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In order to cover the warm end of Earth-scene brightness temperature (TB) range of passive microwave radiometers, intercalibration over warm scenes is necessary. This article presents a methodology to intercalibrate the microwave radiation imager (MWRI) on the Chinese second-generation meteorological satellite Fengyun 3C (FY-3C) with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) over the warm scenes of dense forests using the double-difference (DD) method. Based on the microwave radiative transfer model (RTM), an intercalibration method is developed, in which a modified land surface emissivity (LSE) model for dense forests is proposed. The forests with optically thick canopy are identified in terms of polarization TB differences and normalized difference vegetation index (NDVI) extracted from the latest vegetation product of Moderate-Resolution Imaging Spectroradiometer (MODIS). The matching TBs between FY-3C MWRI and GMI over dense forest warm scenes are collected and analyzed together with the TBs over ocean surfaces obtained by Zeng and Jiang (2020). The results show that: 1) FY-3C MWRI's observations are generally underestimated, and the intercalibration biases are polynomial functions of observations; 2) the intercalibration biases at the warm end are relatively smaller than those at the cold end; and 3) the calibration in the ascending orbits (MWRIA) is relatively better than that in the descending orbits (MWRID). At the tropical rain forest scene TBs defined in this work, the intercalibration biases (mean ± standard deviation at the mean) in the FY-3C MWRI channels of 10 V, 10 H, 18 V, 18 H, 23 V, 36 V, 36 H, 89 V, and 89 H are, respectively, -1.3 ± 0.7, -1.9 ± 1.1, 1.6 ± 0.6, 2.5 ± 0.8, -0.2 ± 0.5, -2.0 ± 0.6, -2.4 ± 0.7, -0.2 ± 0.6, and -0.1 ± 0.6 K for the ascending orbits, while they are, respectively, -4.0 ± 0.8, -5.4 ± 1.2, -1.4 ± 0.7, -1.2 ± 0.8, -2.9 ± 0.5, -4.9 ± 0.7, -5.5 ± 0.7, -2.7 ± 0.8, and -2.3 ± 0.7 K for the descending orbits. The in-orbit calibration coefficients of GMI are successfully transferred to FY-3C MWRI.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station (ISS) on June 29, 2018, and currently provides the highest spatial resolution thermal infrared (TIR) data (38 m x 69 m) available from space. In this study, we validated the ECOSTRESS level-2 Land Surface Temperature (LST) and emissivity product at fourteen global sites to Stage-1 status. Two primary methods are recommended for the validation of LST data: Temperature-based (T-based) and Radiance-based (R-based) methods. The T-based method requires calibrated measurements of the ground leaving radiance concurrent with the satellite overpass. In contrast, the R-based method uses a radiative closure simulation with external atmospheric profiles and an a priori knowledge of surface emissivity. Using these standard methods, we validated 1139 ECOSTRESS clear-sky observations between August 1, 2018, and March 31, 2020. For LST, the results show good agreement with ground-based measurements with an average root mean square error (RMSE) of 1.07 K, mean absolute error (MAE) of 0.40 K, and r²>0.988 at all sites. However, a cold bias of ~0.75 K was identified for temperatures below 295 K linked to calibration issues that will be addressed in future reprocessing of the data. Retrieved emissivity comparisons with laboratory spectra had an RMSE of 0.023 (2.3%) for all bands on average. With the decommissioning of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra in 2023, the multispectral and high-spatial-resolution characteristics of ECOSTRESS data serve as a pathfinder to the National Aeronautics and Space Administration's (NASA) Surface Biology and Geology (SBG) designated observable with an expected launch in 2026.

The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the two Sentinel-3 satellites provides daily global coverage observation at daytime and nighttime. The split-window (SW) algorithm is currently used to retrieve the land surface temperature (LST) from SLSTR images; however, this algorithm has to utilize visible and near-infrared (VNIR) images and land cover to determine pixel emissivity. For nighttime observation, VNIR cannot be observed, and this limitation complicates the LST retrieval from nighttime images using the SW algorithm. This article proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the nighttime LST and emissivity from one middle-infrared (MIR) and two thermal-infrared (TIR) nighttime Sentinel-3 SLSTR images. The sensitive analysis showed that the algorithm could theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.015, respectively. Ground validation showed that the nighttime LST retrieval error was approximately 1.84 K and the bias was approximately −0.33 K. Finally, the TES algorithm was applied to obtain the LST and emissivity images over northern China as an example. The emissivity retrieved from the nighttime observation can be used in the daytime SW algorithm to improve its feasibility in the LST retrieval process.

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

Ground-based radar and radiometer data observed during the 2017–2018 winter season over the Pyeongchang area on the east coast of the Korean Peninsula were used to simultaneously estimate both the cloud liquid water path and snowfall rate for three types of snow clouds: near-surface, shallow, and deep. Surveying all the observed data, it is found that near-surface clouds are the most frequently observed cloud type with an area fraction of over 60 %, while deep clouds contribute the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with the vast majority hardly reaching 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and the liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager (GPM/GMI) channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as an a priori database. Under an idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution, and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.32, 0.41, and 0.62, respectively, for near-surface, shallow, and deep snow clouds over land surfaces; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increasing from 0.32 to 0.52, while a smaller improvement is found for shallow and deep clouds. This study provides a general picture of the microphysical characteristics of the different types of snow clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.

Remote sensing of Land Surface Temperature (LST) generally requires atmospheric parameters and the emissivity ( ∊ ) of the target to be estimated. The atmospheric up- and downwelling radiances and transmittance can be accurately modelled using radiative transfer models and profiles of relative humidity and temperature, either measured by radiosonde probes or retrieved from assimilating weather models. The estimation of ∊ is a large source of uncertainty in the resulting LST product, and there are various approaches using multi-angle observations, multispectral optical or multispectral thermal infrared imagery. In this paper, the estimation of LST from the Thermal InfraRed Sensor (TIRS) on board Landsat 8 is evaluated using more than 6 years of in situ temperature measurements from a network of 14 Autonomous Weather Stations (AWS) in Belgium. ∊ is estimated from concomitant atmospherically corrected imagery from the Operational Land Imager (OLI) using two new neural network approaches trained on ECOSTRESS spectra, and an established NDVI based method. Results are compared to using ∊ = 1 and the ASTER Global Emissivity Dataset. LST retrievals from L8/TIRS perform well for all emissivity data sources for >500 matchups with AWS subsoil temperature measurements: Mean Differences 0.8–3.7 K and unbiased Root Mean Squared Differences of 2.9–3.5 K for both B10 and B11. The use of unity emissivity gives the best results in terms of MD (0.8 K) and unb-RMSD (3 K). Similar ranges of unb-RMSD are found for >500 matchups with broadband radiometer temperatures (2.6–3.1 K), that have lower absolute MD values (−2.2–0.6 K). For the radiometer temperatures, both the neural net approaches gave lowest MD, in the best case ± 0.1 K. The present investigation can hence recommend the neural nets to derive ∊ for the retrieval of LST over the AWS in Belgium. Using published matchup results from other authors however, no single source of ∊ data performed better than ∊ = 1 , but this could be due to their low number of matchups. Further efforts for estimating representative pixel average emissivities are needed, and establishing a denser in situ measurement network over varied land use, with rather homogeneous land cover within a TIRS pixel, may aid further validation of a per pixel and per scene ∊ estimates from multispectral imagery. AWS data seems valuable for evaluation of satellite LST, with the advantage of a much lower cost and higher potential matchup density compared to conventional radiometers.

Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-band SM active passive (SMAP) radiometer observations. The SMI was proposed on the basis of two key foundations: 1) vegetation and roughness have similar effects on “depolarization” of microwave emission, while SM enhances polarization differences and 2) vegetation and roughness generally impose positive effects on surface emissivity, while SM and emissivity are negatively correlated. Based on the two physical principles, it is possible to decouple the effects of SM and those of vegetation and surface roughness in a 2-D space independent of vegetation type and roughness condition. The proposed SMI was then validated by in situ measurements from five dense SM networks covering different vegetation and climatic conditions and also compared with SMAP passive and European space agency climate change initiative (ESA CCI) SM products at a coarse resolution of 36 km, and SMAP-enhanced passive and Japan Aerospace Exploration Agency (JAXA) advanced microwave scanning radiometer (AMSR2) SM products at a medium resolution of 9 km. The results show that the new SMI is able to well reproduce the temporal dynamic of SM with a favorable averaged correlation coefficient value of 0.87 and 0.84 at 36 and 9 km, respectively, higher than that of SMAP passive (0.80), SMAP-enhanced passive (0.77), ESA CCI (0.69), and JAXA AMSR2 (0.53). After removing the systematic differences between satellite and site-specific SM data by using the cumulative distribution function (CDF) matching technique, the SMI can achieve an average root mean squared error (RMSE) of 0.031 and 0.036 m3m−3 at 36 and 9 km during the validation period, respectively, lower than that of the satellite SM products. In addition to surface temperature, the SMI does not need any further information from other sensors [e.g., the optical normalized difference vegetation index (NDVI) or leaf area index (LAI) data] to guarantee an all-weather monitoring. Therefore, it has great potential to estimate SM variability on a global scale.

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.

Land surface emissivity (LSE) is a key parameter for the determination of land surface temperature (LST) from thermal remotely sensed data. A new LSE product has been developed at the National Oceanic and Atmospheric Administration (NOAA), College Park, MD, USA, to enhance the LST product for the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite R-Series (GOES-R) missions as well as to support the forecasting models. A 1-km resolution bare ground emissivity was derived from the historical Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset (ASTER-GED) and Moderate Resolution Imaging Spectroradiometer (MODIS) LSE product. It is then spectrally adjusted to the split window (SW) channels of Visible/Infrared Imager Radiometer Suite (VIIRS) onboard JPSS and Advanced Baseline Imager (ABI) from GOES-R. VIIRS daily green vegetation fraction and snow fraction products are subsequently used to account for the dynamic variations. Validation results indicate that the product has a good agreement with in situ observations, with an emissivity difference less than 0.006 at the bare surface sites and a difference less than 0.007 at one cropland site with three growth stages. The intercomparison with NASA VIIRS daily LSE product proves a good agreement with a standard deviation of less than 0.012. To evaluate the LSE performance in LST retrieval, a whole year VIIRS LST is produced and validated over the SURFace RADiation budget observing network (SURFRAD) sites. The results indicate that the LSE works well with LST RMSE of 1.78 (daytime) and 1.58 K (nighttime). The new LSE algorithm has been integrated to NOAA-20 LST with the status of provisional maturity and will be applied on GOES-16/17 LST product in the near future.

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.

Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have shown a cold bias relative to available weather station measurements, the source of which is unknown. Here, we show that the MODIS cold bias likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that a cold bias in remote sensing temperatures is present not only in MODIS LST products, but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat surface temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the cold bias was most pronounced in the winter (mean cold bias > 8 °C), and least pronounced in the spring and summer (mean cold bias

Thermal Infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs) provide low-cost, high spatial and temporal resolutions TIR data. This paper develops a novel single-channel algorithm adaptive to UAV TIR data. Atmospheric parameters were estimated using atmospheric reanalysis data and surface emissivity were acquired by the Portable Fourier transform thermal infrared spectrometer (102F). Then the effective atmospheric transmittance and emissivity were calculated owing to the broad spectral range of the UAV TIR channel. The results were validated using in-situ land surface temperature (LST) derived from SI-111 radiometers at an area of Baotou City, China. The root mean square error (RMSE) were 2.31K on 24 September and 1.82K on 26 September, which indicates that the proposed algorithm is a promising method to estimate LST from UAV TIR images.

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku-and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects., J.G. acknowledges the efforts by Ian Adams (NASA Goddard Space Flight Center) for support of airborne data collection during the IMPACTS campaign, and support from NASA grant 80NSSC20K0087.

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.

Accurate ground measurements of land surface temperature (LST) are necessary for validating satellite LST products. In order to provide reliable data, ground radiometers must be calibrated with reference to an international standard, and radiometric temperatures must be corrected for land surface emissivity. As opposed to water, land surface emissivity is not usually known for many ground covers, so an emissivity value has to be assumed, assigned from spectral emissivity libraries or measured for each land cover and spectral band considered. The aim of this study is to show the laboratory calibration and the methodology for simultaneous field measurements of LST and emissivity employed in the comparison experiment held in the project Fiducial Reference Measurements for validation of Surface Temperature from Satellites. We used multiband CE-312 radiometers (five narrow bands plus one broad band in the 8-13 ?m window) to simultaneously retrieve LST and band emissivities by means of the temperature-emissivity separation (TES) method for different ground covers (clover, tarmac, soil, gravel and sand). The TES method requires near-simultaneous measurements of ground-leaving radiances and sky downwelling radiances; the latter being measured in the field using a gold reflectance panel. For each surface cover, TES provided the band emissivity in the CE-312 bands and the LST from continuous radiance measurements performed over time. As a result of the experiment, we present the laboratory calibration of the CE-312 radiometers carried out against a traceable, reference blackbody, and the LST series and band emissivity values for the ground covers considered, together with a detailed LST uncertainty analysis including the uncertainties associated to the calibration of ground radiometers, the emissivity estimation by means of the TES method, and the sky radiance measurements, among others. According to these results, the total LST uncertainty was estimated at 0.4-0.6 K for the ground covers measured during the experiment.

The Soil Moisture Active/Passive (SMAP) microwave radiometer is a fully polarimetric L-band radiometer flown on the SMAP satellite in a 6 AM/6 PM sun-synchronous orbit at 685 km altitude. After the SMAP L1B_TB data product version 3 was released in 2016, the radiometer has been undergoing further calibration and validation with the goal of reducing both the bias in the cold-sky measurements and calibration drift in the global ocean measurements experienced during eclipse seasons in data product version 3. The post-launch calibration algorithm has been upgraded by using new estimates of the reflector emissivity as well as using multiple scenes to calibrate the radiometer internal reference sources and antenna gain simultaneously. In addition, a correction offset is applied to the ocean roughness model for horizontal polarization based on nadir observations. Test and validation results show that the goal is achieved (e.g., biases are removed and the calibration stability achieved for data release version 4 is 0.1 K (rms) over both the global ocean and CS).

New empirical geophysical model functions (GMFs) have been developed to interpret 89-GHz brightness temperature measurements over cold Arctic seas. Careful data screening is applied to the Advanced Microwave Scanning Radiometer 2 (AMSR2) multifrequency measurements to exclude atmospheric scattering and large absorption impacts, to estimate the 89-GHz sea surface emissivity and its relative changes with surface wind speed (SWS). Matched-up wind speeds are directly derived from the AMSR2 low-frequency measurements. The GMFs are obtained from the AMSR2 level 1R 89 GHz measurements corrected for the atmospheric impact with physical models and atmospheric parameters derived from the AMSR2 data. A carefully selected database encompasses mostly cold sea conditions (

Accurate measurement of heat flux (HF) is significant in both industrial engineering and scientific research. It is acknowledged that the radiative HF can be easily determined by radiometers. In order to measure the conductive or convective HF distribution flowing across a surface, this paper presents a novel non-contact measuring method based on the infrared (IR) technique and inverse heat transfer method (IHTM). In addition, a specially designed multi-color-reference (MCR) which is composed of four sectors with four different known emissivity will be attached to the focused surface in the practical measurement. Therefore, different radiation signals among the four different sectors of MCR as well as the focused surface will be detected by an IR camera, through which a whole radiation temperature map can be obtained. Based on the differences in the radiation temperatures of the surface and MCR, the IHTM can be adopted to yield useful boundary conditions such as emissivity of the surface, convective heat transfer coefficient, and the critical HF distribution. The measuring method presented in this study combines the advantages of contact and non-contact measurement, which can accurately obtain the HF distribution under the difficult-contacted conditions such as the HF monitoring of a boiling furnace, HF estimating of the aerodynamic heating surface, etc. Numerical simulation and experimental verification were organized to justify the newly-developed measuring method. Relative error in the simulation is estimated to be less than 2%, and that in the experiment is calculated to be around 4.23%. The result displays a relatively high precision of the measuring method, demonstrating that such a method can be applied in practical application.

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.

When using traditional methods to measure the microwave/millimeter-wave emissivity of small targets, errors arise due to that the antenna's main lobe and side lobe cannot be completely covered by the target. To eliminate the errors exist in traditional measurement methods, this paper first analyzes the main sources of errors and gives the analytical expression of the errors. On this basis, an improved method named voltage method is proposed. To test the effectiveness of the improved method, the horizontal polarization emissivity and the vertical polarization emissivity of a metal plate coat with stealthy nano-materials were measured at different observation angles by voltage method and traditional method, respectively. A dick radiometer working in 35 GHz is used in the experiments. Simultaneously, the accurate emissivities of the target are obtained by standard arch method. The results show that the voltage method improves the measurement accuracy largely compared to the traditional method.

Vegetation cover absorbs and scatters L-band microwave emission measured by SMOS and SMAP satellites. Misrepresentation of this phenomena results in uncertainties when inferring, for instance, surface soil moisture in retrieval algorithms that commonly utilize the tau-omega model which is most applicable for a weakly scattering medium. In this study, we investigate the degree to which multiple-scattering is prevalent over a range of land cover classifications (from lightly vegetated grasslands to dense forests) at the satellite scale by explicitly accounting for multiple-scattering in a first-order radiative transfer model, developed here. Even though the tau-omega model with effective parameters can possibly capture higher-order scattering contributions, deliberately partitioning scattering into different components is required to estimate multiple-scattering properties. Specifically, we aim to determine how one can partition between zeroth and first-order radiative transfer terms within a retrieval algorithm without ancillary information, determine whether this method can detect first-order scattering at the SMAP measurement scale without ancillary information, and quantify the magnitude of detected scattering. A simplified first-order radiative transfer model which characterizes single interactions of microwaves with a scattering medium is developed for implementation within retrieval algorithms. This new emission model is implemented within a recently developed retrieval algorithm, the multi-temporal dual channel algorithm (MT-DCA), which does not require ancillary land use information. Scattering parameters as well as SM and vegetation optical depth (τ) are retrieved simultaneously in Africa and South America using the first year of SMAP brightness temperature measurements on a 36 km grid. Specifically, an introduced time invariant first-order scattering coefficient (ω1) is retrieved representing microwave emission interaction with the canopy. We find that ω1 is typically zero in lightly vegetated biomes and non-zero (~0.06) in 74% of the forest pixels. In forest-dominated pixels, the median first-order emissivity is 0.04, or about 4.3% of a given SMAP radiometer brightness temperature measurement. Additionally, explicitly accounting for first-order scattering terms in the radiative transfer model tends to increase SM and τ retrievals by a median of 0.02 m3/m3 and 0.1, respectively, only in forested regions. This study demonstrates the first attempt to explicitly partition higher-order scattering terms in a retrieval algorithm at a satellite scale and ultimately provides a fundamental understanding and quantification of multiple-scattering from grasslands to forests.

Land emissivity sensed within a suitable range of wavelengths by microwave radiometer is useful to deduce land surface temperature and land surface emissivity. Brightness temperature measurements by space-borne microwave sensors have been utilized to determine land surface emissivity for the study of climatology, hydrological and agricultural applications. Currently, application of sounder data is gaining attention, in analyzing land surface characteristic features especially due to the higher spatial resolution, in general. In the present work, an attempt is made to obtain land surface emissivity from the six channels of SAPHIR (Sondeur Atmospherique du Profil d Humidit Intertropicale par Radiometrie), sensor with special reference to bare land and south Western Ghats of India during pre-monsoon and post-monsoon seasons. The innovation of the present analysis is to demonstrate the possibility of estimating and retrieving surface parameters from emissivity values retrieved from brightness temperature measured from SAPHIR sounder with channel 6 of 183.31 ± 11.0 GHz over selected study area with various surface conditions. Further, the present analysis relates to application of emissivity values retrieved from brightness temperature measured from SAPHIR sounder with channel 6, i.e., 183.31 ± 11.0 GHz for the study of vegetation, climatology and agricultural applications.

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.

The lunar surface temperature (LST) derived from thermal infrared (TIR) measurements can aid in understanding the physical properties of the lunar surface. The Diviner Lunar Radiometer Experiment (herein, Diviner) sensor provides global lunar surface observation in seven TIR channels. However, its retrieval of LST constantly uses a single emissivity value (i.e., 0.95) by ignoring the spatial variation of lunar surface, thereby reducing the accuracy of temperature and day–night temperature difference. To overcome this problem, this study developed a physical method called temperature–emissivity separation (TES) algorithm to retrieve LST and lunar surface emissivity from the daytime observation in three Christiansen Feature (CF) channels (7.55–8.05, 8.10–8.40, and 8.38–8.60 μm) of the Diviner, and then used the emissivity from daytime observation to inverse LST at nighttime observation. Findings showed that the TES algorithm could retrieve LST and emissivity with an error of less than 0.8 K and 0.008, respectively. However, observation noise significantly affected the retrieval accuracy, particularly for the low‐temperature pixels; moreover, high retrieval accuracy requires a surface temperature higher than 240 K. The new algorithm was applied to obtain the daytime and nighttime LST and emissivity from the Diviner images. Results showed that the LST retrieved from the algorithm differed approximately 3.9 K from that calculated from a single emissivity 0.95. Finally, an example of global surface temperature and emissivity were obtained. Consequently, the CF pixels were found to distribute in the latitude range from −60° to 60°; however, they did not have a large distribution in high‐latitude and near‐polar regions.

The paper describes a non-calibration radiometric method for measuring the effective emissivity Eeff of a rough underlying surface. The method does not require absolute calibration of the radiometer, but uses a black body with the temperature of the underlying surface. This simplifies measurements and makes it possible to measure the dependences of the effective emissivity both on the parameters of the underlying surface itself, which are recorded at the measurement site, and on the parameters of the atmosphere. The effective emissivity Eeff harmoniously corresponds to the radiation transfer model and the equations that can be used for sounding the Earth from spacecraft. The method allows to measure the real and imaginary parts of the dielectric constant of underlying surface under natural conditions. The data of field measurements performed from the offshore platform are presented.

In this study, the spectral emissivity of aeronautical alloy DD6 at eight temperatures (473 K, 523 K, 573 K, 623 K, 673 K, 723 K, 773 K and 823 K) over the spectral range from 3 μm to 20 μm is systematically studied under argon condition. The multi-temperature calibration method is adopted to accurately obtain the FTIR spectrometer response, and then the improved method that eliminates the disturbances caused by background radiation is used for spectral emissivity determination. The effects of temperature, wavelength and surface roughness on the spectral emissivity of DD6 are investigated. The results show that the spectral emissivity increases with increasing temperature and decreases with increasing wavelength, and the emissivity increases with increasing surface roughness. The fitting relationship between the spectral emissivity and temperature is established, and it is found that the fitting results of the complex model are more consistent than those of other models. To verify the validity of the complex model, the temperature inferred from the model is compared with that measured by the radiometer and thermocouple directly. The modified emissivity model based on the Agababov roughness function is established to infer the spectral emissivity of samples with different surface roughness. The relative combined uncertainty of spectral emissivity is evaluated to be better than 3.2% except for the atmospheric absorption bands.

Passive microwave brightness temperature (TB) radiometers are widely used to retrieve several atmospheric and surface parameters such as precipitation, soil moisture, freeze and thaw, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments, incident angles, footprints, spatial resolutions, and radiometric characteristics, a combination of data from different microwave sensors could be inconsistent. For this reason, this study primarily uses the nonsynchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements to construct the diurnal cycle of TBs for each month. This diurnal pattern could be used as a point of reference to validate and calibrate the diurnal cycle of TBs from fusing the measurements of other sensors with daily fixed acquisition times (SSM/I, SSMIS, AMSR2, and etc.). The data from these sensors should also be merged and harmonized in order to build a comprehensive global diurnal cycle of passive microwave TBs. This highly frequent diurnal cycle will eventually help predict the emissivity estimation and potentially further advance the accurate prediction of the estimated time of the freeze/thaw (FT) transition states. Global comparison of TBs obtained from different sensors shows a moderate variance with a significant dependence on land cover type. The results of this study can enhance the temporal detection of freeze and thaw which is more helpful during the transition times when multiple FT changes may occur within a day.

The SMAP L-band microwave radiometer is in its extended mission of measuring soil moisture and freeze/thaw state globally for quantifying the water and carbon cycles. Instrument behavior has been stable over the past 4 years and 9 months. With the concurrent calibration of the internal calibration parameters and the antenna gain after estimating reflector emissivity, the SMAP radiometer measurements exhibit 0.1 K (rms) stability and nearly zero biases over the averaged global ocean and monthly Cold Sky views. The data (version 4) were released to the public in 2018 for various science activities. Now the radiometer data are under revisit to improve the absolute radiometric calibration and reduce calibration drift. Several approaches are investigated to obtain the optimal solution. In addition, the correction to the radiometer measurement when the SMAP radar transmitter was operational will also be revisited for the next data release. The performances of the calibration revisit and Radio-Frequency Interference (RFI) trends will be presented as well.

We present and discuss the details of the SMAP radiometer calibration that is used in the NASA/RSS ocean surface salinity algorithm. The values for antenna spillover, noise diode and reflector emissivity are fine-tuned to give accurate antenna and brightness temperature values for the global ocean and the Amazon rain forest. The calibration is dynamically adjusted to account for small calibration drifts over time. An adjustment of the thermal model for the reflector temperature is necessary in order to mitigate biases that occur when the SMAP spacecraft goes in and out of solar eclipse. An important test of the reflector emission is an analysis of SMAP brightness temperature differences between ascending and descending swaths. The radiometric calibration accuracy of the NASA/RSS SMAP salinity data set is better than 0.1 Kelvin, which is necessary for accurate ocean surface salinity retrievals.

The Community Microwave Emission Modelling platform (CMEM) has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the forward operator radiative transfer model for low frequency passive microwave brightness temperatures (TB). It is used at ECMWF for L-band TB monitoring over snow free areas. In this paper, upgrades to CMEM are presented in order to explore forward modelling in snow-covered areas for coupled land-atmosphere numerical weather prediction systems. The upgrades enable to use CMEM on an extended range of frequencies and the Helsinki University of Technology multi-layer snow emission model is implemented. Offline CMEM experiments are evaluated against AMSR2 (Advanced Microwave Scanning Radiometer 2) observations showing that simulated TB is improved when using a multi-layer snow scheme, compared to a single-layer scheme. The improvements mainly result from a better representation of snow characteristics in the multi-layer snowpack model. CMEM is also evaluated in the Integrated Forecasting System and coupled to RTTOV (Radiative Transfer for TOVS). The numerical results show improved simulated TB at low frequency V polarization over snow-covered area compared to a configuration using emissivity atlas. Degradations at frequencies higher than 20 GHz indicate that further improvements are required in the emissivity and snowpack properties modelling.

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.

An absolute cryogenic radiometer (ACR) has been developed at the National Institute of Metrology of China. The system was built based on a mechanical cooling system, with a cavity receiver working at 5.2 K and a noise-equivalent power of ~2 nanowatts. The ACR cavity receiver was made from pure copper by electroplating, weighing only

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.

L-band microwave radiometers (1.4 GHz) are relied in the field of remote sensing to retrieve and analyze land surface characteristics. However, the existing ground based L-band microwave radiometers are not portable and requires heavy maintenance. This paper focuses on introducing a new terrestrial hand held L-band microwave radiometer to measure the passive microwave data over bare ground surface. Soil temperature, brightness temperature (TB), emissivity and soil moisture of the bare ground is measured and the relationships between the measured values are evaluated for the present study.

This paper describes the technical approach to generate homogeneous warm brightness temperature scene binary mask over the earth for the intercalibration of microwave imagers. The objective is to identify homogeneous desert scenes as targets in radiometric intercalibration of multisource multisensor systems such as Global Precipitation Measurement (GPM) constellation. In this paper, a method is developed to generate mask over continental Australia using 10, 18, and 37 GHz vertical and horizontal polarized radiance observations from GPM Microwave Imager data. Major factors considered are diurnal variation, second Stokes parameter, spatial homogeneity, and surface emissivity. Major advantage of this tool is its minimal, almost negligible, dependence on any external ancillary modeled or observed data.

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

The Visible/Infrared Imaging Radiometer Suite (VIIRS) is a key sensor on the Suomi National Polar-orbiting Partnership satellite as well as the Joint Polar Satellite System (JPSS). Emissive band calibration in the 3.7 to 12.5 μm wavelength is performed based on spectral emittance of a blackbody. This paper presents the results of the optical characterization for two builds for JPSS J3 and J4, of the VIIRS blackbody. This characterization is a combination of measurement and analysis.

Accurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary datasets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead to approximately a 0.015 difference in passive microwave LSE values, especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 with an independent product—Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2)—shows spatial pattern correlations of order 0.92 at all frequencies. However, there are considerable differences in magnitude between these two LSE estimates, possibly because of differences in incidence angles, frequencies, observation times, and ancillary datasets.