Microwave Radiative Transfer Model Research Articles

QLB-NET: A Dense Soil Moisture and Freeze–Thaw Monitoring Network in the Qinghai Lake Basin on the Qinghai–Tibetan Plateau

Abstract Soil moisture (SM) and soil freeze–thaw (FT) are two relatively active surface parameters that are significant to the sustainable development of the water–land–air–plant–human nexus. Over time, regional or global SM and FT datasets with different spatial resolutions have been developed. In response to the requirements of multiscale product validation and multisource uncertainty tracking, a soil moisture and soil temperature (ST) monitoring network in the Qinghai Lake Basin (QLB-NET) was established in September 2019. The QLB-NET is characterized by densely distributed in situ sites (82 sites) measuring SM and ST at 5-, 10-, and 30-cm depths, with 60 sites in a large-scale network covering an area of 36 km × 40 km and 22 sites evenly distributed across two small-scale 1 km × 1 km networks. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., 36 km × 36 km, 25 km × 25 km, and 0.25° × 0.25°. When statistics are correspondingly performed over 50 out of 54, 25 out of 29, and 25 out of 28 sites, the results are described as follows: 1) the standard deviation of the mean SM varies between 0.0127 and 0.0196 m3 m−3, with the corresponding difference between the upper and lower quartiles being less than 0.02 m3 m−3; 2) the ground freeze–thaw state can be correctly identified with high probabilities ranging from 85.3% to 100% on two freeze–thaw transitional dates. The QLB-NET observed datasets are distributed online and will be continuously updated through cooperation with the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), facilitating product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model.

Forward modelling of synthetic-aperture radar (SAR) backscatter during lake ice melt conditions using the Snow Microwave Radiative Transfer (SMRT) model

Abstract. Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow

Abstract. Improved modelling of snow emissivity is needed to improve the assimilation of surface-sensitive atmospheric sounding observations from satellites in polar regions for numerical weather prediction (NWP). This paper evaluates emissivity simulated with the Snow Microwave Radiative Transfer (SMRT) model using observations of Arctic tundra snow at frequencies between 89 and 243 GHz. Measurements of snow correlation length, density and layer thickness were used as input to SMRT, and an optimisation routine was used to assess the impact of each parameter on simulations of emissivity when compared to a set of Lambertian emissivity spectra, retrieved from observations of tundra snow from three flights of the Facility for Airborne Atmospheric Measurements (FAAM) aircraft. Probability distributions returned by the optimisation routine demonstrate parameter uncertainties and the sensitivity of simulations to the different snow parameters. Results showed that SMRT was capable of reproducing a range of observed emissivities between 89 and 243 GHz. Varying correlation length alone allowed SMRT to capture much of the variability in the emissivity spectra; however, MAE (MAPE) decreased from 0.018 (3.0 %) to 0.0078 (1.2 %) overall when the thickness of the snow layers was also varied. When all three parameters were varied, simulations were similarly sensitive to both correlation length and density, although the influence of density was most evident when comparing spectra from snowpacks with and without surface snow. Simulations were most sensitive to surface snow and wind slab parameters, while sensitivity to depth hoar depended on the thickness and scattering strength of the layers above, demonstrating the importance of representing all three parameters for multi-layer snowpacks when modelling emissivity spectra. This work demonstrates the ability of SMRT to simulate snow emissivity at these frequencies and is a key step in the progress towards modelling emissivity for data assimilation in NWP.

Evaluation and Application of SMRT Model for L-Band Brightness Temperature Simulation in Arctic Sea Ice

Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters.

A novel global snow depth retrieval method considering snow metamorphism and forest influence

Accurate snow depth (SD) information at global scales is highly important for validation and calibration of hydrological model, snowmelt runoff prediction, and global water supplies estimation. Passive microwave remote sensing has been proven to be an efficient method to detect SD at regional or global scales. However, numerous heterogeneous snow property parameters and forest parameters affect the microwave radiation characteristics, which is a great challenge for SD retrieval. In this study, a novel SD retrieval algorithm that comprehensively considered the multiple sensitive factors was developed at global scales by coupling the Microwave Emission Model of Layered Snowpacks (MEMLS) model and machine learning (ML) technique. The exponential correlation length (Pec), snow density (ρ), and forest cover fraction (fforest) are three sensitive parameters for retrieving SD from passive microwave observations based on the extended Fourier amplitude sensitivity test (EFAST) algorithm. The key parameter Pec, which characterizes the snow microstructure information inside snowpacks, was optimized by minimizing the brightness temperature between satellite observations and microwave radiative transfer model simulations by integrating the MEMLS, forest impact, and atmospheric radiation attenuation. The random forest (RF) ML technique was used, and the optimal combination of independent variables was selected by correlation heatmap and importance ranking of variables. Six selected essential parameters, including Pec, vertical polarized brightness temperature differences between 18.7 and 36.5 GHz, ρ from ERA5, fforest, elevation, and longitude together with target SD output were used to train the RF model. Seventy percent of the entire data at meteorological stations were randomly chosen to train the RF model, and the remaining 30% were adopted as the validation data from 2013 to 2020 to ensure the mutual independence of training data and validation data. Compared with independent in situ measurements, the overall RMSE of SD is 13.6 cm, which was noticeably lower than those of the international SD remote sensing products and reanalysis datasets, including AMSR2 (36.2 cm), GlobSnow 3.0 (25.3 cm), ERA5 (38.2 cm), and ERA5-land (29.9 cm). The results demonstrated that the new proposed algorithm, by coupling the MEMLS model with the ML technique, could significantly enhance the overall performance in SD retrieval at global scales.

Simulation analysis of microwave emission from lunar subsurface for SAR radiometric mode dual frequency (L/S Bands) observations onboard Chandrayaan-2

Dual Frequency Synthetic Aperture Radar (DFSAR) (at L and S bands) onboard Chandrayaan-2 (CH-2) has an experimental radiometric mode operation. This paper evaluates the utility of such experimental observations of lunar studies for the future missions. Owing the deeper penetration of L and S bands frequencies, a multilayered microwave radiative transfer model coupled with thermal model has been used to account for the microwave thermal emission or the brightness temperature (TB) from the lunar subsurface at 1.25 GHz and 2.5 GHz frequency. As the dielectric property of lunar regolith is the primary deciding factor of depth of penetration of microwaves into lunar surface, the TB simulated using different dielectric models are compared with the observed TBs of Chang′E-2/Microwave Radiometer (MRM) and selected the dielectric model which consider both frequency and metallic content (Montopoli et al. (2011); Feng et al. (2020); Siegler et al. (2020)) in this study. DFSAR/CH-2 operating frequencies such as 1.25 GHz and 2.5 GHz can be used for sounding dielectric discontinuity due to the presence of water ice, high minerals or densely packed rocky medium than thermal sounding as microwave thermal emissions at these channels are not sensitive to surface temperature, as it is invariant with time for depth below 50 cm. Hence these frequencies are potential tool to study the sub-surface characteristics such as determination of lunar regolith thickness and for the discrimination of subsurface material type.

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Abstract. The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining the latter two techniques, while relaxing some assumptions on, e.g., beam alignment and non-turbulent atmosphere. The method relies on a two-step deep-learning framework inspired from data compression techniques: an encoder model maps a high-dimensional signal to a low-dimensional latent space, while the decoder reconstructs the original signal from this latent space. Here, Doppler spectrograms at two frequencies constitute the high-dimensional input, while the latent features are constrained to represent the snowfall properties of interest. The decoder network is first trained to emulate Doppler spectra from a set of microphysical variables, using simulations from the Passive and Active Microwave radiative TRAnsfer model (PAMTRA) as training data. In a second step, the encoder network learns the inverse mapping, from real measured dual-frequency spectrograms to the microphysical latent space; in doing so, it leverages with a convolutional structure the spatial consistency of the measurements to mitigate the ill-posedness of the problem. The method was implemented on X- and W-band data from the ICE GENESIS campaign that took place in the Swiss Jura Mountains in January 2021. An in-depth assessment of the retrieval accuracy was performed through comparisons with colocated aircraft in situ measurements collected during three precipitation events. The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets. A discussion of the sensitivity and limitations of the method is also conducted. The main contribution of this work is, on the one hand, the theoretical framework itself, which can be applied to other remote-sensing retrieval applications and is thus possibly of interest to a broad audience across atmospheric sciences. On the other hand, the seven retrieved microphysical descriptors provide relevant insights into snowfall processes.

A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR

ABSTRACT Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R 2 = 0.70) and a relatively close agreement with pin profiler (R 2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas.

Forward modelling of synthetic aperture radar backscatter from lake ice over Canadian Subarctic Lakes

Lake ice provides important social and economic services to local communities, in addition to being a sensitive indicator of climate change. The reduction of ground observations of freshwater ice has led to an increased reliance on the use of satellite remote sensing data. There is currently interest in the retrieval of lake ice properties (e.g., ice thickness, bubble radius, roughness) using synthetic aperture radar (SAR). Roughness at the ice-water interface is particularly important as it has been identified as the dominant mechanism for increasing SAR backscatter throughout the ice season and must be considered in numerical radiative transfer models. Therefore, this study determines optimal ice-water interface roughness height for two subarctic lakes in northern Canada and models backscatter throughout two ice seasons using the snow microwave radiative transfer (SMRT) model. The two lakes for this study are Noell Lake and Malcolm Ramsay Lake. Field observations of ice thickness, snow depth, snow density, and the Canadian Lake Ice Model (CLIMo) are used to parameterize SMRT. Modelled L, C, and X-band backscatter at different incidence angles is assessed using SAR imagery from multiple satellite missions. Root mean square errors ranged from 0.38 to 1.45 dB for Noell Lake and 0.70 to 2.33 dB for Malcolm Ramsay Lake. Discrepancies between modelled and observed backscatter were found to be connected to the representation of roughness at different interfaces within the ice column and changes that occurred during freeze-melt events. These results provide insight into how changes in ice properties impact backscatter throughout the ice season. SMRT is valuable for modelling backscatter from lake ice during the cold season and could be used to develop retrieval algorithms for estimating ice-water interface roughness. This would allow for the development of other inversion models for retrieval of surface ice conditions and ice thickness.

The sensitivity of satellite microwave observations to liquid water in the Antarctic snowpack

Abstract. Surface melting on the Antarctic Ice Sheet has been monitored by satellite microwave radiometry for over 40 years. Despite this long perspective, our understanding of the microwave emission from wet snow is still limited, preventing the full exploitation of these observations to study supraglacial hydrology. Using the Snow Microwave Radiative Transfer (SMRT) model, this study investigates the sensitivity of microwave brightness temperature to snow liquid water content at frequencies from 1.4 to 37 GHz. We first determine the snowpack properties for eight selected coastal sites by retrieving profiles of density, grain size and ice layers from microwave observations when the snowpack is dry during wintertime. Second, a series of brightness temperature simulations is run with added water. The results show that (i) a small quantity of liquid water (≈0.5 kg m−2) can be detected, but the actual quantity cannot be retrieved out of the full range of possible water quantities; (ii) the detection of a buried wet layer is possible up to a maximum depth of 1 to 6 m depending on the frequency (6–37 GHz) and on the snow properties (grain size, density) at each site; (iii) surface ponds and water-saturated areas may prevent melt detection, but the current coverage of these waterbodies in the large satellite field of view is presently too small in Antarctica to have noticeable effects; and (iv) at 1.4 GHz, while the simulations are less reliable, we found a weaker sensitivity to liquid water and the maximal depth of detection is relatively shallow (<10 m) compared to the typical radiation penetration depth in dry firn (≈1000 m) at this low frequency. These numerical results pave the way for the development of improved multi-frequency algorithms to detect melt intensity and the depth of liquid water below the surface in the Antarctic snowpack.

Optimal Channel Selection of Spaceborne Microwave Radiometer for Surface Pressure Retrieval over Oceans

Abstract The O2-band channel configuration of existing microwave radiometers is not optimal for surface pressure retrieval, which limits the surface pressure retrieval accuracy. In this study, we present the results of theoretically what might be the optimal microwave channels for surface pressure retrieval. An improved iterative selection method is used to select the channels that contain the highest cumulative content of surface pressure information. The selected optimal channel set comprises 16 channels, among which 10 channels are centered at the 50–60 GHz oxygen absorption band and 6 channels are centered around the 118.75 GHz oxygen absorption line. Two representative spaceborne microwave radiometers are used for comparisons, the Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite and the Microwave Humidity and Temperature Sounder (MWHTS) on board the Chinese Fengyun-3C (FY-3C) satellite. The results of information content analysis show that the optimal channel set contains more surface pressure information than that of the combination of SNPP/ATMS and FY-3C/MWHTS (SNPP/ATMS+FY-3C/MWHTS) channels. A representative dataset from the ERA5 data is input into the plane-parallel Microwave Radiative Transfer model to obtain the simulated brightness temperature observations of the selected optimal channels and the SNPP/ATMS+FY-3C/MWHTS channels. Using the simulated observations, retrieval experiments are performed. Experimental results show that retrieval accuracies of the optimal channel set are 1.09 and 1.64 hPa for clear-sky and cloudy conditions, respectively. The retrieval accuracies are 0.60 and 0.65 hPa better than that of the SNPP/ATMS+FY-3C/MWHTS channels for clear-sky and cloudy conditions, respectively.

A Calibrated Lunar Microwave Radiative Transfer Model Based on Satellite Observations

As a potential external calibration reference for spaceborne microwave sounding instruments, accurate and reliable information of lunar disk-averaged radiance at millimeter band are important and fundamental. Based on study for 2-D lunar scans of the Advanced Technology Microwave Sounder (ATMS) on board the NOAA-20 satellite, the lunar radiance spectrum from 23 to 183 GHz at full moon phase has been reported in our previous work. In this study, the performance of a lunar microwave radiative transfer model (RTM) developed by Keihm was investigated (cited as Keihm model in this paper) . By taking the ATMS observations as the reference truth, the surface emissivity in the lunar RTM can be calibrated. The calibrated RTM model was then evaluated by independent satellite observation data sets from AMSU (Advanced Microwave Sounding Unit) and MHS (Microwave Humidity Sounder) instruments on several NOAA satellites. Results show that with the calibrated model, significant improvement can be made to reduce the uncertainties in the lunar microwave RTM simulations at millimeter wavelengths.

Microwave radiometry experiment for snow in Altay, China: time series of in situ data for electromagnetic and physical features of snowpack

Abstract. In this paper, we present a comprehensive experiment, namely, an Integrated Microwave Radiometry Campaign for snow (IMCS), in Xinjiang, China, during the snow season of 2015–2016. The campaign hosted a dual-polarized microwave radiometer operating at L, K, and Ka bands to provide minutely passive microwave observations of snow cover at a fixed site, along with daily manual snow pit observations of snow physical parameters, automatic observations of 10 min four-component radiation and layered snow temperatures, and meteorological observations of hourly weather data and soil data. To the best of our knowledge, our dataset is unique in providing continuous daily snow pit data and coincident microwave brightness temperatures, radiation and meteorological data, at a fixed site over a full season, which can be straightforwardly used for evaluation and development of microwave radiative transfer models and snow process models, along with land surface process and hydrology models. The consolidated data are available at (https://doi.org/10.11888/Snow.tpdc.270886) (Dai, 2020).

Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling

Satellite microwave observations from 1.4 to 36 GHz already showed sensitivity to several geophysical parameters of sea ice such as Sea Ice Concentration (SIC), Sea Ice Thickness (SIT) or snow depth. The main goal of this article is to provide a realistic and comprehensive characterization of the sea ice and its snow cover that explains the microwave observations during a whole year using a radiative transfer model. For this purpose, we construct a unique dataset of passive microwave observations, to mimic the future Copernicus Imaging Microwave Radiometer (CIMR), along with the active microwave scatterometer data (ASCAT). CIMR database is used to classify sea ice microwave signatures in their spectral dimension with a machine learning technique while ASCAT data are used to help interpret the results of the classification. Classification results are then interpreted with a state-of-art sea ice and Snow Microwave Radiative Transfer model (SMRT) for all highlighted signatures and all seasons. Results make it possible to identify the specific behaviors from the observation co-variabilities for SIC, SIT, and snow structure. Our analysis underlined the role of the depth hoar over multi-year ice, for the interpretation of scattering signals in winter. Scattering signals that appear in late summer are explained by the presence of superimposed ice. This characterization will benefit from future advances in SMRT development, as well as the improved observations of future satellite missions.

Intercalibration of FY-3C MWRI Over Forest Warm-Scenes Based on Microwave Radiative Transfer Model

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.

Investigating the Effect of Lake Ice Properties on Multifrequency Backscatter Using the Snow Microwave Radiative Transfer Model

Recent investigations using polarimetric decomposition and numerical models have helped to improve understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer models provide one method of improving this understanding. These are the first published experiments using the Snow Microwave Radiative Transfer (SMRT) model to investigate the response of different imaging SAR frequencies (L, C, and X-band) at HH and VV polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice-water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. Analysis of the backscatter response to different properties indicate that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited for other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing RMS height at the ice-water interface, which supports surface scattering at the ice-water interface as being the dominant scattering mechanism. These results demonstrate that SMRT is a valuable tool for understanding the response of SAR data to changes in freshwater lake ice properties and could be used in the development of inversion models.

Explanations for Unusual Seasonal Variations in Chang’E-2 Microwave Radiometer Datasets of Lunar Double-Shaded Permanently Shadowed Regions

Near-surface temperatures of permanently shadowed regions (PSRs) on the Moon provide fundamental information for water ice exploration. Seasonal temperature variations of PSRs are found in both Chang’E-2 microwave radiometer data and Diviner Lunar radiometer observations. Furthermore, unusual microwave brightness temperature variations between February 2011 and May 2011 of double-shaded PSRs are shown in the Chang’E-2 observational data, i.e., that the minimum microwave brightness temperature occurs before the time when the infrared brightness temperature reaches the minimum in double-shaded PSRs. To interpret this phenomenon, the 1-D thermal model and the microwave radiation transfer model are used. In the thermal model, the reradiation energy from the illuminated area is estimated by effective solar irradiance, which is an analytic solution for the radiative equilibrium temperature in the shadowed area of a spherical bowl-shaped crater. In the simulation, an assumed internal 0.4 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> heat flow beneath the lunar surface made a plausible fit to the unusual variations during some lunations. However, this is a huge value compared with the well-known heat flow value of about 0.018 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Furthermore, it is difficult to obtain this extra heat energy by lateral conduction below the surface in a large impact crater due to the small thermal conductivity of the lunar regolith. Finally, the unusual microwave brightness temperature (TB) changes are concluded to be caused by a calibration problem after excluding other possible reasons. In addition, a statistical correction method is applied to revise the problematic TB data to obtain the proper variation trend of the brightness temperature.

X-Ray Tomography-Based Microstructure Representation in the Snow Microwave Radiative Transfer Model

The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner–Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.

Assessing Interactions Between Crop Biophysical Parameters and X-Band Backscattering Using Empirical Data and Model Sensitivity Analysis

Active microwave remote sensing data at different frequencies can provide crucial information on crop morphology and conditions, thus effectively supporting agronomic management at different scales. Despite the ever-increasing availability of spaceborne platforms and the extensive research developed throughout more than two decades, some knowledge gaps still await to be filled toward operational use, dealing with SAR backscatter response to crop-specific features and seasonal dynamics, including the effects of agronomic practices. In this work, we used variance-based global sensitivity analysis (GSA) as a quantitative framework for investigating the sensitivity of X-band backscattering to agronomic and morphological features typical of two different crops maize and rice. To this end, we jointly exploited empirical data on crop status and growth, high-resolution TerraSAR-X (TSX) data, and microwave radiative transfer model (RTM) simulations. Phenology-informed simulations allowed us to quantify the contributions of different scattering mechanisms for the two crops under varying observation setups, to assess the sensitivity of X-band backscattering to morphostructural crop biophysical parameters (BPs) (and their interactions), and to evaluate the effects of crop biomass on backscatter across growth stages. In particular, multidimensional GSA outputs accounting for model input correlations through Shapley effects provided a comprehensive suite of information on the relative proportion of total backscatter variance explained by a range of parameters and a quantitative description of the different behavior in vertical and horizontal polarization (changing throughout plant growth) in paddy rice, and the mixed contribution of canopy density and leaf angle distribution (depending on the incident angle) in maize.

Considering Various Multimoment Bulk Microphysics Schemes for Simulation of Passive Microwave Radiative Signatures

Passive microwave radiative transfer models are strongly influenced by the cloud and precipitation hydrometeor properties. Particularly, they can sensitively interact with frozen hydrometeors through multiple high-frequency channels. However, frozen hydrometeors are one of the most difficult parameters to comprehend due to the lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data. Until recently, studies have attempted to describe more reasonable hydrometeor distributions using various microphysics parameterizations coupled with the weather research and forecasting (WRF) models. Herein, we aim to apply the proposed methods to passive microwave radiative transfer simulations. We implemented a passive microwave radiative transfer simulation that considers various microphysical assumptions by creating a new Mie scattering lookup table. Furthermore, we evaluated the bulk microphysics parameterizations [WDM6, Morrison (MORR), Thompson (THOM), and P3 schemes] for the tropical cyclone Krosa (2019) that were observed by the global precipitation measurement microwave imager instrument, specifically concentrating on the rimed and aggregated ice categories (snow, graupel, and P3 ice). Based on the evaluation results, we concluded the following: WDM6 graupel and MORR snow afford excessive scattering signals at 37 GHz. However, at 166 GHz, none of the parameterizations produces sufficient scattering signals for comparison with the observations. The P3 ice affords significantly underestimated scattering signals at 89 GHz and above, despite its sophisticated assumptions. On the contrary, THOM snow affords scattering signals similar to the observations, despite a shape-related error. In summary, this study introduced a method for implementing a microphysical-consistent radiative transfer computation and successfully showed how various microphysical assumptions of clouds can change the passive microwave radiative signatures.