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1
Yin, Chuan, Ming Zhang, and Yaming Bo. "Multilayer Brightness Temperature Tracing Method for Rough Surface Scene Simulation in Passive Millimeter-Wave Imaging." International Journal of Antennas and Propagation 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6763182.
Повний текст джерелаАнотація:
Simulation in passive millimeter-wave (MMW) imaging of rough surfaces is an indispensable step in the simulation in passive radiation imaging, especially for the rough surfaces of different roughness surfaces. However, little attention has been paid to the simulation of rough surface; based on the existing model of brightness temperature tracing described in previous work, diffused reflection of the rough surface is taken into account in the improved model which is presented in this paper. In the paper, the brightness temperature tracing model of different roughness surfaces has been established. Then, we present a method called multilayer brightness temperature tracing (MBTT) method to obtain the radiation brightness temperature of rough surface. Hence, the discrimination of brightness temperature tracing method is enhanced.
2
Shi, Jiu Xi, Jin Song Deng, and Xiao Ming Wang. "Characteristic Analysis of Rural Environment Temperature Field." Advanced Materials Research 807-809 (September 2013): 14–19. http://dx.doi.org/10.4028/www.scientific.net/amr.807-809.14.
Повний текст джерелаАнотація:
Taking villages in the northern plain of Shaoxing County Zhejiang Province as the research object and by using heat transfer model and remote sensing image analysis method and taking advantage of surface temperature information varying in different areas recorded by ETM thermal infrared band and through selection of special endmember, we realize the separation of background and ambient superposed brightness temperature and establish statistical model on change of superimposed environmental brightness temperature based on distance and analyze characteristics of rural environment temperature field according to the features of heat exchange type. Study shows that endmember brightness temperatures of different surface features in the study area are respectively as follows: hard surface is 304.663K, water body is 297.851K, grassland is 298.966K, woodland is 298.827K; superimposed environmental temperature in village area is about 1.737K. Environment superposed brightness temperature and distance function are tools to describe the temperature field, predicting pixel brightness temperature by using the heat transfer model is more accurate than using linear spectrum mixed model.
3
Holbach, Heather M., Eric W. Uhlhorn, and Mark A. Bourassa. "Off-Nadir SFMR Brightness Temperature Measurements in High-Wind Conditions." Journal of Atmospheric and Oceanic Technology 35, no. 9 (September 2018): 1865–79. http://dx.doi.org/10.1175/jtech-d-18-0005.1.
Повний текст джерелаАнотація:
AbstractWind and wave-breaking directions are investigated as potential sources of an asymmetry identified in off-nadir remotely sensed measurements of ocean surface brightness temperatures obtained by the Stepped Frequency Microwave Radiometer (SFMR) in high-wind conditions, including in tropical cyclones. Surface wind speed, which dynamically couples the atmosphere and ocean, can be inferred from SFMR ocean surface brightness temperature measurements using a radiative transfer model and an inversion algorithm. The accuracy of the ocean surface brightness temperature to wind speed calibration relies on accurate knowledge of the surface variables that are influencing the ocean surface brightness temperature. Previous studies have identified wind direction signals in horizontally polarized radiometer measurements in low to moderate (0–20 m s−1) wind conditions over a wide range of incidence angles. This study finds that the azimuthal asymmetry in the off-nadir SFMR brightness temperature measurements is also likely a function of wind direction and extends the results of these previous studies to high-wind conditions. The off-nadir measurements from the SFMR provide critical data for improving the understanding of the relationships between brightness temperature, surface wave–breaking direction, and surface wind vectors at various incidence angles, which is extremely useful for the development of geophysical model functions for instruments like the Hurricane Imaging Radiometer (HIRAD).
4
Yang, Xiao Feng, and Xing Ping Wen. "Evaluation of Land Surface Temperature Retrieved from MODIS Data." Advanced Materials Research 785-786 (September 2013): 1333–36. http://dx.doi.org/10.4028/www.scientific.net/amr.785-786.1333.
Повний текст джерелаАнотація:
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.
5
Winebrenner, Dale P., Eric J. Steig, and David P. Schneider. "Temporal co-variation of surface and microwave brightness temperatures in Antarctica, with implications for the observation of surface temperature variability using satellite data." Annals of Glaciology 39 (2004): 346–50. http://dx.doi.org/10.3189/172756404781813952.
Повний текст джерелаАнотація:
AbstractSatellite observations of microwave emission are a key resource for estimating surface temperatures in Antarctica. Use of these data to examine climate variability, however, relies on the assumption of constancy through time in the relationship between surface temperatures and the proxy brightness temperatures. Thus we are motivated to study the physical relationship between surface and brightness temperature time series, and to seek indicators of possible temporal variability in that relationship. Here we report an initial study using near-surface temperatures from the Byrd Station automated weather station in West Antarctica and 37 GHz, vertically polarized brightness temperatures from the Scanning Multichannel Microwave Radiometer. We begin with the simplest model of the relevant thermal and microwave physics and derive a convolution expression that relates surface and brightness temperatures. The convolution kernel depends on firn thermal diffusivity and the microwave extinction coefficient in a particularly simple way: solely through a single characteristic time-scale. For the Byrd data, we find that the (fractional variation in) observed brightness temperatures can be reproduced by our model in considerable detail, on scales from interannual down to a few days. The time-scale is tightly constrained by minimization of the discrepancy between observed and simulated time series, and the optimized value agrees closely with that derived from independent estimates of firn thermal and microwave parameters. We find no evidence thus far of temporal variability in the relation between surface and brightness temperatures, though investigation across a wider domain in space and time is needed before such variability can be ruled out.
6
Sherjal, I., and M. Fily. "Temporal variations of microwave brightness temperatures over Antarctica." Annals of Glaciology 20 (1994): 19–25. http://dx.doi.org/10.3189/1994aog20-1-19-25.
Повний текст джерелаАнотація:
Passive microwave brightness temperatures from the Special Sensor Microwave Imager (SSMI) are studied together with surface air temperatures from two Automatic Weather Stations (AWS) for the year 1989. One station is located on the East Antarctic plateau (Dome C) and the other on the Ross lee Shelf (Lettau).The satellite data for frequencies 19, 22 and 37 GHz with vertical polarization,centered on the two AWS stations, are studied. A simple thermodynamic model and asimple radiative-transfer model, that takes into account the snow temperature profile and assumes a constant annual emissivity, are proposed. The combination of these two models enables us to compute extinction coefficients, penetration depths and toretrieve the measured brightness temperature variations from the AWS surface temperatures. Afterwards, this model is reversed in order to retrieve the snow-surface temperatures from the satellite data. Results are promising but strong approximationsand a priori knowledge of the extinction coefficient are still needed at this point.
7
Sherjal, I., and M. Fily. "Temporal variations of microwave brightness temperatures over Antarctica." Annals of Glaciology 20 (1994): 19–25. http://dx.doi.org/10.1017/s0260305500016177.
Повний текст джерелаАнотація:
Passive microwave brightness temperatures from the Special Sensor Microwave Imager (SSMI) are studied together with surface air temperatures from two Automatic Weather Stations (AWS) for the year 1989. One station is located on the East Antarctic plateau (Dome C) and the other on the Ross lee Shelf (Lettau).The satellite data for frequencies 19, 22 and 37 GHz with vertical polarization,centered on the two AWS stations, are studied. A simple thermodynamic model and asimple radiative-transfer model, that takes into account the snow temperature profile and assumes a constant annual emissivity, are proposed. The combination of these two models enables us to compute extinction coefficients, penetration depths and toretrieve the measured brightness temperature variations from the AWS surface temperatures. Afterwards, this model is reversed in order to retrieve the snow-surface temperatures from the satellite data. Results are promising but strong approximationsand a priori knowledge of the extinction coefficient are still needed at this point.
8
Stephen, H., S. Ahmad, and T. C. Piechota. "Land Surface Brightness Temperature Modeling Using Solar Insolation." IEEE Transactions on Geoscience and Remote Sensing 48, no. 1 (January 2010): 491–98. http://dx.doi.org/10.1109/tgrs.2009.2026893.
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Gaustad, John E. "Temperature and brightness variations on Betelgeuse." Symposium - International Astronomical Union 118 (1986): 449–50. http://dx.doi.org/10.1017/s0074180900151885.
Повний текст джерелаАнотація:
Changes in Ti0 band strengths correlate well with the brightness changes of α Orionis, thus supporting the hypothesis of Schwarzschild that the irregular luminosity variations of red giants are due to temperature changes in a few extremely large convective elements on their surface.
10
Chen, Xiuzhi, Yongxian Su, Yong Li, Liusheng Han, Jishan Liao, and Shenbin Yang. "Retrieving China’s surface soil moisture and land surface temperature using AMSR-E brightness temperatures." Remote Sensing Letters 5, no. 7 (July 3, 2014): 662–71. http://dx.doi.org/10.1080/2150704x.2014.960610.
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11
Sandells, Melody, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose. "Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels." Cryosphere 18, no. 9 (September 4, 2024): 3971–90. http://dx.doi.org/10.5194/tc-18-3971-2024.
Повний текст джерелаАнотація:
Abstract. Accurate simulations of snow emission in surface-sensitive microwave channels are needed to separate snow from atmospheric information essential for numerical weather prediction. Measurements from a field campaign in Trail Valley Creek, Inuvik, Canada, during March 2018 were used to evaluate the Snow Microwave Radiative Transfer (SMRT) model at 89 GHz and, for the first time, frequencies between 118 and 243 GHz. In situ data from 29 snow pits, including snow specific surface area, were used to calculate exponential correlation lengths to represent the snow microstructure and to initialize snowpacks for simulation with SMRT. Measured variability in snowpack properties was used to estimate uncertainty in the simulations. SMRT was coupled with the Atmospheric Radiative Transfer Simulator to account for the directionally dependent emission and attenuation of radiation by the atmosphere. This is a major developmental step needed for top-of-atmosphere simulations of microwave brightness temperature at atmosphere-sensitive frequencies with SMRT. Nadir-simulated brightness temperatures at 89, 118, 157, 183 and 243 GHz were compared with airborne measurements and with ground-based measurements at 89 GHz. Inclusion of anisotropic atmospheric radiance in SMRT had the greatest impact on brightness temperature simulations at 183 GHz and the least impact at 89 GHz. Medians of simulations compared well with medians of observations, with a root mean squared difference of 14 K across five frequencies and two flights (n=10). However, snow pit measurements did not capture the observed variability fully as simulations and airborne observations formed statistically different distributions. Topographical differences in simulated brightness temperature between sloped, valley and plateau areas diminished with increasing frequency as the penetration depth within the snow decreased and less emission from the underlying ground contributed to the airborne observations. Observed brightness temperature differences between flights were attributed to the deposition of a thin layer of very-low-density snow. This illustrates the need to account for both temporal and spatial variabilities in surface snow microstructure at these frequencies. Sensitivity to snow properties and the ability to reflect changes in observed brightness temperature across the frequency range for different landscapes, as demonstrated by SMRT, are necessary conditions for inclusion of atmospheric measurements at surface-sensitive frequencies in numerical weather prediction.
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Brucker, Ludovic, Ghislain Picard, Laurent Arnaud, Jean-Marc Barnola, Martin Schneebeli, Hélène Brunjail, Eric Lefebvre, and Michel Fily. "Modeling time series of microwave brightness temperature at Dome C, Antarctica, using vertically resolved snow temperature and microstructure measurements." Journal of Glaciology 57, no. 201 (2011): 171–82. http://dx.doi.org/10.3189/002214311795306736.
Повний текст джерелаАнотація:
AbstractTime series of observed microwave brightness temperatures at Dome C, East Antarctic plateau, were modeled over 27 months with a multilayer microwave emission model based on dense-medium radiative transfer theory. The modeled time series of brightness temperature at 18.7 and 36.5 GHz were compared with Advanced Microwave Scanning Radiometer–EOS observations. The model uses in situ high-resolution vertical profiles of temperature, snow density and grain size. The snow grain-size profile was derived from near-infrared (NIR) reflectance photography of a snow pit wall in the range 850–1100 nm. To establish the snow grain-size profile, from the NIR reflectance and the specific surface area of snow, two empirical relationships and a theoretical relationship were considered. In all cases, the modeled brightness temperatures were overestimated, and the grain-size profile had to be scaled to increase the scattering by snow grains. Using a scaling factor and a constant snow grain size below 3 m depth (i.e. below the image-derived snow pit grain-size profile), brightness temperatures were explained with a root-mean-square error close to 1 K. Most of this error is due to an overestimation of the predicted brightness temperature in summer at 36.5 GHz.
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Wrigge, Matthias, You-Hua Chu, Eugene A. Magnier, and Yuichi Kamata. "ASCA SIS X-ray Observations of the Wind Blown Bubble NGC 6888." International Astronomical Union Colloquium 166 (1997): 425–28. http://dx.doi.org/10.1017/s0252921100071372.
Повний текст джерелаАнотація:
AbstractWe present ASCA SIS observations of the wind-blown bubble NGC 6888. Because the ASCA SIS is sensitive to higher energy photons and has a higher spectral resolution compared to the ROSAT PSPC, we are able to detect a T ≈ 8×106 K plasma component besides the T ≈ 1.5×106 K component known from previous PSPC observations. The existence of a high-temperature component, the observed limb-brightened X-ray surface brightness profile, and the observed level of X-ray surface brightness cannot be satisfactorily explained by currently available models. Reducing heat conduction at the contact discontinuity may raise the central temperature and produce a limb-brightening; however, the expected X-ray surface brightness is still considerably higher than the observed surface brightness.
14
Grecu, Mircea, and William S. Olson. "Bayesian Estimation of Precipitation from Satellite Passive Microwave Observations Using Combined Radar–Radiometer Retrievals." Journal of Applied Meteorology and Climatology 45, no. 3 (March 1, 2006): 416–33. http://dx.doi.org/10.1175/jam2360.1.
Повний текст джерелаАнотація:
Abstract Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation–brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation–brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal components and the brightness temperature principal components of the classes. Because the classes are stratified by the sea surface temperature and the echo-top-height estimator, the number of classes that are considered for retrieval is significantly smaller than the total number of classes, making the algorithm computationally efficient. The radiometer-only algorithm is applied to TMI observations, and precipitation estimates are compared with combined TRMM precipitation radar (PR)–TMI reference estimates. The TMI-only algorithm, supported by the empirically derived database, produces estimates that are more consistent with the reference values than the precipitation estimates from the version-6 TRMM facility TMI algorithm. Cloud-resolving model simulations are used to assign a latent heating profile to each precipitation profile in the empirically derived database, making it possible to estimate latent heating using the radiometer-only algorithm. Although the evaluation of latent heating estimates in this study is preliminary, because realistic conditional probability distribution functions are attached to latent heating structures in the algorithm's database, a generally positive impact on latent heating estimation from passive microwave observations is expected.
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McFarland, M. J., R. L. Miller, and C. M. U. Neale. "Land surface temperature derived from the SSM/I passive microwave brightness temperatures." IEEE Transactions on Geoscience and Remote Sensing 28, no. 5 (1990): 839–45. http://dx.doi.org/10.1109/36.58971.
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Richter, Friedrich, Matthias Drusch, Lars Kaleschke, Nina Maaß, Xiangshan Tian-Kunze, and Susanne Mecklenburg. "Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models." Cryosphere 12, no. 3 (March 14, 2018): 921–33. http://dx.doi.org/10.5194/tc-12-921-2018.
Повний текст джерелаАнотація:
Abstract. Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.
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Scott, K. Andrea, Mark Buehner, Alain Caya, and Tom Carrieres. "Direct Assimilation of AMSR-E Brightness Temperatures for Estimating Sea Ice Concentration." Monthly Weather Review 140, no. 3 (February 1, 2012): 997–1013. http://dx.doi.org/10.1175/mwr-d-11-00014.1.
Повний текст джерелаАнотація:
Abstract In this paper a method to directly assimilate brightness temperatures from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) to produce ice concentration analyses within a three-dimensional variational data assimilation system is investigated. To assimilate the brightness temperatures a simple radiative transfer model is used as the forward model that maps the state vector to the observation space. This allows brightness temperatures to be modeled for all channels as a function of the total ice concentration, surface wind speed, sea surface temperature, ice temperature, vertically integrated water vapor, and vertically integrated cloud liquid water. The brightness temperatures estimated by the radiative transfer model are sensitive to the specified values for the sea ice emissivity. In this paper, two methods of specifying the sea ice emissivity are compared. The first uses a constant value for each polarization and frequency, while the second uses a simple emissivity parameterization. The emissivity parameterization is found to significantly improve the fit to the observations, reducing both the bias and the standard deviation. Results from the assimilation of brightness temperatures are compared with those from assimilating a retrieved ice concentration in the context of initializing a coupled ice–ocean model for an area along the east coast of Canada. It is found that with the emissivity parameterization the assimilation of brightness temperatures produces ice concentration analyses that are in slightly better agreement with operational ice charts than when assimilating an ice concentration retrieval, with the most significant improvements during the melt season.
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Mote, Thomas L., Mark R. Anderson, Karl C. Kuivinen, and Clinton M. Rowe. "Passive microwave-derived spatial and temporal variations of summer melt on the Greenland ice sheet." Annals of Glaciology 17 (1993): 233–38. http://dx.doi.org/10.3189/s0260305500012891.
Повний текст джерелаАнотація:
Passive microwave-brightness temperatures over the Greenland ice sheet are examined during the melt season in order to develop a technique for determining surface-melt occurrences. Time series of Special Sensor Microwave/ Imager (SSM/I) data are examined for three locations on the ice sheet, two of which are known to experience melt. These two sites demonstrate a rapid increase in brightness temperatures in late spring to early summer, a prolonged period of elevated brightness temperatures during the summer, and a rapid decrease in brightness temperatures during late summer. This increase in brightness temperatures is associated with surface snow melting. An objective technique is developed to extract melt occurrences from the brightness-temperature time series. Of the two sites with summer melt, the site at the lower elevation had a longer period between the initial and final melt days and had more total days classified as melt during 1988 and 1989. The technique is then applied to the entire Greenland ice sheet for the first major surface-melt event of 1989. The melt-zone signal is mapped from late May to early June to demonstrate the advance and subsequent retreat of one “melt wave”. The use of such a technique to determine melt duration and extent for multiple years may provide an indication of climate change.
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Mote, Thomas L., Mark R. Anderson, Karl C. Kuivinen, and Clinton M. Rowe. "Passive microwave-derived spatial and temporal variations of summer melt on the Greenland ice sheet." Annals of Glaciology 17 (1993): 233–38. http://dx.doi.org/10.1017/s0260305500012891.
Повний текст джерелаАнотація:
Passive microwave-brightness temperatures over the Greenland ice sheet are examined during the melt season in order to develop a technique for determining surface-melt occurrences. Time series of Special Sensor Microwave/ Imager (SSM/I) data are examined for three locations on the ice sheet, two of which are known to experience melt. These two sites demonstrate a rapid increase in brightness temperatures in late spring to early summer, a prolonged period of elevated brightness temperatures during the summer, and a rapid decrease in brightness temperatures during late summer. This increase in brightness temperatures is associated with surface snow melting. An objective technique is developed to extract melt occurrences from the brightness-temperature time series. Of the two sites with summer melt, the site at the lower elevation had a longer period between the initial and final melt days and had more total days classified as melt during 1988 and 1989. The technique is then applied to the entire Greenland ice sheet for the first major surface-melt event of 1989. The melt-zone signal is mapped from late May to early June to demonstrate the advance and subsequent retreat of one “melt wave”. The use of such a technique to determine melt duration and extent for multiple years may provide an indication of climate change.
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Burgard, Clara, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe. "The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output." Cryosphere 14, no. 7 (July 23, 2020): 2369–86. http://dx.doi.org/10.5194/tc-14-2369-2020.
Повний текст джерелаАнотація:
Abstract. We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in cold conditions from a simplified linear temperature profile and a simplified salinity profile as a function of depth in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In warm conditions, the simplified salinity profiles lead to brine volume fractions that are too high in the subsurface layer. To overcome this limitation, we suggest using a constant brightness temperature for the ice during warm conditions and treating melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of wet snow properties. As periods of snow with intermediate moisture content, typically occurring in spring and fall, locally last for less than a month, our approach allows one to estimate realistic brightness temperatures at 6.9 GHz from climate model output for most of the year.
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Mould, Jeremy. "The Metallicity Sensitivity of a Surface Brightness Temperature Scale." Publications of the Astronomical Society of the Pacific 131, no. 1003 (July 18, 2019): 094201. http://dx.doi.org/10.1088/1538-3873/ab29e0.
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Pestana, Steven J., C. Chris Chickadel, and Jessica D. Lundquist. "Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign." Cryosphere 18, no. 5 (May 7, 2024): 2257–76. http://dx.doi.org/10.5194/tc-18-2257-2024.
Повний текст джерелаАнотація:
Abstract. The high temporal resolution of thermal infrared imagery from the Geostationary Operational Environmental Satellites R-series (GOES-R) presents an opportunity to observe mountain snow and forest temperatures over the full diurnal cycle. However, the off-nadir views of these imagers may impact or bias temperature observations, especially when viewing a surface composed of both snow and forests. We used GOES-16 and -17 thermal infrared brightness temperature observations of a flat snow- and forest-covered study site at Grand Mesa, Colorado, USA, to characterize how forest coverage and view angle impact these observations. These two geostationary satellites provided views of the study area from the southeast (134.1° azimuth, 33.5° elevation) and southwest (221.2° azimuth, 35.9° elevation), respectively. As part of the NASA SnowEx field campaign in February 2020, coincident brightness temperature observations from ground-based and airborne IR sensors were collected to compare with those from the geostationary satellites. Observations over the course of 2 cloud-free days spanned the entire study site. The brightness temperature observations from each dataset were compared to find their relative differences and how those differences may have varied over time and/or as a function of varying forest cover across the study area. GOES-16 and -17 brightness temperatures were found to match the diurnal cycle and temperature range within ∼ 1 h and ± 3 K of ground-based observations. GOES-16 and -17 were both biased warmer than nadir-looking airborne IR and ASTER observations. The warm biases were higher at times when the sun–satellite phase angle was near its daily minimum. The phase angle, the angle between the direction of incoming solar illumination and the direction from which the satellite is viewing, reached daily minimums in the morning for GOES-16 and afternoon for GOES-17. In morning observations, warm biases in GOES-16 brightness temperature were greater for pixels that contained more forest coverage. The observations suggest that a “thermal infrared shadow-hiding” effect may be occurring, where the geostationary satellites are preferentially seeing the warmer sunlit sides of trees at different times of day. These biases are important to understand for applications using GOES-R brightness temperatures or derived land surface temperatures (LSTs) over areas with surface roughness features, such as forests, that could exhibit a thermal infrared shadow-hiding effect.
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Lakhankar, T., J. Muñoz, P. Romanov, A. M. Powell, N. Krakauer, W. Rossow, and R. Khanbilvardi. "CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data." Hydrology and Earth System Sciences Discussions 9, no. 7 (July 4, 2012): 8105–36. http://dx.doi.org/10.5194/hessd-9-8105-2012.
Повний текст джерелаАнотація:
Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during winter 2011 at the research site of the National Weather Service office, Caribou ME, USA. In this ground experiment, dual polarized microwave (37 and 89 GHz) observations are conducted along with detailed synchronous observations of snowpack properties. The objective of this long term field experiment is to improve our understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations in the microwave spectral range. In this paper, we presented the overview of field experiment and preliminary analysis of the microwave observations for the first year of experiment along with support observations of the snowpack properties obtained during the 2011 winter season. SNTHERM and HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures respectively. Simulated brightness temperatures were compared with observed brightness temperature from radiometer under different snow conditions. On the time series, large difference in the brightness temperature were observed for fresh compared to aged snow even under the same snow depth, suggesting a substantial impact of other parameters such as: snow grain size and density on microwave observations. A large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization factor was observed due to cold nights and warm days, which caused a cycling between wet snow and ice-over-snow states during the early spring. Scattering analysis of microwave brightness temperatures from radiometers were performed to distinguished different snow conditions developed through the winter season.
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Alasgah, Abdusalam, Maria Jacob, Linwood Jones, and Larry Schneider. "Validation of the Hurricane Imaging Radiometer Forward Radiative Transfer Model for a Convective Rain Event." Remote Sensing 11, no. 22 (November 13, 2019): 2650. http://dx.doi.org/10.3390/rs11222650.
Повний текст джерелаАнотація:
The airborne Hurricane Imaging Radiometer (HIRAD) was developed to remotely sense hurricane surface wind speed (WS) and rain rate (RR) from a high-altitude aircraft. The approach was to obtain simultaneous brightness temperature measurements over a wide frequency range to independently retrieve the WS and RR. In the absence of rain, the WS retrieval has been robust; however, for moderate to high rain rates, the joint WS/RR retrieval has not been successful. The objective of this paper was to resolve this issue by developing an improved forward radiative transfer model (RTM) for the HIRAD cross-track viewing geometry, with separated upwelling and specularly reflected downwelling atmospheric paths. Furthermore, this paper presents empirical results from an unplanned opportunity that occurred when HIRAD measured brightness temperatures over an intense tropical squall line, which was simultaneously observed by a ground based NEXRAD (Next Generation Weather Radar) radar. The independently derived NEXRAD RR created the simultaneous 3D rain field “surface truth”, which was used as an input to the RTM to generate HIRAD modeled brightness temperatures. This paper presents favorable results of comparisons of theoretical and the simultaneous, collocated HIRAD brightness temperature measurements that validate the accuracy of this new HIRAD RTM.
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Cecil, Daniel J. "Passive Microwave Brightness Temperatures as Proxies for Hailstorms." Journal of Applied Meteorology and Climatology 48, no. 6 (June 1, 2009): 1281–86. http://dx.doi.org/10.1175/2009jamc2125.1.
Повний текст джерелаАнотація:
Abstract The Tropical Rainfall Measuring Mission (TRMM) satellite has been used to infer distributions of intense thunderstorms. Besides the lightning measurements from TRMM, the radar reflectivities and passive microwave brightness temperatures have been used as proxies for convective vigor. This is based on large graupel or hail lofted by strong updrafts being the cause of high–radar reflectivity values aloft and extremely low brightness temperatures. This paper seeks to empirically confirm that extremely low brightness temperatures are often accompanied by large hail at the surface. The three frequencies examined (85, 37, and 19 GHz) all show an increasing likelihood of hail reports with decreasing brightness temperature. Quantification is limited by the sparsity of hail reports. Hail reports are common when brightness temperatures are below 70 K at 85 GHz, 180 K at 37 GHz, or 230 K at 19 GHz.
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Bettenhausen, Michael H., and Magdalena D. Anguelova. "Brightness Temperature Sensitivity to Whitecap Fraction at Millimeter Wavelengths." Remote Sensing 11, no. 17 (August 29, 2019): 2036. http://dx.doi.org/10.3390/rs11172036.
Повний текст джерелаАнотація:
Accurate representation of the ocean-atmosphere coupling in weather, wave and climate models requires reliable estimates of air-sea surface fluxes of momentum, heat and mass. Whitecap fraction (W) usually quantifies the enhancement of the surface fluxes due to wave breaking. Satellite-based passive remote sensing of W from ocean surface brightness temperatures ( T B s) observes open ocean surface fluxes at low spatial resolution. Radiometric surface observations at higher resolution are necessary to monitor the complex environment in the coastal zone and in polar regions. We assess the feasibility of using the millimeter-wave frequencies (89 to 150 GHz) to observe whitecaps. We evaluate the derivative of the T B with respect to W as a measure for the observation of W. We describe the models and data used to evaluate the T B sensitivity to W for different instrumental and environmental conditions. Atmospheric absorption limits the ability to observe the surface at millimeter-wave frequencies. We find that the T B sensitivity to W at 89 GHz may be sufficient to support limited W retrieval from observations at altitudes below 1 km and that the T B sensitivity at 113 and 150 GHz is not sufficient. Clear skies, and low to moderate atmospheric humidity favor whitecap observations.
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Maass, Nina, Lars Kaleschke, Xiangshan Tian-Kunze, and Rasmus T. Tonboe. "Snow thickness retrieval from L-band brightness temperatures: a model comparison." Annals of Glaciology 56, no. 69 (2015): 9–17. http://dx.doi.org/10.3189/2015aog69a886.
Повний текст джерелаАнотація:
AbstractThe Soil Moisture and Ocean Salinity (SMOS) satellite’s L-band (1.4 GHz) measurements have been used to retrieve Snow thickness over thick sea Ice in a previous study. Here we consider brightness temperature simulations for 2.5–4.5m thick Arctic multi-year Ice and compare the results of the relatively simple emission model (M2013) used previously for the retrieval with simulations from a more complex model (T2011) that combines a sea-Ice version of the Microwave Emission Model for Layered Snowpacks (MEMLS) with a thermodynamic model. We find that L-band brightness temperature is mainly determined by Ice temperature. In the M2013 model, Ice temperature in turn is mainly determined by surface temperature and Snow thickness, and this dependence has been used previously to explain the potential for a Snow thickness retrieval. Our comparisons suggest that the M2013 retrieval model may benefit from a more sophisticated thermodynamic calculation of the Ice temperature or from using independent temperature data (e.g. from 6 GHz channels). In both models, horizontally polarized brightness temperatures increase with Snow thickness while holding surface temperature, Ice thickness and Snow density near constant. The increase in the T2011 model is steeper than in M2013, suggesting a higher sensitivity to Snow thickness than found earlier.
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Brown, Shannon, Shailen Desai, Stephen Keihm, and Wenwen Lu. "Microwave Radiometer Calibration on Decadal Time Scales Using On-Earth Brightness Temperature References: Application to the TOPEX Microwave Radiometer." Journal of Atmospheric and Oceanic Technology 26, no. 12 (December 1, 2009): 2579–91. http://dx.doi.org/10.1175/2009jtecha1305.1.
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Abstract A method is described to calibrate a satellite microwave radiometer operating near 18–37 GHz on decadal time scales for the purposes of climate studies. The method uses stable on-earth brightness temperature references over the full dynamic range of on-earth brightness temperatures to stabilize the radiometer calibration and is applied to the Ocean Topography Experiment (TOPEX) Microwave Radiometer (TMR). These references are a vicarious cold reference, which is a statistical lower bound on ocean surface brightness temperature, and heavily vegetated, pseudoblackbody regions in the Amazon rain forest. The sensitivity of the on-earth references to climate variability is assessed. No significant climate sensitivity is found in the cold reference, as it is not sensitive to a climate minimum (e.g., coldest sea surface temperature or driest atmosphere) but arises because of a minimum in the sea surface radio brightness that occurs in the middle of the climatic distribution of sea surface temperatures (SSTs). The hot reference is observed to have a small climate dependency, which is most evident during the 1997/98 El Niño event. A time-dependent model for the hot reference region is constructed using meteorological fields from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis product. This model is shown to accurately account for the small climate variations in this reference. In addition to the long-term stabilization of the brightness temperatures, an improvement to the TMR antenna pattern correction is described that removes residual geographically correlated errors, in particular errors correlated with distance to land or sea ice. The recalibrated TMR climate data record is cross-validated with the climate data record produced from the Special Sensor Microwave Imager (SSM/I). It is shown that the intersensor drift is small, providing realistic error bars for the climate trends generated from the instrument pair, as well as validating both the methodology described in this paper and the SSM/I climate data record.
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Wen, Zhongkai, Huan Zhang, Weiping Shu, Liqiang Zhang, Lei Liu, Xiang Lu, Yashi Zhou, Jingjing Ren, Shuang Li, and Qingjun Zhang. "The SSR Brightness Temperature Increment Model Based on a Deep Neural Network." Remote Sensing 15, no. 17 (August 24, 2023): 4149. http://dx.doi.org/10.3390/rs15174149.
Повний текст джерелаАнотація:
The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research.
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Van Der Veen, C. J., and K. C. Jezek. "Seasonal variations in brightness temperature for central Antarctica." Annals of Glaciology 17 (1993): 300–306. http://dx.doi.org/10.1017/s0260305500013008.
Повний текст джерелаАнотація:
The radiative-transfer model developed by Zwally (1977) is modified and coupled to a one-dimensional time-dependent temperature model, to calculate the seasonal variation in brightness temperature. By comparing this with observed records, the radiative properties of firn can be determined. By retaining scattering as a source term in the radiative transfer function, agreement between model-derived scattering and absorption coefficients and those calculated from the Mie/Rayleigh scattering theory can be obtained. The horizontal brightness temperature is not linked to the vertical one through a constant power reflection coefficient.
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Chen, Zhiwei, Rong Jin, Liqiang Zhang, Ke Chen, and Qingxia Li. "Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica." Remote Sensing 15, no. 5 (March 1, 2023): 1396. http://dx.doi.org/10.3390/rs15051396.
Повний текст джерелаАнотація:
The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm.
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Hong, Sungwook, Hwa-Jeong Seo, and Young-Joo Kwon. "A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis." Journal of Atmospheric and Oceanic Technology 33, no. 7 (July 2016): 1363–75. http://dx.doi.org/10.1175/jtech-d-15-0128.1.
Повний текст джерелаАнотація:
AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.
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Timmermans, J., W. Verhoef, C. van der Tol, and Z. Su. "Retrieval of Canopy component temperatures through Bayesian inversion of directional thermal measurements." Hydrology and Earth System Sciences Discussions 6, no. 2 (April 2, 2009): 3007–40. http://dx.doi.org/10.5194/hessd-6-3007-2009.
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Abstract. In remote sensing evapotranspiration is estimated using a single surface temperature. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (ℜspectra≈0.3, ℜgonio≈0.3, and ℜAATSR≈0.5), and no improvement using mono-angular sensors (ℜ≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSEgrass=0.50 K, RMSEwheat=0.29 K, RMSEsugar beet=0.75 K, RMSEbarley=0.67 K), but for low LAI values the measurement setup provides extra disturbances in the directional brightness temperatures, RMSEyoung maize=2.85 K, RMSEmature maize=2.85 K. As these disturbances, were only present for two crops and can be eliminated using masked thermal images the method is considered successful.
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Jackson, Darren L., and Gary A. Wick. "Near-Surface Air Temperature Retrieval Derived from AMSU-A and Sea Surface Temperature Observations." Journal of Atmospheric and Oceanic Technology 27, no. 10 (October 1, 2010): 1769–76. http://dx.doi.org/10.1175/2010jtecha1414.1.
Повний текст джерелаАнотація:
Abstract A 10-m air temperature (Ta) retrieval using Advanced Microwave Sounding Unit A (AMSU-A) and satellite-derived sea surface temperature (Ts) observations is presented. The multivariable linear regression retrieval uses AMSU-A brightness temperatures from the 52.8- and 53.6-GHz channels and satellite-derived daily sea surface temperatures to determine Ta. A regression error of 0.83°C using 841 matched satellite and ship observations demonstrates a high-quality fit of the satellite observations with in situ Ta. Validation of the retrieval using independent International Comprehensive Ocean–Atmosphere Dataset (ICOADS) ship and buoy observations results in a bias of −0.21°C and root-mean-square (RMS) differences of 1.55°C. A comparison with previous satellite-based Ta retrievals indicates less bias and significantly smaller RMS differences for the new retrieval. Regional biases inherent to previous retrievals are reduced in several oceanic regions using the new Ta retrieval. Satellite-derived Ts–Ta data were found to agree well with ICOADS buoy data and were significantly improved from previous retrievals.
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Li, Jiangtao, Yuanhong Guan, Qifeng Lu, Yansong Bao, Chunqiang Wu, and Chaofan Xu. "Retrieval of Desert Microwave Land Surface Emissivity Based on Machine Learning Algorithms." Remote Sensing 16, no. 1 (December 25, 2023): 89. http://dx.doi.org/10.3390/rs16010089.
Повний текст джерелаАнотація:
Based on the community radiative transfer model, ensemble tree-based random forest algorithm, and extreme gradient boosting tree algorithm, this study established a random forest retrieval model (RF) and an extreme gradient boosting tree retrieval model (XGBoost) for the microwave land surface emissivity by using ERA5 reanalysis data and the observed brightness temperature of 10.65 GHz vertical polarization from FY3C Microwave Radiation Imager-I. In addition, an optimized Bayesian regularized neural network retrieval model (M2_30NN) has also been established on the basis of the original neural network land surface emissivity retrieval model (M1_20NN). The results show that compared with the simulated brightness temperature of the original land surface emissivity, the simulated brightness temperature of the land surface emissivity from each retrieval model is not only significantly improved in the correlation coefficient with the observed brightness temperature (5.92% (M1_20NN), 4.23% (M2_30NN), 14.21% (RF), 13.07% (XGBoost)), but also in the evaluation indexes of root mean square error, mean absolute error and explained variance regression score in the training datasets. Furthermore, in terms of testing datasets and spatiotemporal independence test datasets, the retrieval results of RF and XGBoost models can capture the spatial distribution patterns that are consistent with observations well, and also show great numerical improvement compared with the original model. In general, the XGBoost retrieval model is the best, followed by the RF retrieval model.
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Li, Yanyan, Qing Dong, Yongzheng Ren, Fanping Kong, and Zi Yin. "Effect of sea surface temperature on sea surface brightness temperature measured by L-band microware radiometers." IOP Conference Series: Earth and Environmental Science 310 (September 5, 2019): 022045. http://dx.doi.org/10.1088/1755-1315/310/2/022045.
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Novotná, P., and A. Landfeld. "Colour of baked products during baking." Czech Journal of Food Sciences 18, No. 2 (January 1, 2000): 67–70. http://dx.doi.org/10.17221/8312-cjfs.
Повний текст джерелаАнотація:
The parameters of the colour of the surface of bakery products – brightness L*, a* for red and b* for yellow colour – were measured at different temperature regimes. The parameters were found to change during the time of baking. There is a considerable decrease in brightness L* and an increase in parameters a* and b* under the regime of temperature decrease between 6th and 10th minute of baking. The products baked at the 9th and 10th temperature grade were darker in general, brightness L* was lower for the 10th temperature grade and parameter a* was higher from the 8th minute of baking for the 9th temperature grade.
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Favrichon, Samuel, Carlos Jimenez, and Catherine Prigent. "Inter-calibrating SMMR brightness temperatures over continental surfaces." Atmospheric Measurement Techniques 13, no. 10 (October 14, 2020): 5481–90. http://dx.doi.org/10.5194/amt-13-5481-2020.
Повний текст джерелаАнотація:
Abstract. Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instruments, such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the subsequent Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, and the more recent Global Precipitation Measurement Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long-time-series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ∼6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from 3 years of GMI brightness temperatures are first calculated and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data records of microwave brightness temperatures over continental surfaces.
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Boyarskii, D. A., A. N. Romanov, I. V. Khvostov, V. V. Tikhonov, and E. A. Sharkov. "On evaluation of depth of soil freezing based on Smos satellite data." Исследования Земли из Космоса, no. 2 (May 21, 2019): 3–13. http://dx.doi.org/10.31857/s0205-9614201923-13.
Повний текст джерелаАнотація:
The results of a comparative analysis of the brightness temperatures determined from the SMOS satellite and the corresponding depths of soil freezing, measured at weather stations located at the test sites of the Kulunda Plain, are presented. Based on the daily satellite measurement of brightness temperature, the effect of soil freezing on the microwave radiation of the underlying surface was studied. A theoretical calculation of the dependence of soil brightness temperature on the depth of freezing is performed with the model of microwave radiation of a plane-layered inhomogeneous non-isothermal medium. The real parameters of the Kulunda plain soil as well as the climatic characteristics of the sites under study, obtained from the weather stations for the same period, were used as the input parameters of the model. The analysis of satellite, field and model data showed that the evaluation of the depth of soil freezing with satellite microwave radiometry is limited by the need to conduct the contact measurements of physical properties of soil in the areas, for which the SMOS product on the brightness temperature is given.
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Ivanov, A. P. "Correcting the infrared images of soft biological tissues." Proceedings of the National Academy of Sciences of Belarus. Physics and Mathematics Series 55, no. 1 (March 26, 2019): 110–17. http://dx.doi.org/10.29235/1561-2430-2019-55-1-110-117.
Повний текст джерелаАнотація:
Non-invasive (remote) thermographic methods based on IR images are being actively implemented. Using the calculation results of the temperature increment that occurs when a pathological source exists in the person’s skin, a number of ways of solving “inverse problems” are proposed. These include the determination of the depth of the thermal source by measuring the mono or polychrome increment of the normalized brightness of the tissue surface at one point; the source depth and heat transfer parameter by measuring the poly or monochrome one of the normalized brightness (or temperature) at two points; the thermal power of the source by measuring the increment of absolute brightness or temperature at one point; the depth of the thermal source and its size in the near-surface layer by measuring the increment of the normalized brightness at two points. In order to solve these problems, the thermophysical and optical properties of the soft tissues of the biological organism are indicated. Analytical solutions are given for describing the temperature and the glow that arises under its influence from the sources of cylindrical and spherical shape.
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Mengna, JIA, DI Kaichang, YUE Zongyu, and SUN Shujuan. "Spatio-temporal variation characteristics of the Mars surface brightness temperature." National Remote Sensing Bulletin 20, no. 4 (2016): 632–42. http://dx.doi.org/10.11834/jrs.20165243.
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Xu, Gang, Ying Yang, and Zhiguo Meng. "Influence of surface topography on brightness temperature of the regions along the road." E3S Web of Conferences 145 (2020): 02025. http://dx.doi.org/10.1051/e3sconf/202014502025.
Повний текст джерелаАнотація:
Internal temperature of the road is one of the important indicators to evaluate the safety of the road, and the microwave radiometer data is only efficient way to acquire the internal temperatures. This study is to evaluate the influence of the surface topography on the brightness temperature (TB) measured the microwave radiometer data. The results are as follows. (1) The surface slope (θ) and its direction play the important roles on the TB. (2) The influence of θ on TB is weaker compared to that of the surface temperature. (3) At least in low latitude regions, the influence of topography on the TB can be neglected in macro scale. The conclusions are essential to better understand the internal physical parameters of the road with the microwave radiometer data.
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Meng, Xin, Haihua Chen, Jun Liu, Kun Ni, and Lele Li. "Arctic Sea Ice Surface Temperature Inversion Using FY-3D/MWRI Brightness Temperature Data." Remote Sensing 16, no. 3 (January 26, 2024): 490. http://dx.doi.org/10.3390/rs16030490.
Повний текст джерелаАнотація:
The Arctic plays a crucial role in the intricate workings of the global climate system. With the rapid development of information technology, satellite remote sensing technology has emerged as the main method for sea ice surface temperature (IST) observation. To obtain Arctic IST, we used the FengYun-3D Microwave Radiation Imager (FY-3D/MWRI) brightness temperature (Tb) data for IST inversion using multiple linear regressions. Measured data on IST parameters in the Arctic are difficult to obtain. We used the Moderate-Resolution Imaging Spectroradiometer (MODIS) MYD29 IST data as the baseline to obtain the coefficients for the MWRI IST inversion function. The relation between MWRI Tb data and MODIS MYD29 IST product was established and the microwave IST inversion equation was obtained for the months of January to December 2019. Based on the R2 results and the IST inversion results, we compared and analyzed the MWRI IST data from the months of January to April, November, and December with the Operation IceBridge KT19 IR Surface Temperature data and the Northern High Latitude Level 3 Sea and Sea Ice Surface Temperature (NHL L3 SST/IST). We found that compared MWRI IST with NHL L3 IST, the correlation coefficients (Corr) > 0.72, mean bias ranged from −1.82°C to −0.67 °C, and the standard deviation (Std) ranged from 3.61 °C to 4.54 °C; comparing MWRI IST with KT19 IST, the Corr was 0.69, the bias was 0.51 °C, and the Std was 4.34 °C. The obtained error conforms to the precision requirement. From these results, we conclude that the FY-3D/MWRI Tb data are suitable for IST retrieval in the Arctic using multiple linear regressions.
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Shuman, Christopher A., Richard B. Alley, and Sridhar Anandakrishnan. "Characterization of a hoar-development episode using SSM/I brightness temperatures in the vicinity of the GISP2 site, Greenland." Annals of Glaciology 17 (1993): 183–88. http://dx.doi.org/10.3189/s0260305500012817.
Повний текст джерелаАнотація:
Formation of a surface-hoar/depth-hoar complex at the GISP2 site in central Greenland was correlated with large changes in Special Sensor Microwave/Imager (SSM/I) brightness-temperature data. Pass-averaged SSM/I brightness-temperature data over a 1/2° latitude by 1° longitude cell for the 19 and 37 GHz, vertically (V) and horizontally (Η) polarized bands were manipulated to yield differential (V-Η) trends which clearly show a gradual decline as the hoar formation caused a progressively rougher surface with progressively lower density. The hoar episode ended as snowfall, and high winds buried and destroyed the surface-hoar layer and caused rapid V-Η increases in ≈ 1 day. Comparison of the different trends with changes in the field-monitored variables and theoretical values suggest that the V-Η trends are sensitive primarily to changes in surface roughness, and secondarily to near-surface density changes. Consistent expression of trends in microwave brightness temperature over 35 adjacent study cells indicates that this technique may provide a remote-sensing signature capable of defining the timing and spatial extent of surface- and depth-hoar formation in central Greenland.
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Shuman, Christopher A., Richard B. Alley, and Sridhar Anandakrishnan. "Characterization of a hoar-development episode using SSM/I brightness temperatures in the vicinity of the GISP2 site, Greenland." Annals of Glaciology 17 (1993): 183–88. http://dx.doi.org/10.1017/s0260305500012817.
Повний текст джерелаАнотація:
Formation of a surface-hoar/depth-hoar complex at the GISP2 site in central Greenland was correlated with large changes in Special Sensor Microwave/Imager (SSM/I) brightness-temperature data. Pass-averaged SSM/I brightness-temperature data over a 1/2° latitude by 1° longitude cell for the 19 and 37 GHz, vertically (V) and horizontally (Η) polarized bands were manipulated to yield differential (V-Η) trends which clearly show a gradual decline as the hoar formation caused a progressively rougher surface with progressively lower density. The hoar episode ended as snowfall, and high winds buried and destroyed the surface-hoar layer and caused rapid V-Η increases in ≈ 1 day. Comparison of the different trends with changes in the field-monitored variables and theoretical values suggest that the V-Η trends are sensitive primarily to changes in surface roughness, and secondarily to near-surface density changes. Consistent expression of trends in microwave brightness temperature over 35 adjacent study cells indicates that this technique may provide a remote-sensing signature capable of defining the timing and spatial extent of surface- and depth-hoar formation in central Greenland.
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Wethey, David S., Nicolas Weidberg, Sarah A. Woodin, and Jorge Vazquez-Cuervo. "Characterization and Validation of ECOSTRESS Sea Surface Temperature Measurements at 70 m Spatial Scale." Remote Sensing 16, no. 11 (May 24, 2024): 1876. http://dx.doi.org/10.3390/rs16111876.
Повний текст джерелаАнотація:
The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities.
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Rambe, Pahala Roy, Mubarak Mubarak, and Rifardi Rifardi. "A Map of Sea Surface Temperature in Rupat Strait Based on Satellite Image of Aqua-Modis." Journal of Coastal and Ocean Sciences 3, no. 1 (January 10, 2022): 54–59. http://dx.doi.org/10.31258/jocos.3.1.54-59.
Повний текст джерелаАнотація:
The study aims to identify variations in the surface temperature of the water of dumai, Riau province, using the AQUA-Modis satellite. Data analysis uses seadas and surfer software. In this study, the method used was the survey method. Ground check done was to establish the immediate value of ocean surface temperature. It also measures the quality of the water and gives documented observations of the waters. Parameters measured in this study are those of the quality of the water covering: brightness, temperature, acidity (pH), and salinity. The measured parameters for the quality of water in the dumai waters were carried out at 3 stations that are thought to represent the parameters of the waters of the city of dumai. The result of the measured parameters for the quality of the dumai waters is known that for the value of the brightness of the dumai waters is between 7.5 and 16 cm, the water temperature is between 28-29 oC, the degree of acidity (pH) of water ranges between 6-7 and dumai water salinity (26-32‰). Based on studies that 2020 variations in surface temperature (SST) have a varying range of temperatures. Variations in surface temperature are marked by SST anomalies indicating that the value of negative anomalies indicates that an SST shift is marked by a drop in temperature
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Davis, Curt H., and H. Jay Zwally. "Geographic and seasonal variations in the surface properties of the ice sheets by satellite-radar altimetry." Journal of Glaciology 39, no. 133 (1993): 687–97. http://dx.doi.org/10.1017/s0022143000016580.
Повний текст джерелаАнотація:
AbstractGeosat-altimeter wave forms from the Greenland and Antarctic ice sheets are analyzed using an algorithm based upon a combined surface-and volume-scattering model. The results demonstrate that sub-surface volume-scattering occurs over major parts of the ice sheets. Quantitative estimates of geographic variations in the near-surface ice-sheet properties are derived by retrackingindividualaltimeter wave forms. The derived surface properties correlate with elevation, latitude and microwave brightness-temperature data. Specifically, the extinction coefficient of snow obtained by this method varies from 0.48 to 0.13 m−1over the latitudes from 65° to 72°N on the central part of the Greenland ice sheet and from 0.20 to 0.10 m−1over a section of Wilkes Land in East Antarctica where the elevation increases from 2550 to 3150 m.Analysis of passive-microwave data over East Antarctica shows that the brightness temperature increases with elevation as the extinction coefficient decreases. Larger snow grain-sizes occur at lower elevations of the ice sheet because of higher mean annual temperatures. The larger grain-sizes increase the extinction coefficient of snow and decrease the emitted energy (brightness temperature) from greater snow depths. The passive-microwave data are also used to determine the average number of melt d year−1(1979–87) for the central part of the Greenland ice sheet. For latitudes from 65° to 68.5° N, the average number of melt days decreases from 3.5 to 0.25 d year, whereas no melt events are observed for latitudes above 69°N over the 8 year period. Snow subjected to alternate melting and freezing has enhanced grain-sizes compared to that of dry snow. This accounts for the larger values and larger spatial variations ofkeon the Greenland ice sheet compared to East Antarctica, where surface temperatures are never high enough to cause surface melting.
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Davis, Curt H., and H. Jay Zwally. "Geographic and seasonal variations in the surface properties of the ice sheets by satellite-radar altimetry." Journal of Glaciology 39, no. 133 (1993): 687–97. http://dx.doi.org/10.3189/s0022143000016580.
Повний текст джерелаАнотація:
AbstractGeosat-altimeter wave forms from the Greenland and Antarctic ice sheets are analyzed using an algorithm based upon a combined surface-and volume-scattering model. The results demonstrate that sub-surface volume-scattering occurs over major parts of the ice sheets. Quantitative estimates of geographic variations in the near-surface ice-sheet properties are derived by retracking individual altimeter wave forms. The derived surface properties correlate with elevation, latitude and microwave brightness-temperature data. Specifically, the extinction coefficient of snow obtained by this method varies from 0.48 to 0.13 m−1 over the latitudes from 65° to 72°N on the central part of the Greenland ice sheet and from 0.20 to 0.10 m−1 over a section of Wilkes Land in East Antarctica where the elevation increases from 2550 to 3150 m.Analysis of passive-microwave data over East Antarctica shows that the brightness temperature increases with elevation as the extinction coefficient decreases. Larger snow grain-sizes occur at lower elevations of the ice sheet because of higher mean annual temperatures. The larger grain-sizes increase the extinction coefficient of snow and decrease the emitted energy (brightness temperature) from greater snow depths. The passive-microwave data are also used to determine the average number of melt d year−1 (1979–87) for the central part of the Greenland ice sheet. For latitudes from 65° to 68.5° N, the average number of melt days decreases from 3.5 to 0.25 d year, whereas no melt events are observed for latitudes above 69°N over the 8 year period. Snow subjected to alternate melting and freezing has enhanced grain-sizes compared to that of dry snow. This accounts for the larger values and larger spatial variations of ke on the Greenland ice sheet compared to East Antarctica, where surface temperatures are never high enough to cause surface melting.
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KITAMURA, Ayako, Takeki IZUMI, and Hiroshi MATSUYAMA. "Relationship among Surface Temperature Estimated by Surface Energy Budget, Ground Air Temperature and Brightness Temperature of Landsat-5 TM." Journal of Geography (Chigaku Zasshi) 113, no. 4 (2004): 495–511. http://dx.doi.org/10.5026/jgeography.113.4_495.
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