52 (0.54) and 0.45 (0.47) for vertical (horizontal) polarization, respectively, while for the L-band retrieval the corresponding values were 0.81 (0.77). The parameterization exercise showed that matching the C- and X-band TB measurements with an emission model was not difficult; the problem was relating the observations to SM under the influence of large roughness and vegetation effects. As a result, parameter optimization produced values for some sites that were not realistic or did not allow any practical sensitivity to SM at C- and X-band. Considering the L-band observations, the parameter optimization resulted in superior bias performance as compared to the operational SMAP product parameterization, but the sensitivity to SM changes (R and unbiased root mean square difference) did not improve markedly, or in some cases degraded at the expense of a smaller bias. • Spaceborne radiometer observation at L/C/X-band over 12 soil moisture core sites. • Calibration of soil moisture retrieval algorithm for L/C/X-band using the core sites. • L-band retrieval achieves correlation of 0.8 while C/X-band achieves only 0.5. • Retrieval capability of C/X-band questionable over some of the sites. • Calibration of L-band improves bias and RMSD over SMAP product but not sensitivity. [ABSTRACT FROM AUTHOR]

Satellite microwave observations from 1.4 to 36 GHz already showed sensitivity to several geophysical parameters of sea ice such as Sea Ice Concentration (SIC), Sea Ice Thickness (SIT) or snow depth. The main goal of this article is to provide a realistic and comprehensive characterization of the sea ice and its snow cover that explains the microwave observations during a whole year using a radiative transfer model. For this purpose, we construct a unique dataset of passive microwave observations, to mimic the future Copernicus Imaging Microwave Radiometer (CIMR), along with the active microwave scatterometer data (ASCAT). CIMR database is used to classify sea ice microwave signatures in their spectral dimension with a machine learning technique while ASCAT data are used to help interpret the results of the classification. Classification results are then interpreted with a state-of-art sea ice and Snow Microwave Radiative Transfer model (SMRT) for all highlighted signatures and all seasons. Results make it possible to identify the specific behaviors from the observation co-variabilities for SIC, SIT, and snow structure. Our analysis underlined the role of the depth hoar over multi-year ice, for the interpretation of scattering signals in winter. Scattering signals that appear in late summer are explained by the presence of superimposed ice. This characterization will benefit from future advances in SMRT development, as well as the improved observations of future satellite missions. • Realistic sea ice and snow characterization is made for a round year. • Kohonen classification highlights sea ice microwave signature. • Radiative transfer modSel links classification results and geophysical parameters. • Depth hoar play an important role in microwave scattering explanation. [ABSTRACT FROM AUTHOR]

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979–2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04 m 3 m − 3 , respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007–2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates. [ABSTRACT FROM AUTHOR]

Abstract: We analyze the surface emissivity and radar backscatter measured by the Aquarius L-band radiometer and scatterometer under rainy conditions. The residual signals due to rain are derived from measurements after accounting for roughness due to wind and flat surface emissivity. The wind roughness is accounted for by a geophysical model function (GMF) built using rain-free data. Using more than one year of Aquarius data collocated with SSMI/S, WindSAT rain rate and NCEP wind, our analysis reveals rain rate dependence in radar backscatter and surface emissivity, which become increasingly significant as the wind speed approaches zero and not significant above 15ms−1. The effect of rain on radar backscatter is dominated by raindrop splashing as indicated by the incidence angle dependence and polarization characteristics of the residuals. [Copyright &y& Elsevier]

In the TOPSAR acquisition mode, Sentinel-1 sensors acquire some data in passive mode, with a periodicity of approximately one second. These data provide valuable information about scene brightness and Radio Frequency pollution. This paper reviews the method to extract these passively sensed data and identify Radio Frequency Interferences (RFI). Two sets of measurements are generated by back-projecting passively sensed data on the ground, generating Earth Brightness Temperature (BT) and RFI equivalent temperature. A proper calibration has been implemented thanks to AMSR-2 data. The paper shows the result of one year of data collection from Sentinel 1 A and B regarding RFI and BT. We discuss the stability of the systems and the accuracy of the measurements by cross-comparing results from the two sensors, and we show how this information can be exploited for precise SAR data denoising and RFI mitigation. • Sentinel-1 TOPSAR mode senses Earth Brightness and Radio Frequency Interferences. • Brightness maps can be generated over most landmasses and nearby sea-ocean. • C-band Radio Frequency emissions can be detected with a sensitivity of hundred of K and continuously monitored World-Wide. • A geometric resolution of kilometers can be achieved by back-projecting ascending and descending passes. • These maps are used to enhance SAR products quality by denoising and removing RFI. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: The key variable describing global seasonal snow cover is snow water equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40mm for cases when SWE <150mm. Retrieval uncertainty increases when SWE is above this threshold. The SWE estimates are also compared with results obtained by a typical stand-alone satellite passive microwave algorithm. This comparison demonstrates the benefits of the newly developed assimilation approach. Additionally, the trends and inter-annual variability of northern hemisphere snow mass during the era of satellite passive microwave measurements are shown. [Copyright &y& Elsevier]

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r 2 NDVI =0.90; r 2 CHL3 =0.85; p <0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll... [Copyright &y& Elsevier]

Abstract: Optimal deconvolution (ODC) utilizes the footprint overlap in microwave observations to estimate the earth''s brightness temperatures (T B). This paper examines the accuracy of ODC-estimated T B compared with a standard averaging technique. Because brightness temperatures cannot be independently verified, we constructed synthetic True T B for accuracy assessment. We assigned T B at a high spatial resolution (1 km) grid and computed the True T B by spatial averaging of the assigned T B 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 T B to generate synthetic observations at AMSR-E footprint locations. These synthetic observations were subsequently deconvolved in the ODC technique to estimate T B at the lower resolution earth grid. The ODC-estimated T B and the simple grid cell averages of the synthetic observations were compared with the True T B allowing us to quantify the efficacy of each technique. In areas of high T B 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. [Copyright &y& Elsevier]

Operated since the end of 2009, the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite mission is the first orbiting radiometer that collects regular and global observations from space of two Essential Climate Variables of the Global Climate Observing System: Sea Surface Salinity (SSS) and Soil Moisture. The National Aeronautics and Space Administration (NASA) Aquarius mission, with the primary objective to provide global SSS measurements from space operated from mid-2011 to mid-2015. NASA's Soil Moisture Active-Passive (SMAP) mission, primarily dedicated to soil moisture measurements, but also monitoring SSS, has been operating since early 2015. The primary sensors onboard these three missions are passive microwave radiometers operating at 1.4 GHz (L-band). SSS is retrieved from radiometer measurements of the sea surface brightness temperature (T B). In this paper, we first provide a historical review of SSS remote sensing with passive L-band radiometry beginning with the discussions of measurement principles, technology, sensing characteristics and complementarities of the three aforementioned missions. The assessment of satellite SSS products is then presented in terms of individual mission characteristics, common algorithms, and measurement uncertainties, including the validation versus in situ data, and, the consideration of sampling differences between satellite SSS and in situ salinity measurements. We next review the major scientific achievements of the combined first 10 years of satellite SSS data, including the insights enabled by these measurements regarding the linkages of SSS with the global water cycle, climate variability, and ocean biochemistry. We also highlight the new ability provided by satellites to monitor mesoscale and synoptic-scale SSS features and to advance our understanding of SSS' role in air-sea interactions, constraining ocean models, and improving seasonal predictions. An overview of satellite SSS observation highlights during this first decade and upcoming challenges are then presented. • Historical review of sea surface salinity estimates with passive L-band radiometry • SMOS, Aquarius, and SMAP sensor characteristics and algorithms are presented. • Quality assessment of latest satellite SSS products is provided. • The major scientific achievements of the first decade of satellite SSS are reviewed. [ABSTRACT FROM AUTHOR]

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. • The article is about the high resolution (1 km and 3 km) soil moisture data from the SMAP mission. • The soil moisture is produced using the SMAP L-band TB and Sentinel 1A/B C-band Sigma0. • The high resolution soil moisture has reasonable accuracy (RMSE) of ~0.05 m3/m3. • The SMAP-Sentinel1 high resolution soil moisture product is ready for geophysical applications. • The temporal frequency of the this product is ~12 days, however, in Europe it is 6 days. [ABSTRACT FROM AUTHOR]