Passive Microwave Radiation Research Articles

Reconstructing All-Weather Land Surface Temperature Using the Bayesian Maximum Entropy Method Over the Tibetan Plateau and Heihe River Basin

Land surface temperature (LST) is an important parameter for measuring the water–heat balance on the Earth's surface. Remote sensing technology provides a unique way to monitor LSTs over large spatial areas. Thermal infrared (TIR)-derived LST has a high spatial resolution and accuracy, but missing values caused by clouds hinder applications of this method. Passive microwave radiation can penetrate clouds, but the data have a relatively lower spatial resolution and accuracy. The Bayesian maximum entropy (BME) method was used for blending the Moderate Resolution Imaging Spectroradiometer (MODIS) LST and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) LST to produce a spatially complete, accurate, and high-spatial-resolution LST over the Tibetan Plateau (TP) and the Heihe River Basin (HRB). The accuracy of the BME method was validated using the adjusted soil temperature collected from the two verification regions. The root-mean-square errors (RMSEs) are less than 3.54 and 4.89 K over the relatively flat verification region during nighttime and daytime, respectively. In another rugged terrain verification region, the RMSE is less than 3 K during nighttime and ranges from 4.2 to 8.29 K during daytime. The RMSE of the blended LST is significantly better than that of a recent study of the AMSR-E LST retrieved during nighttime, and the RMSE is comparable to the AMSR-E LST retrieved during daytime. The BME method was adopted to integrate the MODIS LST and AMSR-E LST acquired on November 30, 2010 over the TP and HRB. The spatial completeness of the blended LST reached 100%, and the blended LST spatial pattern was generally consistent with the spatial patterns of the MODIS LST and AMSR-E LST. This paper demonstrates the utility of generating all-weather regional LSTs using the BME method from TIR LST and microwave LST products.

Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

A remote sensing method for retrieving land surface emissivity and temperature in cloudy areas: a case study over South China

ABSTRACTLand surface temperature (LST) is an important parameter at the surface–atmosphere interface, and measurements of the LST at continuous temporal and spatial scales are necessary in many research fields. Passive microwave radiation can penetrate clouds and detect land surface information under clouds; consequently, passive microwave remote sensing has the potential to obtain LST under almost all weather conditions. In this study, the relationships between the brightness temperature polarization ratio (PR) and horizontally polarized emissivities, and between the horizontally and vertically polarized emissivities are combined to develop a three-stage LST retrieval algorithm from 19 GHz microwave brightness temperature observations in cloudy areas. During the first two stages, the horizontally and vertically polarized emissivities are solely obtained from the brightness temperature data. In the last stage, the LST is calculated with the known emissivity data by ignoring the atmospheric effect. In the validation of South China, the root-mean-square errors (RMSEs) of the estimated horizontally and vertically polarized emissivities at 19 GHz are 0.0078 and 0.0023, respectively. The retrieved LST is compared to single-point measurements of the meteorological stations, and the RMSE is 4.1200 K. The LST errors are mainly due to the propagation of estimated land surface emissivity (LSE) errors, and the poor representativeness of the validated data in mixed pixels. The superiority of this LST retrieval algorithm lies in simultaneously obtaining the LST and both polarized LSE under almost all weather conditions with little input data.

Assessment and adjustment of sea surface salinity products from Aquarius in the southeast Indian Ocean based on in situ measurement and MyOcean modeled data

The in situ sea surface salinity (SSS) measurements from a scientific cruise to the western zone of the southeast Indian Ocean covering 30°–60°S, 80°–120°E are used to assess the SSS retrieved from Aquarius (Aquarius SSS). Wind speed and sea surface temperature (SST) affect the SSS estimates based on passive microwave radiation within the mid- to low-latitude southeast Indian Ocean. The relationships among the in situ, Aquarius SSS and wind-SST corrections are used to adjust the Aquarius SSS. The adjusted Aquarius SSS are compared with the SSS data from MyOcean model. Results show that: (1) Before adjustment: compared with MyOcean SSS, the Aquarius SSS in most of the sea areas is higher; but lower in the low-temperature sea areas located at the south of 55°S and west of 98°E. The Aquarius SSS is generally higher by 0.42 on average for the southeast Indian Ocean. (2) After adjustment: the adjustment greatly counteracts the impact of high wind speeds and improves the overall accuracy of the retrieved salinity (the mean absolute error of the Zonal mean is improved by 0.06, and the mean error is - 0.05 compared with MyOcean SSS). Near the latitude 42°S, the adjusted SSS is well consistent with the MyOcean and the difference is approximately 0.004.

Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution–emission model

To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. However, the main uncertainties using existing SWE algorithms arise from snow metamorphism which evolves during the winter season, and changes the snow emissivity. To consider the evolution in snow emissivity a coupled snow evolution–emission model can be used to simulate the brightness temperature (T B) of the snowpack. During a dedicated campaign in the winter season, from November to April, of 2007–2008 two surface-based radiometers operating at 19 GHz and 37 GHz continuously measured the passive microwave radiation emitted through a seasonal snowpack in southern Quebec (Canada). This paper aims at modeling and interpreting this time series of T B over the whole season, with an hourly step, using a coupled multi-layer snow evolution–emission model. The thermodynamic snow evolution model, referred as to Crocus, was driven by local meteorological measurements. Results from this model provided, in turn, the input variables to run the Microwave Emission Model of Layered Snowpacks (MEMLS) in order to predict T B at 19 GHz and 37 GHz for both vertical (V) and horizontal (H) polarizations. The accuracy of T B predicted by the Crocus–MEMLS coupled model was evaluated using continuous measurements from the surface-based radiometers. The weather conditions observed during the winter season were diverse, including several warm periods with melting snow and rain-on-snow events, producing very complex variations in the time series of T B. To aid our analysis, we identified days with melting snow versus days with dry snow. The Crocus–MEMLS coupled model was able to accurately predict melt events with a success rate of 86%. The residual error was due to an overestimation of the duration of several melt events simulated by Crocus. This problem was explained by 1) a limitation of percolation, and 2) a very long-acting melt of lower layers due to geothermal flux. When the snowpack was completely dry, the global trend of T B during the season was characterized by a decrease of T B due to growth in the snow grain size. During most of the season, Crocus–MEMLS correctly predicted the evolution of T B resulting from temperature gradient metamorphism; the root mean square errors ranged between 2.8 K for the 19 GHz vertical polarization (19V) and 6.9 K for the 37 GHz horizontal polarization (37H). However, during dry periods near the end of the season, the values of T B were strongly overestimated. This overestimation was mainly due to a limitation of the growth of large snow grains in the wet snowpack simulated by Crocus. This effect was confirmed by estimating snow grain sizes from the observed T B and the coupled model. The estimated snow grain sizes were larger and more realistic than those initially predicted by the Crocus model.

Simulation and measurement of relief effects on passive microwave radiation

Simulation and measurement of relief effects on passive microwave radiation

Use of low-temperature scanning electron microscopy to compare and characterize three classes of snow cover

This study, which uses low-temperature scanning electron microscopy (LTSEM), systematically sampled and characterized snow crystals that were collected from three unique classes of snow cover: prairie, taiga, and alpine. These classes, which were defined in previous field studies, result from exposure to unique climatic variables relating to wind, precipitation, and air temperature. Snow samples were taken at 10 cm depth intervals from the walls of freshly excavated snow pits. The depth of the snow pits for the prairie, taiga, and alpine covers were 28, 81, and 110 cm, respectively. Visual examination revealed that the prairie snow cover consisted of two distinct layers whereas the taiga and alpine covers had four distinct layers. Visual measurements were able to establish the range of crystal sizes that occurred in each layer, the temperature within the pit, and the snow density. The LTSEM observations revealed the detailed structures of the types of crystals that occurred in the snow covers, and documented the metamorphosis that transpired in the descending layers. Briefly, the top layers from two of the snow covers consisted of freshly fallen snow crystals that could be readily distinguished as plates and columns (prairie) or graupel (taiga). Alternatively, the top layer in the alpine cover consisted of older dendritic crystal fragments that had undergone early metamorphosis, that is, they had lost their sharp edges and had begun to show signs of joining or bonding with neighboring crystals. A unique layer, known as sun crust, was found in the prairie snow cover; however, successive samplings from all three snow covers showed similar stages of metamorphism that led to the formation of depth hoar crystals. These changes included the gradual development of large, three-dimensional crystals having clearly defined flat faces, sharp edges, internal depressions, and facets. The study, which indicates that LTSEM can be used to enhance visual data by systematically characterizing snow crystals that are collected at remote locations, is important for understanding the physics of snowpacks and the metamorphosis that leads to potential avalanche situations. In addition, the metamorphosis of snow crystals must be considered when microwave radiometry is used to estimate the snow water equivalent in the winter snowpack, because large snow crystals more effectively scatter passive microwave radiation than small crystals.

Snow crystal orientation effects on the scattering of passive microwave radiation

For this study, consideration is given to the role crystal orientation plays in scattering and absorbing microwave radiation. A discrete dipole scattering model is used to measure the passive microwave radiation at two polarizations (horizontal and vertical), scattered by snow crystals oriented in random and nonrandom positions and having various sizes (ranging from 1 /spl mu/m to 10000 /spl mu/m in radius) and shapes (including spheres, cylinders, hexagons). The model results demonstrate that for the crystal sizes typically found in a snowpack, crystal orientation is insignificant compared to crystal size in terms of scattering microwave energy in the 8100 /spl mu/m (37 GHz) region of the spectrum. Therefore, the assumption used in radiative transfer approaches, where snow crystals are modeled as randomly oriented spheres, is adequate to account for the transfer of microwave en energy emanating from the ground and passing through a snowpack.

Up‐scaling effects in passive microwave remote sensing: ESTAR 1.4 GHz measurements during SGP '97

Passive microwave radiation measurements over sparsely vegetated surfaces during the Southern Great Plain Experiment are used to determine spatial up‐scaling effects. For various distributions of geophysical parameters, the differences between mean soil moisture derived from high resolution (800 by 800m²) 1.4 GHz brightness temperatures and soil moisture calculated from mean brightness temperatures at larger scales are compared. For spatial resolutions of 31.4²km², the effects of these non‐linearities in radiative transfer at 1.4 GHz do not yield differences in estimated volumetric surface soil moisture (0–5 cm) exceeding 0.02 [cm³cm−3].

Interpretation of an atlas of passive microwave‐derived rainfall over the eastern North Atlantic Ocean and North Sea

Conventional measurements of rainfall over the ocean are fraught with physical and practical problems and instrument difficulties, leading to spatial and temporal inconsistencies in climate datasets. Estimates of rainfall based on the scattering and emission characteristics of passive microwave radiation, measured by instruments on board orbiting satellites, substantially improve the knowledge of rainfall patterns and processes over open water. A new passive microwave satellite-based Atlas of Rainfall and Wind Speed over the Eastern North Atlantic and North Sea has been prepared, including rainfall totals (mm), rates (mm h−1), and percent occurrences (%) for the period 1979–1996 inclusive. Annual, seasonal and monthly maps have been generated, as well as histogram and time series products. In this paper, the main rainfall patterns over this region are summarised and interpreted in comparison with several other existing datasets. The interpretation of passive microwave-derived wind speed over the eastern North Atlantic and North Sea shall be reported in a future paper. Upon the analysis of above and below normal rainfall periods, significant relationships are revealed with respect to the mean sea level pressure, air and sea surface temperature, and atmospheric precipitable water. It is shown that the location and strength of the belt of prevailing Westerlies, and their interaction with major land masses, dictate the overall rainfall distribution. Results show that rainfall over this area is well-related to various indices of the general circulation. Also, patterns of rainfall over the North Sea are significantly related to the direction and strength of the wind. Finally, it is shown that there is a significant linear relationship between climate station rainfall and the estimate over the nearest open water pixel, although the percent of explained variance seldom exceeds 50%. Copyright © 1999 Royal Meteorological Society

Interpretation of an atlas of passive microwave-derived rainfall over the eastern North Atlantic Ocean and North Sea

Conventional measurements of rainfall over the ocean are fraught with physical and practical problems and instrument difficulties, leading to spatial and temporal inconsistencies in climate datasets. Estimates of rainfall based on the scattering and emission characteristics of passive microwave radiation, measured by instruments on board orbiting satellites, substantially improve the knowledge of rainfall patterns and processes over open water. A new passive microwave satellite-based Atlas of Rainfall and Wind Speed over the Eastern North Atlantic and North Sea has been prepared, including rainfall totals (mm), rates (mm h−1), and percent occurrences (%) for the period 1979–1996 inclusive. Annual, seasonal and monthly maps have been generated, as well as histogram and time series products. In this paper, the main rainfall patterns over this region are summarised and interpreted in comparison with several other existing datasets. The interpretation of passive microwave-derived wind speed over the eastern North Atlantic and North Sea shall be reported in a future paper. Upon the analysis of above and below normal rainfall periods, significant relationships are revealed with respect to the mean sea level pressure, air and sea surface temperature, and atmospheric precipitable water. It is shown that the location and strength of the belt of prevailing Westerlies, and their interaction with major land masses, dictate the overall rainfall distribution. Results show that rainfall over this area is well-related to various indices of the general circulation. Also, patterns of rainfall over the North Sea are significantly related to the direction and strength of the wind. Finally, it is shown that there is a significant linear relationship between climate station rainfall and the estimate over the nearest open water pixel, although the percent of explained variance seldom exceeds 50%. Copyright © 1999 Royal Meteorological Society

Effects of snow crystal shape on the scattering of passive microwave radiation

A discrete dipole scattering model is used to measure the passive microwave radiation scattered by snow particles having different shapes and sizes. The model results demonstrate that the shape of the snow crystal is insignificant in scattering microwave energy in the 37-GHz region of the spectrum.

Carbon dioxide crystals: An examination of their size, shape, and scattering properties at 37 GHz and comparisons with water ice (snow) measurements

On Earth, the temperature regime is such that water is generally fairly close to its freezing point, and thus relatively small differences in climate affect how much snow and ice are present and whether or not the snow covering will be seasonal or last from one year to the next. On Mars, as on Earth, the presence of ice also plays a role in large‐scale climate processes and it is important in controlling the abundance of atmospheric carbon dioxide (CO2) and water vapor. Passive microwave radiometry has been used to derive snow extent and snow depth on Earth, where scattering by snow (H2O) crystals is the dominant effect on the microwave radiation emanating from the ground and emerging from the snowpack. Microwave remote sensing may also prove to be useful for assessing the coverage and thickness of the frozen H2O and CO2 on Mars, but more exact information is needed on how both H2O crystals and frozen CO2 crystals scatter and absorb passive microwave radiation. In this study, CO2 crystals have been produced in a laboratory cold chamber with temperature conditions similar to those found on the polar caps of Mars, and detailed three‐dimensional images of their size and shape have been made with a low‐temperature scanning electron microscope. Unlike the much larger H2O snow crystals found on Earth, which typically range in size between 0.1 mm and 1.0 mm (radius), CO2 crystals are differently shaped and considerably smaller. Bipyramid crystals (base to base four‐sided pyramids) are commonly observed, some as small as 1.0 μm. A discrete dipole model was employed to calculate the passive microwave radiation scattered and absorbed by crystals of various sizes and shapes. Modeling results indicate that the shape of the crystal, whether for frozen CO2 or H2O, is of little consequence in affecting extinction efficiency. However, owing to their smaller size, frozen CO2 crystals are more emissive than the H2O crystals in the 37 GHz region of the microwave spectrum. For the larger sizes of the modeled crystals, scattering dominates over absorption since the particles approach the size of the wavelength. The scattering values are 2 orders of magnitude larger than absorption for the 900 μm size snow particles. For CO2 crystals of 3.0 μm in size, absorption is 7 orders of magnitude greater than scattering.

Estimation of Snow Water Equivalent Using Passive Microwave Radiation Data

A knowledge of the potential volume of water present in snowpacks is vital for estimates of the global water balance. It is estimated that about three quarters of the world’s terrestrial water reserves are locked in snow and ice. The principal objective of this study is to analyze the degree to which passive microwave radiation can be used to interpret snow water equivalent (SWE). The methodology reflects the importance of isolating phenomena other than SWE that may influence the microwave signal. These factors include: snow wetness, depth hoar, complex mountainous terrain, and dense forest cover. Applying these four variables, 16 land-cover categories were identified, and relationships between SWE and brightness temperature were derived for each category. Results show that, for the “simplest” of these categories, a nonforested, nonmountainous terrain, with no depth hoar or melting snow, the SWE can be estimated with 95% confidence within ±44 mm. The confidence limits increase as the complexity of the snowpack, land form, and land cover increases.

Satellite estimation of precipitation over land

The availability of satellite-derived rainfall products to hydrologists and other natural scientists has increased enormously in the last five years. The purpose of this paper is to review concisely the current state-of-the-art of satellite precipitation estimation over land and the availability of standard products, and to highlight some of the strengths and limitations of satellite-derived rainfall data for hydrological applications. Methods based on infrared, visible and passive microwave radiation measurements are discussed. Results of several international activities aimed at evaluating the performance of the estimation algorithms are briefly summarized.

Remote sensing of volcanic ash clouds using special sensor microwave imager data

Measurements from the satellite‐based special sensor microwave imager (SSM/I) were used to collect passive microwave radiation (19–85 GHz) for the August 19, 1992 (UT date), Crater Peak/Spurr volcanic cloud. This eruption was also imaged by a ground‐based C‐band radar system at Kenai, Alaska, 80 km away, and by the thermal infrared channels of the polar‐orbiting advanced very high resolution radiometer (AVHRR). The SSM/I sensor detects scattering of Earth‐emitted radiation by millimeter size volcanic ash particles. The size of ash particles in a volcanic ash cloud can be estimated by comparing the scattering at different microwave frequencies. The mass of particles in the volcanic ash cloud can be estimated by using a theoretical method based on Mie theory or by adapting the empirical methods used for estimating rainfall rates and accounting for the different dielectric constants of volcanic ash and raindrops. For the August 19, 1992, Crater Peak/Spurr eruption, the SSM/I‐based estimate of ash fallout mass (1.3 × 109 − 3 × 1010 kg) was 4%–85% of the mass fallout measured in the field. Like weather radar systems, the SSM/I offers the ability to sense young volcanic ash clouds during and immediately following (within 30 min) actual eruptions. Because most volcanoes are out of range of weather radar systems, the SSM/I may be an important tool for determining the magnitude, initial trajectory, and potential fallout mass of eruptions. The SSM/I may therefore play a role in mitigating volcanic cloud hazards for aircraft, determining masses where ground sampling is not possible, and in issuing fallout warnings for communities downwind of volcanic eruptions.

Radiative Transfer to Space through a Precipitating Cloud at Multiple Microwave Frequencies. Part I: Model Description

Abstract In a two-part study we investigate the impact of time-dependent cloud microphysical structure on the transfer to space of passive microwave radiation at several frequencies across the EHF and lower SHF portions of the microwave spectrum in order to explore the feasibility of using multichannel passive-microwave retrieval techniques for the estimation of precipitation from space-based platforms. A series of numerical sensitivity experiments have been conducted that were designed to quantify the impact of an evolving cumulus cloud in conjunction with a superimposed rain layer on the transfer to space of microwave radiation emitted and scattered from the cloud layers, rain layer and the underlying surface. The specification of cloud microphysics has been based on the results of a time-dependent two-dimensional numerical cumulus model developed by Hall (1980). An assortment of vertically homogeneous rain layers, described by the Marshall-Palmer rain drop distribution, has been inserted in the model a...