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Land surface temperature (LST) is a key parameter and plays an important role in hydrology, ecology, environment, and biogeochemistry. It is difficult for the existing satellites to acquire global LST with spatial and temporal consistency. The Moon-based Earth observation platform with a long life, large coverage can observe continuously the Earth, and obtain the global-scale LST. At present, various approaches for retrieving LST from passive microwave remote sensing data have been developed for the satellite remote sensing data with small and constant viewing zenith angle, however, the Moon-based Earth observation platform located outside the Earth's ionosphere has the viewing zenith angle of 0-90°. In this study, a modified physical-based method of LST retrieval from passive microwave data was developed for wide viewing zenith angles, and the LST and emissivity can be simultaneously estimated through the analysis of various atmospheric and ionosphere parameters. Three types of data, including the FengYun-3B satellite microwave radiation imager data, the multichannel Advanced Microwave Scanning Radiometer data, and Moon-based microwave radiation simulation data with the viewing zenith angles of 52-53°, 55°, and 0-90°, respectively, were used to retrieve LST. Results show that the estimation accuracy of LST decreases with the increase of viewing zenith angle. The 23.8 and 36.5 GHz brightness temperature is optimum for the LST estimation under a large-scale viewing zenith angle, and the root mean square errors of the LST are 5.18, 5.44, and 4.79 K, respectively.

A two-dimensional variational retrieval (2D-Var) is presented for a passive microwave imager. The overlapping antenna patterns of all frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are explicitly simulated to attempt retrieval of near-surface wind speed and surface skin temperature at finer spatial scales than individual antenna beams. This is achieved, with the effective spatial resolution of retrieved parameters judged by analysis of 2D-Var averaging kernels. Sea surface temperature retrievals achieve about 30 km resolution, with wind speed retrievals at about 10 km resolution. It is argued that multi-dimensional optimal estimation permits greater use of total information content from microwave sensors than other methods, with no compromises on target resolution needed; instead, various targets are retrieved at the highest possible spatial resolution, driven by the channels' sensitivities. All AMSR2 channels can be simulated within near their published noise characteristics for observed clear-sky scenes, though calibration and emissivity model errors are key challenges. This experimental retrieval shows the feasibility of 2D-Var for cloud-free retrievals and opens the possibility of stand-alone 3D-Var retrievals of water vapour and hydrometeor fields from microwave imagers in the future. The results have implications for future satellite missions and sensor design, as spatial oversampling can somewhat mitigate the need for larger antennas in the push for higher spatial resolution.

The purpose of this chapter is to offer an accurate treatment of relevant aspects of satellite remote sensing of precipitation using passive microwave (PMW) radiometers. Microwave observations of the Earth’s system differ substantially from those based on optical and infrared wavelengths. Visible (VIS) and infrared (IR) instruments essentially sense the cloud top properties by measuring reflected or emitted radiation. Microwave frequencies, on the contrary, possess greater penetrating capabilities than optical radiation and can thus be exploited to investigate cloud internal properties by retrieving the interaction of hydrometeors with the radiation field. This fact is particularly true in the remote sensing of precipitation, where the impact of the volume of raindrops on the radiative field is directly linked to the total extinction of incident radiation. The following paragraphs will be focused on one hand on theoretical considerations at the foundations of thermal radiation processes and on the other the attention will be centered on a treatment of practical aspects of retrieving precipitation in the microwave bands. Particular attention will be dedicated in the first part of the chapter to the radiative transfer theory in the microwaves by using the Rayleigh-Jeans formulation and a description of the absorption and scattering processes associated with the Mie theory with the approximation of extinction from poly-disperse media as proxies for natural media. This section will create a valid substrate to understand and theoretically evaluate the impact of atmospheric constituents such as precipitation types or hydrometeor phases and sizes on the natural radiation emitted from the Earth. Furthermore, the theoretical considerations of this section will be compared in the second part of the chapter with real measurements. Making use of a wide suite of microwave frequencies of a new generation of PMW sensors flying on board polar orbiting satellites the attenuation of the microwave signal due to rain clouds will be discussed possibly discerning the contribution to the total radiation of the emissivity from various surfaces falling into the satellite field of view. Finally, a new microwave highfrequency method to retrieve and classify precipitation types will be presented.

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LAI) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the active-only model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for $\sigma _{\mathrm{ HH}}^{o} $ , $\sigma _{\mathrm{ VV}}^{o} $ , $T_{\mathrm {BH}}$ , and $T_{\mathrm {BV}}$ , respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data.

The absolute radiometric accuracy of the Fengyun 3D advanced Medium Resolution Spectral Imager (FY3D MERSI-II) thermal infrared bands was evaluated using the collected field measurements and atmospheric transfer simulations during 16–22 August 2019 at Lake Qinghai. A thermal infrared radiometer equipped on an unmanned surface vehicle was used to continuously collect the water temperature. Atmospheric conditions, surface emissivity, and aerosol optical depth measured near the field experiment site were adopted by the atmospheric transfer code to calculate the parameters about the influence of atmosphere on long-wave radiation, including the path radiance and the transmittance propagated from land surface to the satellite. The radiometric calibration accuracy analysis suggests that the differences between the simulated brightness temperature and satellite-based brightness temperature are −0.346 K and −0.722 K for channel 24 on 18 and 20 August, respectively, while it reaches −0.460 K and −1.036 K for channel 25 on 18 and 20 August, respectively. The vicarious calibration coefficients were found to be in good agreement with the internal onboard calibration coefficient in channel 24 and 25 of the FY3D MERSI-II according to the validation analysis in selected regions. The thermal infrared bands of the FY3D have a good in-orbit operational status according to our vicarious calibration experiments.

Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

International audience; During its 13.5 years of operation around the Saturn system, the microwave radiometer incorporated in the Cassini RADAR observed Rhea at 2.2 cm during 9 flybys, with resolutions up to a tenth of Rhea's radius. We compare the antenna temperatures measured by this instrument to simulated data generated with the combination of thermal, radiative transfer, and emissivity models, in order to derive new constraints on the thermal, structural, and compositional properties of the near subsurface of different regions of Rhea. We find that the Cassini radiometer probes depths of 5 to 15 m on Rhea (that is 200–700 wavelengths), implying a weakly absorbing regolith and therefore little contamination by non-ice compounds. In the South polar region, local summer and fall observations constrains the maximum loss tangent to be 8.1 × 10−4, implying a contaminant volumetric fraction of 10% in the first ~10 m. The derived thermal inertias (>60 MKS) in the South pole are higher than the ones measured in the thermal infrared (1–46 MKS), consistent with increasing compaction with depth. Over all of Rhea, current models relating surface microwave emissivity and backscatter cannot explain both the emissivities and the high radar backscatter recorded by the Cassini Radar, suggesting the presence of especially efficient backscattering structures in the subsurface of Rhea. This interpretation is consistent with the very low derived dielectric constants (1.1–1.5), indicating that the subsurface structures are depolarizing. In particular, the ejecta blanket of the Inktomi crater has an emissivity about 20% lower than its surroundings and is very radar-bright: the impact that formed this young crater must have excavated fresh water ice from the subsurface, while also creating structures (such as cracks) reflecting centimetric wavelengths.

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data.

Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.

One of the key problems in determining lunar surface composition for thermal-infrared measurements is the lack of comparable laboratory-measured spectra. As the surface is typically composed of fine-grained particulates, the lunar environment induces a thermal gradient within the near sub-surface, altering the emission spectra: this environment must therefore be simulated in the laboratory, considerably increasing the complexity of the measurement. Previous measurements have created this thermal gradient by either heating the cup in which the sample sits or by illuminating the sample using a solar-like source. This is the first setup able to measure in both configurations, allowing direct comparisons to be made between the two. Also, measurements across a wider spectral range and at a much higher spectral resolution can be acquired using this new setup. These are required to support new measurements made by the Diviner Lunar Radiometer, the first multi-spectral thermal-infrared instrument to orbit the Moon. Results from the two different heating methods are presented, with measurements of a fine-grained quartz sample compared to previous similar measurements, plus measurements of a common lunar highland material, anorthite. The results show that quartz gives the same results for both methods of heating, as predicted by previous studies, though the anorthite spectra are different. The new calibration pipeline required to convert the raw data into emissivity spectra is described also

© 2017 American Meteorological Society. MODIS thermal sensors can provide us with global land surface temperature (LST) several times each day, but have difficulty in obtaining information from the land surface in cloudy situations. As a result, the monthly day or night LST products [Terra monthly day LST (TMD), Terra monthly night LST (TMN), Aqua monthly day LST (AMD), and Aqua monthly night LST (AMN)] are the average LST values calculated over a variable number of clear-sky days in a month. Is it possible to derive an accurate estimate of monthly mean LST based on averaging of the multidaily overpasses of MODISsensors? In situ ground measurements and ERA-Interim reanalyses data, both of which provide continuous information in either clear or cloudy conditions, have been used to validate the approach. Using LST measurements from 156 ground flux towers, it was found that the three mean values LST(AMD,AMN), LST(TMD,TMN), and LST(AMD,AMN,TMD,TMN) (mean biases of 0.19, 0.59, and 0.40 K, respectively) can all provide a reliable estimate of all-sky monthly mean LST. Of the three means, we recommend the use of LST(AMD,AMN,TMD,TMN) for monthly mean LST in climate studies as it provides the most complete coverage.When retrievals from either Terra or Aqua are not available, then either LST(AMD,AMN) or LST(TMD,TMN) may be used to fill the gaps. The intrinsic error in the MODIS monthly mean LST cannot be explained from monthly mean view time, view angle, and clear-sky ratio. MODIS monthly LST calculated using this approach LST(AMD,AMN,TMD,TMN) (RMSE52.65, mean bias < ±1K) will have wide applicability for climate studies and numerical model evaluation.

The Planck satellite in orbit mission ended in October 2013. Between the end of Low Frequency Instrument (LFI) routine mission operations and the satellite decommissioning, a dedicated test was also performed to measure the Planck telescope emissivity. The scope of the test was twofold: (i) to provide, for the first time in flight, a direct measure of the telescope emissivity; and (ii) to evaluate the possible degradation of the emissivity by comparing data taken in flight at the end of mission with those taken during the ground telescope characterization. The emissivity was determined by heating the Planck telescope and disentangling the system temperature excess measured by the LFI radiometers. Results show End of Life (EOL) performance in good agreement with the results from the ground optical tests and from in-flight indirect estimations measured during the Commissioning and Performance Verification (CPV) phase. Methods and results are presented and discussed., Comment: 18 pages, 18 pictures. Results from the Planck Satellite "End Of Life" Test Campaign

Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.

Microwave emissions at the L-band (1&ndash, 2 GHz) in Antarctica are characterized by a significant contribution of ice layers at great depth, from hundreds to a thousand meters. Brightness temperatures, thus, could provide the internal temperature of the ice sheet. However, there are two difficulties to overcome in developing an accurate retrieval algorithm. First, it is difficult to know precisely from which depths waves are emanating because the ice-absorption coefficient is uncertain at the L-band, despite several formulations proposed in the literature over the past few decades. Second, emissivity potentially varies in Antarctica due to remnant scattering in firn (or ice), even at the Brewster angle, and despite the low frequency, limiting the accuracy of the estimate of the physical temperature. Here, we present a retrieval method able to disentangle the absorption and emissivity effects from brightness temperature over the whole continent. We exploit the fact that scattering and absorption are controlled by different physical parameters and phenomena that can be considered as statistically independent. This independence provides a constraint to the retrieval method, that is then well-conditioned and solvable. Our results show that (1) the retrieved absorption agrees with the permittivity model proposed by M&auml, tzler et al. (2006), and (2) emissivity shows significant variations, up to 6% over the continent, which are correlated with wind speed and accumulation patterns. A possible cause of this latter point is density heterogeneity and sastrugi buried in the firn. These two results are an important step forward for the accurate retrieval of internal temperature using low-frequency microwave radiometers.

An autonomous system for field land surface temperature (LST) measurements taken at different observation angles was developed to be deployed easily at any conventional meteorological tower station. The system permits ground-truth data to be acquired on a continuous basis, and angularly scans land and sky hemispheres with a single thermal-infrared (TIR) radiometer. This paper describes the autonomous angular system and the methodology to assess ground-truth LST and relative-to-nadir emissivity data from system measurements. Ground-truth LSTs were used to validate satellite-retrieved LST products at two experimental sites (rice crop and shrubland areas). The relative-to-nadir emissivity values were used to analyze the anisotropy of surface emissive properties over thermally-homogeneous covers. The EOS-MODIS MOD11_L2/MYD11_L2 LST product was evaluated and shown to work within expected uncertainties (

Land surface temperature (LST) is an important parameter that directly affects the water and heat balance between the Earth surface and atmosphere. Mapping the LST distribution at continuous temporal and wide spatial scales is very helpful for researching many physical and biochemical processes. Remotely sensed instruments are the key players in these studies. Passive microwave remotely sensed data have the advantage of retrieving the land parameters under nearly all weather conditions because of the power to penetrate clouds. In this study, a practical two-stage algorithm, which uses single-frequency and double-polarization passive microwave brightness temperature observations, is presented to retrieve the LST over China. The vertically polarized land surface emissivity (LSE) at 18.7 GHz is first estimated by a parameterization relationship with the polarization ratio (PR), which is defined as the ratio of the horizontal to the vertical brightness temperatures at the same frequency. Subsequently, the LST is retrieved using the estimated LSE by ignoring the atmospheric effect. The evaluation of the simulated data shows an RMSE of 1.45 K, which is very encouraging. The cross validations by the satellite thermal infrared products are from daily to monthly time scales. The daily accuracy is 3.04 K and the bimonthly accuracy is 4.43 K. A high positive bias in arid and semiarid regions and a negative bias in frequently cloudy regions are noticeable in both comparisons. These biases do not reveal the uncertainties of this LST retrieval algorithm; on the contrary, the retrieved the Advanced Microwave Scanning Radiometer − EOS (AMSR-E) LST appears to be more reasonable compared to the thermal infrared LST under certain circumstances.

This document presents a short summary of the improvements made at CNRS and the identified limitations for the measure of the directional spectral emissivity thanks to the addition of a new spectroradiometer to the existing system. These improvements offer both an increased quantity of services in the same operating time (more samples measured), and also a better quality of the service thanks to the finely detailed angular spectral data now provided to the users.

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a mission, led and developed by NASA, that will measure a variety of climate variables with an unprecedented accuracy to quantify and attribute climate change. CLARREO consists of three separate instruments: an infrared (IR) spectrometer, a reflected solar (RS) spectrometer, and a radio occultation (RO) instrument. The mission will contain orbiting radiometers with sufficient accuracy, including on orbit verification, to calibrate other space-based instrumentation, increasing their respective accuracy by as much as an order of magnitude. The IR spectrometer is a Fourier Transform spectrometer (FTS) working in the 5 to 50 μm wavelength region with a goal of 0.1 K (k = 3) accuracy. The FTS will achieve this accuracy using phase change cells to verify thermistor accuracy and heated halos to verify blackbody emissivity, both on orbit. The RS spectrometer will measure the reflectance of the atmosphere in the 0.32 to 2.3 μm wavelength region with an accuracy of 0.3% (k = 2). The status of the instrumentation packages and potential mission options will be presented.

The infrared emission of four stainless steel textiles was characterized in the mid-infrared range, i.e. 3.6÷5.1 μm, by observing their temperature evolution under heating regime. The investigated steel textiles differ by the type of fabrics, resulting in some variation in the infrared emission. Standard test method for measuring emissivity was applied to the set of metallic textiles using infrared imaging radiometers as well as a reference surface of known high emissivity. The obtained experimental results allowed to retrieve the infrared emissivity at different applied temperatures. The experimental data were interpreted by means of Plank's theory of black-body radiation. Finally, all the investigated textiles appear to be suitable for thermal shielding applications.

A technique to measure the human skin emissivity in vivo is described for the frequency band 80-100 GHz. Emissivity measurements were performed on 60 participants, 35 males and 25 females, with ages ranging from 20 to 60 years. Results show that the emissivity of males is higher than that of females. The study suggests a trend in the emissivities with age and gender, which might be due to variations of skin thickness and water content. As non-contact screening is desirable in medical applications, passive millimeter-wave sensing could be a means of achieving this in the diagnosis of skin disease or damage, where the disease/damage alters the water content or the skin thickness.

© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. With the performance of millimeter wave security screening imagers improving (reduced speckle, greater sensitivity, and better spatial resolution) attention is turning to identification of anomalies which appear on the human body. Key to this identification is the understanding of how the emissive and reflective properties vary over the human body and between different categories of people, defined by age and gender for example. As the interaction of millimetre waves with the human body is only a fraction of a millimetre into the skin, precise measurement of the emission and reflection of this radiation will allow comparisons with the norm for that region of the body and person category. On an automated basis at security screening portals, this will increase detection probabilities and reduce false alarm rates, ensuring high throughputs at entrances to future airport departure lounges and transport networks. A technique to measure the human skin emissivity in vivo over the frequency band 80 GHz to 100 GHz is described. The emissivities of the skin of a sample of 60 healthy participants (36 males and 24 females) measured using a 90 GHz calibrated radiometer was found to range from 0.17±0.002 to 0.68±0.002. The radiometric measurements were made at four locations on the arm, namely: palm of hand, back of hand, dorsal surface of the forearm, and volar side of the forearm, where the water content and the skin thickness are known to be different. These measurements show significant variation in emissivity from person to person and, more importantly, significant variation at different locations on the arms of individuals. Males were found to have an emissivity 0.03 higher than those of females. The emissivity of the back of the hand, where the skin is thinner and the blood vessels are closer to the skin surface, was found to be lower by 0.0681 than the emissivity of the palm of the hand, where the skin is thicker. The measurements also show that the emissivity of the volar side location where the blood vessels are closer to the skin surface is lower by 0.0677 than the emissivity of the dorsal surface location. The measured differences agree with those differences estimated by a half space electromagnetic model of the interaction and can be interpreted in terms of the differing water contents and skin thickness of those regions of the body.

Any radiometer at a fixed location has a biased view when observing a convoluted, three-dimensional surface such as an urban canopy. The goal of this contribution is to determine the bias of various sensors views observing a simple urban residential neighbourhood (nadir, oblique, hemispherical) over a 24 hour cycle under clear weather conditions. The error in measuring a longwave radiation flux density (L) and/or inferring surface temperatures (T0) is quantified for different times over a diurnal cycle. Panoramic time-sequential thermography (PTST) data were recorded by a thermal camera on a hydraulic mast above a residential canyon in Vancouver, BC. The data set resolved sub-facet temperature variability of all representative urban facets in a 360° swath repetitively over a 24-hour cycle. This data set is used along with computer graphics and vision techniques to project measured fields of L for a given time and pixel onto texture sheets of a three-dimensional urban surface model at a resolution of centimetres. The resulting data set attributes L of each pixel on the texture sheets to different urban facets and associates facet location, azimuth, slope, material, and sky view factor. The texture sheets of L are used to calculate the complete surface temperature (T0,C) and to simulate the radiation in the field of view (FOV) of narrow and hemispheric radiometers observing the same urban surface (in absence of emissivity and atmospheric effects). The simulated directional (T0,d) and hemispheric (T0,h) radiometric temperatures inferred from various biased views are compared to T0,C. For a range of simulated off-nadir (φ) and azimuth (Ω) angles, T0,d(φ,Ω) and T0,C differ between −2.6 and +2.9 K over the course of the day. The effects of effective anisotropy are highest in the daytime, particularly around sunrise and sunset when different views can lead to differences in T0,d(φ,Ω) that are as high as 3.5 K. For a sensor with a narrow FOV in the nadir of the urban surface, T0,d(φ=0) differs from T0,C by +1.9 K (day) and by −1.6 K (night). Simulations of the FOV of hemispherical, downward-facing pyrgeometers at 270 positions show considerable variations in the measured L and inferred hemispherical radiometeric temperature T0,h as a function of both horizontal placement and height. The root mean squared error (RMSE) between different horizontal positions in retrieving outgoing longwave emittance L&uparrow; decreased exponentially with height, and was 11.2, 6.3 and 2.0 W m−2 at 2, 3, and 5 times the mean building height zb. Generally, above 3.5zb the horizontal positional error is less than the typical accuracy of common pyrgeometers. The average T0,h over 24 h determined from the hemispherical radiometer sufficiently above an urban surface is in close agreement with the average T0,C. However, over the course of the day, the difference between T0,h and T0,C shows an RMSE of 1.7 K (9.4 W m−2) because the relative contributions of facets within the projected FOV of a pyrgeometer do not correspond to their fractions of the complete urban surface.

A novel algorithm for the detection of precipitation is described and tested. The algorithm is applicable to any modern passive microwave radiometer on board polar orbiting satellites independent of the observation geometry and channel frequency assortment. The algorithm is based on the application of canonical correlation analysis and on the definition of a threshold to be applied to the resulting linear combination of the brightness temperatures in all available channels. The algorithm has been developed using a 2-year data set of co-located Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission precipitation radar (TRMM-PR) measurements and Advanced Microwave Sounding Unit (AMSU) Microwave Humidity Sounder and TRMM-PR measurements. This data set was partitioned into four classes depending on the background surface emissivity (vegetated land, arid land, ocean, and coast) with the same procedure applied for each surface class. In this paper we describe the procedure and evaluate the results in comparison with many well-known algorithms for the detection of precipitation. The algorithm shows a small rate of false alarms and superior detection capability; it can efficiently detect (probability of detection between 0.55 and 0.71) minimum rain rate varying from 0.14 mm h-1 (AMSU over ocean) to 0.41 (SSMIS over coast) with the remarkable result of 0.25 mm h-1 over arid land surfaces.

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 , the mean retrieval error of the present algorithm is 0.634 K, while the land surface emissivity errors range from −0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is −0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the results measured by the National Oceanic and Atmospheric Administration (NOAA) VIIRS LST products, and the results indicate that 82.63% of the difference values are within the range of −1 to 1 K, and 17.37% of the difference values are within the range of ±2 to ±1 K. In a conclusion, with the advantages of multi-sensors taken fully exploited, more accurate results can be achieved in the retrieval of land surface temperature.

Due to the difficulty in accurately interpreting surface emissivity spectra, problems remain in the application of passive microwave satellite observations over land surfaces. This study develops a parameterized soil surface emissivity model to quantify the microwave emissivity accurately and rapidly for Gaussian-correlated rough surfaces. We first analyze the sensitivity of surface emissivity to parameters using the advanced integral equation model (AIEM) simulated data. On the basis of the analysis and previous empirical models, two function factors that consider the polarization dependence of surface reflectivity are developed in the parameterized soil surface emissivity model. These factors also comprehensively account for the effects of surface roughness, soil moisture, and incident angle. A comparison with the AIEM simulated data indicates that the absolute error of effective reflectivity estimated by the parameterized soil surface emissivity model is small with a magnitude of 10−2. Validation through experimental measurements suggests that a good agreement could be obtained. The parameterized soil surface emissivity model is applied to simulate satellite measurements of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Compared with the commonly-used microwave land emissivity model developed by Weng et al. (2001), the simulation results using the parameterized soil surface emissivity model yield a lower root-mean-square error (RMSE) and the overall errors are reduced, particularly for horizontal polarization. The newly-developed parameterized soil surface emissivity model should be useful in improving our understanding and modeling the measurements of passive microwave radiometers.

Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4LST) project. The LST algorithm is based on the split-window technique with an explicit dependence on the surface emissivity. Performance of the methodology is assessed by using MEdium Resolution Imaging Spectrometer/Advanced Along-Track Scanning Radiometer (MERIS/AATSR) pairs, instruments with similar characteristics than OLCI/ SLSTR, respectively. The LST retrievals were properly validated against in situ data measured along one year (2011) in three test sites, and inter-compared to the standard AATSR level-2 product with satisfactory results. The algorithm is implemented in BEAM using as a basis the MERIS/AATSR Synergy Toolbox. Specific details about the processor validation can be found in the validation report of the SEN4LST project., 14 pages, 7 figures, 3 tables

Wind farms are located in areas with a high probability of ice occurrence. Icing involves problems such as energy losses, mechanical failures and downtimes. The priority is to detect icing in order to avoid these problems. Icing detection is a complex procedure for example, low temperatures are not a guaranty of ice formation, and other variables may affect. Different techniques have been recently proposed to detect ice in wind turbine blades. They are mainly based on damping of ultrasonic waves on the blade surface, or measuring the resonant frequency of a probe. But these methods have some drawbacks that may cause the system to fail, e.g. the behaviour of ultrasonic waves in composite materials is difficult to predict due to different fibre orientations, and the ice detection by changes in the resonance frequency could lead to false alarms due to variations in working conditions. This paper takes advantage of the remote sensing techniques to propose a novel approach for icing detection without physical contact. The approach is based on the drastic emissivity change that it is produced over a surface characterized with a low emissivity value when ice appears. An experiment was conducted using a broad-band thermal radiometer and a section of a wind turbine blade. Radiometric temperature measurements were collected over the blade with and without an aluminium foil patch. The piece of blade was cooled down and different scenarios were considered, including frozen with and without ice. This study was completed with a sensitivity analysis of the approach to dust accumulation, accounting for real operation conditions. Results show the feasibility of this technique to detect ice formation and discern between frozen and icing conditions.

International audience; A comprehensive calibration and mapping of the thermal microwave emission from Titan’s surface is reported based on radiometric data obtained at 2.18-cm wavelength by the passive radiometer included in the Cassini RADAR instrument. Compared to previous work, the present results incorporate the much larger data set obtained in the approximately ten years following Saturn Orbit Insertion. Brightness temperature data including polarization were accumulated by segments in Titan passes from Ta (October 2004) through T98 (February 2014). The observational segments were analyzed to produce a mosaic of effective dielectric constant based on the measurement of thermal polarization covering 76% of the surface, and brightness temperature at normal incidence covering Titan’s entire surface. As part of the mosaicking process we also solved for the seasonal variation of physical temperature with latitude, which we found to be smaller by a factor of 0.87 ± 0.05 in relative amplitude compared to that reported in the thermal infrared by Cassini’s Composite Infrared Spectrometer (CIRS). We used the equatorial temperature obtained by the Huygens probe and the seasonal dependence with latitude from CIRS to convert the brightness mosaic to absolute emissivity, from which we could infer global thermophysical properties of the surface in combination with the dielectric mosaic. We see strong evidence for subsurface (volume) scattering as a dominant cause of the radar reflectivity in bright regions, and elsewhere a surface composition consistent with the slow deposition and processing of organic compounds from the atmosphere. The presence of water ice in the near subsurface is strongly indicated by the high degree of volume scattering observed in radar-bright regions (e.g., Hummocky/mountainous terrains) constituting ∼ 10% of Titan’s surface. A thermal analysis allowed us to infer a mean 2.18-cm emission depth in the range 40 to 100 cm for the dominant radar-dark terrains (the remainder of Titan’s surface) at all latitudes of Titan, consistent with the deposition and possible processing and redistribution of tholin-like atmospheric photochemical products.

Measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission–Water 1 (GCOM-W1) satellite at 6.925 and 7.3 GHz and both linear polarizations over tropical cyclones (TCs) during 2012–2014 are used to derive a new geophysical function relating the brightness temperature to the sea surface wind speed (SWS) in extreme conditions. Similar sensitivity to the SWS at close C-band frequencies allowed correcting for the atmospheric contributions to the microwave radiance and estimating the brightness temperature $(T_{B})$ at the surface under TCs, combining theoretical modeling and measured $T_{B}$ analyses. Estimated oceanic $T_{B}$ 's were regressed against the wind speeds from the Best Track Archive to derive a new geophysical model function for the wind speed excess emissivity at AMSR2 C-band microwave frequencies.

International audience; Dunes cover about 15% of Titan’s visible surface, and represent one of the largest reservoirs of hydrocarbon solids on Titan ( and ). Herein, we use data from the Cassini spacecraft to derive constraints on the compositional and regional variability of Titan’s dune and interdune regions by combining spectral information from the Visual and Infrared Mapping Spectrometer (VIMS) and spatial information from Synthetic Aperture RADAR (SAR) data. Using the combined datasets, we extract pure infrared spectra of dune and interdune regions by extrapolating linear correlations between VIMS reflectance and dune area fraction calculated in each VIMS footprint from SAR images. We applied the same method using the Cassini RADAR Radiometer dataset to extract the microwave surface emissivity of the dune and interdune regions.Globally the dune spectra show little variation, but we find that the interdune spectra exhibit several different behaviors. Similarly, we extract from passive radiometry a mean dune emissivity of 0.98 ± 0.01, while interdune emissivity varies from 0.86 to 0.98. We find that the interdune regions are often spectrally similar to other Titan terrain units, namely Caladan Planitia, the Adiri Mountains, and Sinlap crater, while the dunes are spectrally distinct from all terrain units. Around Sinlap crater, the interdune regions correspond to the dark blue VIMS unit: the dunes could be forming on top of the ejecta, or the material corresponding to the blue unit could be depositing preferentially in the interdunes areas. There was one region in the Belet sand sea where we were unable to extract the dune and interdune spectra and emissivities in spite of high-quality data, which we interpret to result from a thick sand cover in the interdune regions, implying inactive or saturated dune fields. However, the fact that we were able to extract distinct dune and interdune spectra and emissivities in most of Titan’s dune fields makes a strong case for sand-free interdune areas of varying composition in these regions, on depths from micrometers to decimeters. This would imply that the sand dunes have been active recently on geologic timescales.

This article describes a new algorithm to retrieve the atmospheric water vapour total column (TCWV) using the Thermal InfraRed (TIR) measurements from the two TIR channels of the Along Track Scanning Radiometer (ATSR) instrument series, on board the European Space Agency's ERS-1, ERS-2 and ENVISAT satellites spanning 1991 to 2012. The use of the dual view capability of the ATSR-type instruments allows for the accurate and precise, day-time and night-time retrievals of cloud free TCWV over oceans. The retrieval scheme uses the instrument physical characteristics, in combination with advanced radiative transfer models and a sea surface spectral emissivity database. The retrievals therefore do not require algorithm tuning or adjustments to independent water vapour datasets.Inter-comparisons with the Remote Sensing Systems Special Sensor Microwave Imager (RSS SSM/I) and ECMWF ERA-Interim total water vapour column products have been carried out for a set of test cases and are discussed in detail. The initial results show that the ATSR TCWV data, aggregated at the SSMM/I or ECMWF spatial resolution, have no median bias with respect to the collocated datasets, and have an estimated precision of about 4% for the ATSR-2 and AATSR instruments and up to 15% for the ATSR-1 instrument. The TCWV precision for the 1×1km2 resolution is of the order of 12% for ATSR-2 and AATSR, while is about 30% for ATSR-1.This new physical algorithm can be readily extended to dual view radiometers similar to ATSR such as the upcoming Sea and Land Surface Temperature Radiometer (SLSTR) instrument on the European Copernicus Sentinel-3 satellite series. © 2015 Elsevier Inc.

Quantifying roughness effects on ground surface emissivity is an important step in obtaining high-quality soil moisture products from large-scale passive microwave sensors. In this study, we used a semi-empirical method to evaluate roughness effects (parameterized here by the parameter) on a global scale from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observations. AMSR-E brightness temperatures at 6.9 GHz obtained from January 2009 to September 2011, together with estimations of soil moisture from the SMOS (Soil Moisture and Ocean Salinity) L3 products and of soil temperature from ECMWF’s (European Centre for Medium-range Weather Forecasting) were used as inputs in a retrieval process. In the first step, we retrieved a parameter (referred to as the parameter) accounting for the combined effects of roughness and vegetation. Then, global MODIS NDVI data were used to decouple the effects of vegetation from those of surface roughness. Finally, global maps of the Hr parameters were produced and discussed. Initial results showed that some spatial patterns in the values could be associated with the main vegetation types (higher values of were retrieved generally in forested regions, intermediate values were obtained over crops and grasslands, and lower values were obtained over shrubs and desert) and topography. For instance, over the USA, lower values of were retrieved in relatively flat regions while relatively higher values were retrieved in hilly regions.

This work estimated and validated the land surface temperature (LST) from thermal-infrared Channels 4 (10.8 µm) and 5 (12.0 µm) of the Visible and Infrared Radiometer (VIRR) onboard the second-generation Chinese polar-orbiting FengYun-3A (FY-3A) meteorological satellite. The LST, mean emissivity and atmospheric water vapor content (WVC) were divided into several tractable sub-ranges with little overlap to improve the fitting accuracy. The experimental results showed that the root mean square errors (RMSEs) were proportional to the viewing zenith angles (VZAs) and WVC. The RMSEs were below 1.0 K for VZA sub-ranges less than 30° or for VZA sub-ranges less than 60° and WVC less than 3.5 g/cm2, provided that the land surface emissivities were known. A preliminary validation using independently simulated data showed that the estimated LSTs were quite consistent with the actual inputs, with a maximum RMSE below 1 K for all VZAs. An inter-comparison using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST product MOD11_L2 showed that the minimum RMSE was 1.68 K for grass, and the maximum RMSE was 3.59 K for barren or sparsely vegetated surfaces. In situ measurements at the Hailar field site in northeastern China from October, 2013, to September, 2014, were used to validate the proposed method. The result showed that the RMSE between the LSTs calculated from the ground measurements and derived from the VIRR data was 1.82 K.

The goal of this work is to inter-compare a number of global land surface emissivity products over various land-cover conditions to assess their consistency. Ultimately, the discrepancies between the studied emissivity products will help interpreting the divergences among numerical weather prediction models in which land emissivity is a key surface boundary parameter. The intercompared retrieved land emissivity products were generated over five-year period (2003–2007) using observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E), Special Sensor Microwave Imager (SSM/I), The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Windsat. First, all products were reprocessed in the same projection and spatial resolution as they were generated from sensors with various configurations. Then, the mean value and standard deviations of monthly emissivity values were calculated for each product to assess the spatial distribution of the consistencies/inconsistencies among the products across the globe. The emissivity values from four products were also compared to soil moisture estimates and satellite-based vegetation index to assess their sensitivities to the changes in land surface conditions. Results show that systematic differences among products exist and variation of emissivities at each product has similar frequency dependency at any land cover type. Monthly means of emissivity values from AMSR-E in the vertical and horizontal polarizations seem to be systematically lower across various land cover condition which may be attributed to the 1.30 a.m./p.m. overpass time of the sensor and possibly a residual skin temperature effect in the product. The standard deviation of the analysed products was the lowest (less than 0.01) in rain forest regions for all products and the highest in northern latitudes, above 0.04 for AMSR-E and SSM/I and around 0.03 for WindSat. Despite differences in absolute emissivity estimates, all products were similarly sensitive to changes in soil moisture and vegetation. The correlation between the emissivity polarization differences and NDVI values showed similar spatial distribution across the products with values close to the unit except over densely vegetated and desert areas.