Brightness Temperature Retrieval Research Articles

Synergistic Evaluation of Passive Microwave and Optical/IR Data for Modelling Vegetation Transmissivity towards Improved Soil Moisture Retrieval.

Vegetation cover and soil surface roughness are vital parameters in the soil moisture retrieval algorithms. Due to the high sensitivity of passive microwave and optical observations to Vegetation Water Content (VWC), this study assesses the integration of these two types of data to approximate the effect of vegetation on passive microwave Brightness Temperature (BT) to obtain the vegetation transmissivity parameter. For this purpose, a newly introduced index named Passive microwave and Optical Vegetation Index (POVI) was developed to improve the representativeness of VWC and converted into vegetation transmissivity through linear and nonlinear modelling approaches. The modified vegetation transmissivity is then applied in the Simultaneous Land Parameters Retrieval Model (SLPRM), which is an error minimization method for better retrieval of BT. Afterwards, the Volumetric Soil Moisture (VSM), Land Surface Temperature (LST) as well as canopy temperature (TC) were retrieved through this method in a central region of Iran (300 × 130 km2) from November 2015 to August 2016. The algorithm validation returned promising results, with a 20% improvement in soil moisture retrieval.

L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation, and Soil Salinity

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (<i>SM</i>), soil temperature, soil salinity and vegetation, using L-band microwave brightness temperature (<i>T</i>_B) observations and retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions, Catchment Land Surface Model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based <i>SM</i> outperformed CLSM <i>SM</i> when evaluated against field data, and forward L-band <i>T</i>_B simulations derived from in situ <i>SM</i> and temperature performed better than those derived from CLSM estimates when evaluated against SMOS <i>T</i>_B observations. The surface salinity for the sampled pixels was on average only 4 mg&#x002F;g and only locally influenced the <i>T</i>_B simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize <i>T</i>_B simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multi-angular SMOS <i>T</i>_B data and constraints of CLSM <i>SM</i> and temperature. However, the latter modeled <i>SM</i> was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo and surface roughness resulted in realistic values.

Geolocation of RFIs by Multiple Snapshot Difference Method for Synthetic Aperture Interferometric Radiometer

The presence of unauthorized radio frequency interference (RFI) sources seriously affects the retrieval of brightness temperature of a synthetic aperture interferometric radiometer (SAIR). Postobservation RFI geolocation is important for improving the RFI situation hereafter and mitigating the impact of the identified RFIs. Previous works have demonstrated the potential of using high-/super-resolution direction-of-arrival (DOA) estimation techniques to achieve accurate localization of RFI sources in SAIR imagery. In this article, a multiple snapshot difference method is proposed to further reduce the RFI source geolocation bias. The covariance matrices of the consecutive snapshots with power changed RFIs are picked out and subtracted to cancel out the background scene. A MUSIC-based DOA algorithm is developed. Numerical simulations validate the theoretical correctness and the practical effectiveness of the proposed method.

2-D Lunar Microwave Radiance Observations From the NOAA-20 ATMS

Reported here are disk-integrated Moon surface microwave brightness temperature ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Tb$ </tex-math></inline-formula> ) retrievals covering the frequency range of 23–183 GHz. Full Moon observations obtained from the advanced technology microwave sounder (ATMS) onboard the NOAA-20 satellite during a special spacecraft pitch–maneuver operation forms the basis of the retrievals. Instrument nonlinearity, Earth sidelobe contamination, cosmic background radiation, and reflector thermal emission corrections are applied to the observations to obtain accurate values of the Moon’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Tb$ </tex-math></inline-formula> at all frequencies. The measured full Moon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Tb$ </tex-math></inline-formula> ranges from ~240 to 293 K with frequency increases from 23 to 183 GHz. A clear frequency trend is detected when the brightness temperature increases.

Surface Roughness Effect on L-Band Multiangular Brightness Temperature Modeling and Soil Liquid Water Retrieval of Frozen Soil

A less explored aspect of L-band microwave emission is the surface roughness effect on the multiangular brightness temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> ) modeling and retrieval of liquid water content (θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> ) in frozen soil. This letter investigates this effect based on a data set of multiangular ETH L-band radiometer (ELBARA-III) T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> measurements performed in a seasonally frozen Tibetan meadow ecosystem. The surface roughness effect is first investigated via comparing the performance of the HQN model and Shi's parameterization of the integral equation model in simulating the measured T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> signatures. Overestimations are noted for the two model simulations at the vertical polarization in comparison to the ELBARA-III measurements, and Shi's model with Gaussian autocorrelation function shows better performance especially for the vertical polarization at large incidence angles. The HQN model is then calibrated with the multiangular T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> measurements, which leads to h = 0 and Q > 0 that depends on the polarization. As such, the noncoherent emission contribution to cross-polarization mixing can be accounted for by the HQN model. Furthermore, θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> retrievals are derived from the multiangular T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> measurements using the default and optimized HQN model and Shi's model and compared to the in situ θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> . Unbiased root-mean-square errors of less than 0.025 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> are obtained for these retrievals, which are well within the satellite mission requirements. This study demonstrates that the multiangular T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> modeling can be enhanced with site-specific calibration of the HQN model that also leads to the improvement of the θ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">liq</sub> retrievals, and consideration of polarization mixing is necessary for L-band emission modeling.

Retrieval of Sea Surface Temperature From HY-2A Scanning Microwave Radiometer

The scanning microwave radiometer (RM) onboard the Haiyang-2A (HY-2A) satellite has low-frequency channels with the capability of observing sea surface temperature (SST) from space. To improve the accuracy of the HY-2A RM SST data, the intercalibration of RM brightness temperature (BT) and SST retrieval were carried out. Based on the simulated BTs using the microwave radiative transfer model (RTM), the double-difference approach was applied to perform the intercalibration of RM BTs for the 10.7, 18.7, 23.8, and 37.0 GHz channels, with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) as the reference. The RM 6.6-GHz BTs were corrected with the RTM modeled BT on account of a lack of the similar channel in GMI. The comparison of RM original BTs with GMI and modeled BTs showed large biases and the strong dependence on latitude. We obtained latitude-dependent coefficients using robust linear regression for RM BT correction and applied recalibrated RM BTs for the SST retrieval. The validation of the RM retrieved SST showed the bias of −0.12 °C and the robust standard deviation (RSD) of 1.10 °C compared with buoy SST in the region between 70°S and 70°N. In the tropical and subtropical regions, the bias was −0.12 °C and the RSD was 0.93 °C. In addition, the relationships between the SST difference and the sea surface and atmospheric parameters were investigated. Both statistics of validation results and error analysis indicated significant improvement of RM SST accuracy.

A Subspace Algorithm to Mitigate Energy Unknown RFI for Synthetic Aperture Interferometric Radiometer

Radio frequency interferences (RFIs) are present in the data collected by a synthetic aperture interferometric radiometer (SAIR), and these would seriously affect the retrieval of brightness temperature (BT), if not properly handled. Due to the truncated Fourier characteristic, RFIs create ripples that extend over a large set of pixels of the whole image, which is difficult to cancel out their impacts at the BT domain. The current strategy relies on removing the unwanted sources at the visibility level (Fourier domain) with a set of synthetic visibilities of the RFI sources for a given snapshot. However, a major problem is that, as unauthorized emissions, the energies of RFIs are typically unknown and the estimation to the sources’ temperature could be inaccurate without credible prior information. In this article, a subspace algorithm is proposed to handle the RFI mitigation problem by exploiting RFI’s point-source characteristic, where the visibilities related to the RFIs are excluded from that of the BT scene through an orthogonal design without the need of knowing the sources’ power. Both the numerical simulations and the proof-of-concept experiment validate the theoretical correctness and the practical effectiveness of our proposed method.

Global Assessment of the SMAP Freeze/Thaw Data Record and Regional Applications for Detecting Spring Onset and Frost Events

More than half of the global land area undergoes seasonal frozen and thawed conditions that constrain eco-hydrological processes. The freeze-thaw (FT) retrieval from satellite microwave remote sensing detects landscape changes between frozen and non-frozen conditions due to the strong dependence of surface microwave emissions on liquid water abundance. We conducted an assessment of the latest version (R16) of the NASA Soil Moisture Active Passive (SMAP) Level 3 FT (L3_FT) global product. The L3_FT product provides a global FT classification with 3-day mean temporal fidelity derived using SMAP L-band (1.4 GHz) microwave brightness temperature (Tb) retrievals. The R16 product uses both normalized polarization ratio (NPR) and single channel vertically-polarized Tb (FT-SCV) algorithms to obtain FT retrievals over land areas where frozen temperatures are a significant ecological constraint. The L3_FT product is generated in a standard global grid with similar grid cell resolution (36-km) as the SMAP radiometer footprint. An enhanced 9-km global grid L3_FT product is also produced from optimally interpolated SMAP Tb retrievals. The resulting L3_FT products span a larger domain and longer period (2015–present) than earlier product releases. The L3_FT 36-km results showed a respective global mean annual FT classification accuracy of approximately 78 and 90 percent for descending (AM) and ascending (PM) orbit observations in relation to independent surface air temperature-based FT estimates from ~5000 global weather stations. The FT accuracy was lower in areas with greater terrain complexity, open water and vegetation cover; where the combined land cover factors explained 29–53% of the variability in the SMAP FT global accuracy. The L3_FT 9-km product showed an apparent enhancement of FT spatial patterns, but with ~4% lower accuracy than the 36-km product; the lower 9-km accuracy was attributed to stronger degradation from land cover heterogeneity, particularly in coastal areas, and artifact noise introduced from the spatial interpolation of SMAP Tb retrievals. Selected regional applications indicated product utility in capturing anomalous frost events over Australia and seasonal thaw and spring onset patterns over Alaska. Overall, the L3_FT global accuracy meets or exceeds the FT product science requirements established by the mission, while enabling studies of dynamic FT and water mobility constraints influencing hydrological and ecosystem processes, and global water-carbon-energy cycle linkages.

Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada

Variations in land surface albedo and snow-cover strongly impact the global biosphere, particularly through the snow-albedo feedback on climate. The seasonal freeze-thaw (FT) transition is coupled with snowpack melt dynamics and strongly impacts surface water mobility and the energy budget in the northern (≥45°N) arctic and boreal region (ABR). However, understanding of the regional variation in snowmelt and its effect on the surface energy budget are limited due to sparse in situ measurements of these processes and environmental constraints on effective monitoring within the ABR. In this study, we combined synergistic observations from overlapping satellite optical-infrared and microwave sensor records to quantify the regional patterns and seasonal progression in wet snow conditions during the spring snowmelt and autumn snow accumulation periods across Alaska and western Canada. The integrated satellite record included daily landscape FT status from AMSR microwave brightness temperature retrievals; and snow-cover extent, black sky albedo and net shortwave solar radiation (Rsnet) derived from MODIS and AVHRR observations. The integrated satellite records were analyzed with in situ surface air temperature and humidity observations from regional weather stations over a two-year study period (2015–2016) overlapping with the NASA ABoVE (Arctic Boreal Vulnerability Experiment). Our results show a large (79%) mean decline in land surface albedo between dry snow and snow-free conditions during the spring (March–June) and autumn (August–November) transition periods. Onset of diurnal thawing and refreezing of the surface snow layer and associated wet snow conditions in spring contributed to an approximate 25% decrease in snow cover albedo that extended over a seven to 21 week snowpack depletion period. The lower wet snow albedo enhances Rsnet by approximately 74% (9–10 MJ m−2 d−1) relative to dry snow conditions, reinforcing snowmelt and surface warming, and contributing to growing season onset and activation of biological and hydrological processes in the ABR. These results contribute to better understanding of snow albedo feedbacks to Arctic amplification, and the representation of these processes in global Earth system models.

Simultaneous retrieval of temperature and area according to sub-pixel hotspots from nighttime Landsat 8 OLI data

The monitoring of thermal anomalies such as from wildfires, coal fires, and volcanoes has been a major application of satellite remote sensing. The Landsat 8 Operational Land Imager (OLI) has achieved sufficient enhanced radiometric performance to quantify visible and near infrared (VNIR) to shortwave infrared (SWIR) radiance emitted from thermal anomalies during nighttime. We propose a method to simultaneously retrieve the brightness temperature and corresponding area of a subpixel hotspot assuming a single isothermal heat source using the spectral radiances in VNIR and SWIR regions (bands 3–7) from nighttime Landsat 8 OLI data. We conducted a prescribed fire experiment coincident with a Landsat 8 overpass on January 24, 2015. The brightness temperature retrieved from the OLI data agreed with the peak temperature of the time series thermocouple record within 60K. The retrieved area of fire was 19% of the true fire area. The blackbody assumption (emissivity=1) might cause the underestimated retrieved area. We applied the hotspot brightness temperature and area retrieval to 17 Landsat 8 scenes that cover Kanto, Japan to validate the proposed method. More than 79% of the detected results were confirmed as true positives based on the land use for each scene. Meanwhile, the obvious false positive was only up to 1% of the detected results. The remaining ambiguous results, mostly found in areas regarding agriculture, were possibly caused by occasional burnings because these locations were never repetitively detected. Time series comparison of the retrieved results in cement plants as a typical stable heat source revealed that the brightness temperature is accurately retrieved, though the retrieval of area is somewhat sensitive to noise on OLI imagery. The proposed method had founded more than twice of the hotspots detected using Visible Infrared Imaging Radiometer Suite (VIIRS) fire products in the studied area. More than 82% among the hotspots from VIIRS were detected by OLI. However our method failed to detect spatially broad but slightly cool hotspots. Higher spatial resolution at an appropriate spectral configuration enabled enhanced sensitivity to detect small heat sources, as well as quantification of the subpixel hotspots.

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

Abstract. Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002–December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (Tmx and Tmn), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003–2010) and AMSR2 (2013–2015) observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE ≤ 0.06), PWV (R ≥ 0.91; RMSE ≤ 4.94 mm), Tmx and Tmn (R ≥ 0.90; RMSE ≤ 3.48 °C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE ≤ 0.06 cm3 cm−3). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at http://files.ntsg.umt.edu/data/LPDR_v2/.

Standing water effect on soil moisture retrieval from L-band passive microwave observations

Abstract 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.

Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

Satellite optical-infrared remote sensing from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides effective air temperature (Ta) retrieval at a spatial resolution of 5 km. However, frequent cloud cover can result in substantial signal loss and remote sensing retrieval error in MODIS Ta. We presented a simple pixel-wise empirical regression method combining synergistic information from MODIS Ta and 37 GHz frequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for estimating surface level Ta under both clear and cloudy sky conditions in the United States for 2006. The instantaneous Ta retrievals showed favorable agreement with in situ air temperature records from 40 AmeriFlux tower sites; mean R2 correspondence was 86.5 and 82.7 percent, while root mean square errors (RMSE) for the Ta retrievals were 4.58 K and 4.99 K for clear and cloudy sky conditions, respectively. Daily mean Ta was estimated using the instantaneous Ta retrievals from day/night overpasses, and showed favorable agreement with local tower measurements (R2 = 0.88; RMSE = 3.48 K). The results of this study indicate that the combination of MODIS and AMSR-E sensor data can produce Ta retrievals with reasonable accuracy and relatively fine spatial resolution (~5 km) for clear and cloudy sky conditions.

Evaluation of Land Surface Temperature Retrieved from MODIS Data

Land surface temperature (LST) is important factor in global climate change studies, radiation budgets estimating, city heat and others. In this paper, land surface temperature of Guangzhou metropolis was retrieved from two MODIS imageries obtained at night and during the day respectively. Firstly, pixel values were calibrated to spectral radiances according to parameters from header files. Then, the brightness temperature was calculated using Planck function. Finally, The brightness temperature retrieval maps were projected and output. Comparing two brightness temperature retrieval maps, it is concluded that the brightness temperature retrieval are more accurate at night than during the day. Comparing the profile line of brightness temperature from north to south, the brightness temperature increases from north to south. Temperature different from north to south is larger at night than during the day. The average temperature nears 18°C at night and the average temperature nears 26°C during the day, which is consistent with the surface temperature observed by automatic weather stations.

Effects of Antenna Cross-Polarization Coupling on the Brightness Temperature Retrieval at L-Band

Retrieval of the brightness temperature (TB) at the L-band is studied in the context of the remote sensing of ocean surface salinity. The measurement of antenna temperature and the retrieval of TB are simulated with a radiative transfer model and an observing system model of an orbiting spacecraft. Two sets of antenna gain patterns are used: 1) theoretical analysis and 2) measurements of a 1/10th-size scale model. The latter set notably shows the large cross-polarization coupling from the first Stokes transmit into the third Stokes receive. The large cross-polarization coupling causes an error of up to 4° in the estimate of the Faraday rotation angle (the size of the angle itself is mostly less than 15° in the severe ionospheric condition). By this amount of the error, an additional rotation is introduced to the retrieval of the second and third Stokes TBs in front of the feed horn before the Faraday rotation correction. The additional rotation also degrades the performance of the antenna pattern correction (APC). However, when the Faraday rotation correction is performed by the square sum of the two retrieved Stokes, the retrieved TB below the ionosphere and at the top of the atmosphere after the Faraday correction becomes insensitive to the additional rotation (i.e., being insensitive to the error in the Faraday angle estimate and the rotational error in the APC). The formal proof of the insensitivity is presented. The first and second Stokes TBs at the top of the atmosphere observed at 5.6-s intervals from space may be retrieved with an error smaller than 0.1-K rms without the accurate ancillary information of the gain pattern and the Faraday rotation angle, assuming correct calibration, 0.08 K NEΔT for the radiometer noise, and accurate correction or flagging of the solar and galaxy radiation.

An Improved C-Band Ocean Surface Emissivity Model at Hurricane-Force Wind Speeds Over a Wide Range of Earth Incidence Angles

An improved microwave radiometric ocean surface emissivity model has been developed to support forward radiative transfer modeling of brightness temperature and geophysical retrieval algorithms for the next-generation airborne Hurricane Imaging Radiometer instrument. This physically based C-band emissivity model extends current model capabilities to hurricane-force wind speeds over a wide range of incidence angles. It was primarily developed using brightness temperature observations during hurricanes with coincident high-quality surface-truth wind speeds, which were obtained using the airborne Stepped-Frequency Microwave Radiometer. Also, other ocean emissivity models available through the published literature and the spaceborne WindSat radiometer measurements were used.

Lunar 3He estimations and related parameters analyses

As a potential nuclear fuel, 3He element is significant for both the solution of impending human energy crisis and the conservation of natural environment. Lunar regolith contains abundant and easily extracted 3He. Based on the analyses of the impact factors of 3He abundance, here we have compared a few key assessment parameters and approaches used in lunar regolith 3He reserve estimation and some representative estimation results, and discussed the issues concerned in 3He abundance variation and 3He reserve estimation. Our studies suggest that in a range of at least meters deep, 3He abundance in lunar regolith is homogeneously distributed and generally does not depend on the depth; lunar regolith has long been in a saturation state of 3He trapped by minerals through chemical bonds, and the temperature fluctuation on the lunar surface exerts little influence on the lattice 3He abundance. In terms of above conclusions and the newest lunar regolith depth data from the microwave brightness temperature retrieval of the “ChangE-1” Lunar Microwave Sounder, a new 3He reserve estimation has been presented.

INFLUENCE OF DUST AEROSOL ON THE BRIGHTNESS TEMPERATURE OF THERMAL INFRARED SPLIT WINDOW AND LAND SURFACE TEMPERATURE RETRIEVAL

Dust aerosol can cause the change of the land surface temperature(LST) by altering the thermal infrared radiative transfer of earth-atmosphere system.The influence of different winter and summer dust aerosol optical depth(AOD) on thermal infrared brightness temperature(BT) and LST retrieval was systematically studied and the inversion results influenced by water vapor in the atmosphere and surface factor were also studied.Thermal infrared radiative transfer calculation indicates that 1.brightness temperature difference is less than 0 under the influence of dust aerosol;2.BT and LST decrease with the increase of AOD,in which the decreasing trend of LST is more obvious;3.LST changes very little with different water vapor content;4.LST mainly reflects the temperature of dust layer for bigger AOD.These results agree well with the instance analysis of typical dust in the north of China.

Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP

A global 2‐month comparison is presented between the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Moderate Resolution Imaging Spectroradiometer (MODIS) for both cloud detection and cloud top height (CTH) retrievals. Both CALIOP and MODIS are part of the NASA A‐Train constellation of satellites and provide continuous near‐coincident measurements that result in over 28 million cloud detection comparisons and over 5 million CTH comparisons for the months of August 2006 and February 2007. To facilitate the comparison, a computationally efficient and accurate collocation methodology is developed. With the collocated MODIS and CALIOP retrievals, nearly instantaneous comparisons are compiled regionally and globally. Globally, it is found that the MODIS 1‐km cloud mask and the CALIOP 1‐km averaged layer product agreement is 87% for cloudy conditions for both August 2006 and February 2007. For clear‐sky conditions the agreement is 85% (86%) for August (February). The best agreement is found for nonpolar daytime and the poorest agreement in the polar regions. Differences in cloud top heights depend strongly on cloud type. Globally, MODIS underestimates the CTH relative to CALIOP by 1.4 ± 2.9 km for both August 2006 and February 2007. This value of 1.4 km is obtained using the CALIOP 1 km layer products. When compared to the CALIOP 5‐km products, the differences increase to −2.6 ± 3.9 km as a result of CALIOP's increased sensitivity to optically thin cirrus. When only high clouds above 5 km are considered, the differences are found to be greater than 4 km with individual comparisons having differences larger than 10 km. These large differences (&gt;10 km) represent approximately 16% of the nonpolar high cloud retrievals (&gt;5 km). For high clouds it is found that MODIS retrieves a cloud top height for 90% of the clouds detected by the CALIOP 5‐km layer products. The large MODIS underestimates for optically thin cirrus occur for cases when MODIS reverts to a window brightness temperature retrieval instead of CO2 slicing. A systematic bias is found for marine low‐level stratus clouds, with MODIS overestimating the CTH by over 1 km in dense marine stratocumulus regions. The cause of the bias was identified in the MODIS Collection 5 algorithm; an application of a modified algorithm reduced this bias.

The sensitivity of radiative transfer calculations to the changes in the HITRAN database from 1982 to 2004

Over the last quarter century, improvements in the determination of the spectroscopic characteristics of the infrared-active trace species have enhanced our ability to retrieve quantitative distributions of temperatures, clouds, and abundances for various trace species within the Earth's atmosphere. These improvements have also allowed for refinements in the estimates of climatic effects attributed to changes in the Earth's atmospheric composition. Modeling efforts, however, have frequently experienced significant delays in assimilating improved spectroscopic information. Such is the case for highly parameterized models, where considerable effort is typically required to incorporate any revisions. Thus, a line-by-line radiative transfer model has been used to investigate the magnitude of the effects resulting from modifications to the spectroscopic information. Calculations from this line-by-line model have demonstrated that recent modifications to the HITRAN (High Resolution Transmission) line parameters, the continuum formulation, and the CO 2 line-mixing formulation can significantly affect the interpretation of the high spectral resolution radiance and brightness temperature retrievals. For certain moderate-resolution satellite remote sensing channels, modifications to these spectroscopic parameters and formulations have shown the capacity to induce changes in the calculated radiances equivalent to brightness temperature differences of 1–2 K. Model calculations have further shown that modifications of the spectroscopic characteristics tend to have a modest effect on the determination of spectrally integrated radiances, fluxes, and radiative forcing estimates, with the largest differences being of order 1 W m −2 for the total thermal infrared fluxes, and of order 2–3% of the calculated radiative forcing at the tropopause attributed to the combined doubling of CO 2, N 2O, and CH 4. The results from this investigation are intended to function as a guide to differentiate between cases where older parameterizations provide acceptable results, within specified accuracy bounds, and cases where upgrades to the latest spectroscopic database are necessary.