Search

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]

Abstract Detection and mitigation of radio frequency interference (RFI) is one of the most crucial issues in microwave remote sensing measurements. This study analyzes the brightness temperature and RFI acquired by the soil moisture active passive (SMAP) satellite L-band radiometer over China and its offshore areas, revealing that the RFI distribution is uneven. In China, RFI is mainly concentrated in the urban agglomerations and their surrounding areas. Further, RFI and population density show strong positive correlation. A comparison of the radiometer antenna brightness temperature data and the data after RFI detection and mitigation shows that the volume of the detected RFI is ∼1.4 K and the standard deviation is 6.5. Under the influence of RFI, different bands and polarizations in the study area did not show significant differences. [ABSTRACT FROM AUTHOR]

In the present paper, permittivity maps of equatorial lunar surface are generated using brightness temperature ( T B ) data obtained from Microwave Radiometer (MRM) of Chang'e-1 and physical temperature ( T P ) data obtained from Diviner of Lunar Reconnaissance Orbiter (LRO). Here, permittivity mapping is not carried out above 60° latitudes towards the lunar poles due to large anomaly in the physical temperature obtained from the Diviner. Microwave frequencies, which are used to generate these maps are 3 GHz, 7.8 GHz, 19.35 GHz and 37 GHz. Permittivity values are simulated using T B values at these four frequencies. Here, weighted average of physical temperature obtained from Diviner are used to compute permittivity at each microwave frequencies. Longer wavelengths of microwave signals give information of more deeper layers of the lunar surface as compared to smaller wavelength. Initially, microwave emissivity is estimated using T B values from MRM and physical temperature ( T P ) from Diviner. From estimated emissivity the real part of permittivity ( ε ), is calculated using Fresnel equations. The permittivity maps of equatorial lunar surface is generated. The simulated permittivity values are normalized with respect to density for easy comparison of simulated permittivity values with the permittivity values of Apollo samples as well as with the permittivity values of Terrestrial Analogue of Lunar Soil (TALS) JSC-1A. Lower value of dielectric constant ( ε ′) indicates that the corresponding lunar surface is smooth and doesn't have rough rocky terrain. Thus a future lunar astronaut can use these data to decide proper landing site for future lunar missions. The results of this paper will serve as input to future exploration of lunar surface. [ABSTRACT FROM AUTHOR]

Microwave radiometer is an effective instrument to monitor the atmosphere continuously in different weather conditions. It measures brightness temperatures at different frequency bands which are subjected to standard retrieval methods to obtain real time profiles of various atmospheric parameters such as temperature and humidity. But the retrieval techniques used by radiometer have to be adaptive to changing weather condition and location. In the present study, three retrieval techniques have been used to obtain the temperature and relative humidity profiles from brightness temperatures, namely; piecewise linear regression, feed forward neural network and neural back propagation network. The simulated results are compared with radiosonde observations using correlation analysis and error distribution. The analysis reveals that neural network with back propagation is the most accurate technique amongst the three retrieval methods utilized in this study. [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]

In the present study, the effectiveness of nowcasting convective activities using a microwave radiometer has been examined for Kolkata (22.65° N, 88.45° E), a tropical location. It has been found that the standard deviation of brightness temperature (BT) at 22 GHz and instability indices like Lifting Index (LI), K Index (KI) and Humidity Index (HI) has shown definite changes before convective events. It is also seen that combination of standard deviation of BT at 22 GHz and LI can be most effective in predicting convection. A nowcasting algorithm is prepared using 18 isolated convective events of 2011 and in all cases, a marked variation of these parameters has been seen an hour before the event. Accordingly, a prediction model is developed and tested on convective events of 2012 and 2013. It is seen that the model gives reasonable success in predicting convective rain about 7075 min in advance with a prediction efficiency of 80%. [ABSTRACT FROM AUTHOR]

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: A multi-channel microwave radiometre (make: Radiometrics Corporation) is installed at Instituto Nacional de Pesquisas Espaciais–INPE, Brazil (22°S). The radiometric output of two channels of the radiometer in the form of brightness temperature at 23.834GHz and 30GHz, initially, were used to find out the ambient water vapor content and the non-precipitable cloud liquid water content. The necessary algorithm was developed for the purpose. The best results were obtained using the hinge frequency 23.834GHz and 30GHz pair having an r.m.s. error of only 2.64. The same methodology was then adopted exploiting 23.034GHz and 30GHz pair. In that case the r.m.s. error was 3.42. These results were then compared with those obtained over Kolkata (22°N), India, by using 22.234GHz and 31.4GHz radiometric data. This work conclusively suggests the use of a frequency should not be at the water vapor resonance line. Instead, while measuring the vapor content for separation of vapor and cloud liquid, one of them should be a few GHz left or right from the resonance line i.e., at 23.834GHz and the other one should be around 30GHz. [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]

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]