The depolarization ratio (PR), defined as the ratio of C-band HV to VV backscattering coefficients, is sensitive to snow depth, making it a useful metric for estimating large-scale snow depth distribution using Sentinel-1 data. However, its application in certain regions has led to significant errors, indicating an incomplete understanding of the underlying physical mechanisms. In an experiment conducted in the Altay Mountains, China, we observed markedly different depolarization ratios in adjacent areas with similar snow depths, where the underlying surface conditions differed. Directly using the depolarization ratio for snow depth retrieval in such cases leads to significantly different estimates, which are not supported by in situ observations. In this study, we demonstrate that this inconsistency is likely caused by the generation of cross-polarization backscattering due to the coupling of bistatic rough surface scattering and snow volume scattering. In particular, a rough surface may produce high cross-polarization scattering under certain incident and scattering angles, which interacts with the snow volume through bistatic scattering. An iterative solution to the vector radiative transfer equation is used to properly account for the coupling between polarimetric bistatic rough surface scattering and snow volume scattering. The simulation results show that after this improvement, the depolarization ratio under rough surface conditions increased, and its potential to match real-world observations was significantly enhanced. It is further demonstrated that simply subtracting the depolarization ratio under snow-free conditions cannot fully cancel the influence of the rough surface, as the remaining depolarization ratio still contains terms related to snow-surface interaction. Therefore, further correction of the rough surface effect is necessary to improve the accuracy of C-band depolarization ratio-based snow depth retrieval.

Snow density is the key parameter in converting snow depth to snow mass. A two-parameter retrieval algorithm has been developed to estimate snow density and soil permittivity simultaneously in ground-based experiments. This study tested the two-parameter retrieval algorithm applied for the L-band multiple-angle SMOS (Soil Moisture Ocean Salinity) and single-angle SMAP (Soil Moisture Active Passive) missions, respectively, at 46 sites in Quebec, Canada. To alleviate the ill-posed problem, we also developed a one-parameter retrieval algorithm, where only the snow density was retrieved using a soil permittivity calculated from the GLDAS-Noah soil simulations. Results showed that, the two-parameter retrieval algorithm achieved an ubRMSE of 40 and 60 kg⁄m3for SMOS and SMAP, respectively, but the correlation is low (0.23-0.24), because the sensitivity of observations to snow density is weaker than soil parameters, and the estimates from coarse-resolution satellite observations were validated against point-scale measurements. On the contrary, if soil permittivity is determined despite a small bias, the one-parameter retrieval algorithm based on SMOS can increase the correlation coefficient to 0.65 in the October to June period. It indicates the importance of a soil permittivity prior or a stable snow condition (for example, frozen soil condition) to achieve high accuracy for satellite-based snow density estimation.

This paper describes the Altay 2024 airborne field campaign in support of snow observation retrieved from spaceborne InSAR measurements from the Chinese LuTan-1 (a spaceborne L-band SAR constellation launched in 2022). The airborne and field measurements that are synchronized with LuTan-1 InSAR acquisitions will be conducted in January-February 2024 (snow on) and May-July 2024 (snow off). The remote sensing and in-situ measurements include various in-situ observations and drone-based lidar measurements. We first provide the overview of the Altay 2024 campaign including the choice of the in-situ measurement locations and flight tracks of the drone-based lidar. Then, historical InSAR dataset from all the available L/C-band SAR’s (e.g. JAXA’s ALOS, ESA’s Sentinel-1, China’s LuTan-1) over the study area are used to generate SWE change products, which are further compared against the in-situ measurements when available. This synergetic spaceborne airborne field campaign will directly validate the LuTan-1 derived snow products using the acquired airborne and field dataset, which can also support the design of future spaceborne mission concepts for snow retrieval.

An accurate microwave emissions model is essential for the simulating satellite brightness temperature (TB) and developing snow depth retrieval algorithm. The dense media radiative transfer theory based on quasicrystalline approximation (DMRT-QCA) model and the DMRT with bicontinuous (DMRT-Bic) model are currently recognized as representative and highly accurate snow emission models. Meanwhile, the latest development of DMRT-Bic-NN (DMRT-Bic-neural network) model has improved computational efficiency based on DMRT-Bic model. In order to evaluate the performance of the above models in the simulation of brightness temperature, this study compares and validates the capabilities of DMRT-Bic-NN model and DMRT-QCA model to simulate TB at 18.7 GHz and 36.5 GHz with the same input parameters. The results show that the correlation coefficient (R) between the TB simulations of DMRT-Bic-NN and DMRT-QCA is higher at 18.7 GHz than at 36.5 GHz. Moreover, the validation of TB simulations by ground-based observations in the Altay region shows that the DMRT-Bic-NN model has higher simulation accuracy than DMRT-QCA model, and the R / RMSE between the DMRT-Bic-NN simulations and the ground-based microwave radiometer observations TB is 0.35~0.87, 20.46~18.17K at 18.7 GHz and 36.5 GHz with V polarization, respectively. However, during the snow melting season, DMRT-QCA exhibits higher simulation accuracy than DMRT-Bic-NN at 36.5 GHz. This work can provide important guidance for the snow depth retrieval.

The characterization of snow dynamics in mountainous regions requires high-resolution snow depth (SD) and snow water equivalent (SWE) data from remote sensing techniques. To enhance our comprehension of coarse- resolution passive microwave signals in complex terrains and to maximize their utility in these areas, we developed a new SWE estimation method, leveraging physically-based snow process and snow radiative transfer models. This method utilizes 0.1-degree AMSR2 brightness temperatures and 3-km fractional snow cover (FSC) time series from MODIS to construct an observation equation of nested grids. Ensembles of SWE, snow cover, and brightness temperature (TB) time series are created through perturbed meteorological datasets. Subsequently, ensemble weights are determined and utilized to generate a SWE product in 3-km resolution. This method, resembling adjustment computation theory more than data assimilation techniques, ensures the preservation of water balance within the estimation. It will undergo testing and evaluation in the Qinghai-Tibetan Plateau and Xinjiang province in China, with comparisons to station SD measurements.

Snow water equivalent (SWE) quantitatively describes water storage in snowpack. Satellite-based passive microwave (PMW) remote sensing is a valid tool for monitoring SWE in the Northern Hemisphere. However, the current operational SWE retrieval methods, especially those without assimilating near real-time station snow depth, still utilize globally-constant coefficients to build regression-based retrieval algorithms. In the context of the successful launch of the FY-3F satellites in 2023, we are attempting to work out a better Northern Hemisphere algorithm for the Micro-Wave Radiation Imager-II (FY-3F/MWRI-II), using a set of pixel-sensitive dynamic coefficients regressed based on a spatiotemporally continuous reference SWE dataset. We first utilize a method that couples random forest with the HUT snow emission model to calculate a high-accuracy SWE reference dataset. Then the linear-regression equations are used to fit the reference SWE data and satellite brightness temperature observations at each pixel to build the new operational FY-3F algorithm. Finally, the proposed FY-3F algorithm is validated extensively using four spatially independent datasets. The proposed FY-3F algorithm will improve the global monitoring capabilities for snow cover and enhance a complete and timely understanding of changes in SWE.

Satellite passive microwave (PMW) remote sensing is widely used for monitoring the snow water equivalent (SWE) in the Northern Hemisphere. Existing operational SWE retrieval methods, especially those without assimilating ground-based snow depth priors, still utilize globally constant coefficients to construct regression-based retrieval algorithms. The current Fengyun-3 (FY-3) series of SWE product algorithms has made improvements in China, where biases have been significantly reduced locally but not in other regions. Within the context of the successful launch of the FY-3F satellites in 2023, we developed a better Northern Hemisphere algorithm for the Microwave Radiation Imager-II (FY-3F/MWRI-II) using pixel-sensitive coefficients regressed on a reference SWE dataset. We utilized the random forest model coupled with the snow emission model (HUT-RF) to obtain a high-accuracy SWE reference dataset. Then, we employed linear regression equations to fit the reference HUT-RF dataset at each pixel to construct the new operational FY-3F algorithms. We innovatively introduced the brightness temperature differences between 18.7 and 89 GHz and the polarization differences at 10.65 GHz in the regression after noting their sensitivity in deep snow estimation. The proposed FY-3F algorithm was extensively validated via four spatially independent datasets. The results demonstrated that the proposed FY-3F algorithm performed well in non-mountainous and sparsely forested areas, e.g., the overall unbiased root mean square error (unRMSE) values were 27.15 mm over Russia and 13.70 mm over China. High uncertainties still occurred in complex terrains and densely forested areas, e.g., the overall unRMSE values were 75.30 mm over Canada and 129.06 mm over western North America. The proposed FY-3F algorithm could improve global snow cover monitoring capabilities and enhance the complete and timely understanding of SWE changes.

Accurate simulation of the microwave signatures of snow using the emission models is of guiding significance to develop the snow parameters retrieval algorithm. This study based on reanalysis dataset ERA5-Land and auxiliary data to investigate the potential of the DMRT model combined with the τ –ω model for simulating passive microwave brightness temperature (TB) of snow cover at 10.65 GHz, 18.7 GHz, and 36.5 GHz. The results showed that the correlation coefficient (R) and bias between the simulations and the ground-based microwave radiometer observations at the Altay is 0.45~0.66, 8.21 k~13.3K at V polarization, and 0.44~0.63, 9.32K~14.68K at H polarization, respectively. In addition, the R and bias between the simulations and the AMSR2 and FY-3D TB is 0.61-0.81, 0.58~0.73, and 18.2K~20.75K, 18.25K~19.2K at V polarization, and 0.47~0.65, 0.51~0.69, and 19.79K~28.62K, 20.52K~26.8K at H polarization, respectively. In some forested areas, there is a significant increase in the simulation bias at 36 GHz, which could be attributed to an overestimation of vegetation influence at this frequency.

A physical-statistical framework to estimate Snow Water Equivalent (SWE) and Snow depth (SD) from SAR measurements was implemented and applied to SnowSAR flight-line data collected during the SnowEx’2017 field campaign in Grand Mesa, Colorado, USA and averaged to 90 m resolution. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, SWE and SD and physical model parameters. To reduce the number of physical parameters, the snowpack is represented by two layers only. Retrievals compare well with pit observations with good performance in deep snow and residual errors less than 8% for SnowSAR incidence angles > 30°.

The spatiotemporal distribution of snow depth (SD) has a significant impact on the energy and water balances of the Earth’s system. However, passive microwave remote sensing widely used for SD estimation has large uncertainties due to the variations in snow physical properties. In this study, we demonstrate a new method to minimize these uncertainties and to increase the accuracy of SD estimation. Our method is based on the synergy between the passive microwave AMSR-2 brightness temperature (TB) and a physical snow process model (SNTHERM) to estimate snow grain size, snow density and first-guess SD as priors. On one hand, we used TB from three frequencies and removed non-representative ground measurements at the stations to improve deep snow estimation. Then, we applied a machine learning (ML) algorithm based on both the AMSR-2 TB and the SNTHERM simulations to retrieve the global SD. The results showed that the root-mean-squared error (RMSE) of the retrieved SD was 12.4 cm at the meteorological stations. Independent validations showed that our method significantly reduced the SD and snow water equivalent (SWE) underestimation in the mountains compared to the current satellite products.

Microwave remote sensing is effective in monitoring snow distribution over large areas. The dense media radiative transfer model provides theoretical basis for deriving snow information through its scattering to electromagnetic waves. The microwave emission model of layered snowpacks (MEMLS) and the dense media radiative transfer model with bicontinuous media approximation of snow (DMRT-Bicontinuous media) are two widely adopted models. Both models assume a random media representation of snow. In this paper, the scattering properties of both models are compared and analyzed to provide guidance in model selection. Sensitivity of the bicontinuous media - discrete dipole approximation (DDA) to its numerical parameters is also discussed to improve usability of the model.

Soil freeze-thaw cycles have a profound impact on heat and water fluxes at the land-atmosphere interface and transport in soils. Microwave remote sensing is a widely used technique to detect near-surface soil freeze/thaw states due to significant changes in dielectric properties associated with water phase transitions in soils, where uncertainty remains. This letter proposes a new parameterization scheme for the estimation of unfrozen water content to improve the modeling of soil dielectric properties during freeze-thaw transitions. Predictions from the new model referred to as Zhang-Zhao’s model were compared with dielectric measurements during thawing processes of soil samples collected from Baoding (silty clay soil), Zhangjiakou (loamy sandy soil), and Zhengzhou (clay loam soil) in China. The mean biases of the predictions were 3.25 (4.44 and 2.07 for the thawed value and frozen value, respectively) and 1.54 (2.22 and 0.88 for the thawed value and frozen value, respectively) for the real part and imaginary part, respectively. The model-predicted soil complex relative permittivity (CRP) was highly correlated with measurements, with correlation coefficients ranging from 0.7944 to 0.9865. The normalized root mean square errors of the predictions were 13.72% (real part) and 25.41% (imaginary part).

Snow depth or snow water equivalent in mountainous region is crucial for hydrology, water resources management, meteorological and climate research. Remote sensing can be used for snow depth monitoring in regional scale or global scale. However, the spaceborne remote sensing of snow depth in mountain is challenging because of the sensor sensitivity and spatial resolution problems. Recently, time series Sentinel-1 is used for snow depth retrieval in mountains, which shows encouraging accuracy. In this study, an algorithm to estimate the snow depth in mountainous regions using optical and passive microwave remote sensing observations is proposed, which can be applied in periods before and after the launch of Sentinel-1. The optical and passive microwave remote sensing observations and the Sentinel-1 derived snow depth in 2016-2021 are used to train the snow depth retrieval algorithm using the Extreme gradient boosting (XGBoost) machine learning algorithm. Validations are performed using cross validation method and independentin-situsnow depth data. The cross validation shows correlation coefficient of 0.81 and mean absolute error (MAE) of 0.17m. The correlation coefficient and MAE of predicted snow depth andin-situsnow depth in 2002-2016 are 0.61 and 0.33 m, respectively, which shows significant higher accuracy compared with AMSR-E/AMSR2 snow depth products. The site dependence of the machine learning method is also discussed. The machine learning based snow depth retrieval presented in this study can be applied to mountains globally to the optical and passive microwave remote sensing era.

The snow water equivalent (SWE, a measurement of the amount of water contained in snow packs) is an important variable in earth systems. Microwave remote sensing provides a possible solution for estimating the SWE globally. To support radar SWE retrieval, the snow backscattering theory needs to be studied; the forward simulation model needs to be validated against natural snow observations. In this study, a one-winter experiment to observe the time series backscattering coefficient of snow-covered bare soil is reported. This is the first long time series snow-covered soil backscattering experiment that was measured by an imaging radar. The backscattering coefficient was observed at three frequencies covering the X-band and dual-Ku bands, which are of great interest to the snow remote sensing community and are used for SWE estimation in mountains. The calibration of the synthetic aperture radar (SAR) system was conducted manually and carefully to ensure high-quality radar observation data. The observations from our experiment show that in general, the time series backscattering signature of snow-covered terrain is mainly driven by soil freezing, snow grain size growth, and snow accumulation processes. The time series observations for dry snow are modeled by backscattering models with model inputs directly calculated from field measurements. Our simulation results indicate that the time series radar backscattering at three frequencies and four polarizations can be simulated with high accuracy, including the cross-polarization channels. This study provides some key understanding of the time series signature of radar backscattering from snow and provides some key implications for SWE retrieval from radar observations.

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature (Tb) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne Tbs, the inversion was also applied using the synthetic observations, where Tbs were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne Tbs are used, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne Tbs are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

Tropical monsoon climate in IndoChina Peninsula features dry and rainy season. Microwave remote sensing offers emerging capabilities for hydrological simulation. This paper aims to clarify whether the contributions of remote sensing precipitation on runoff simulation will change due to terrains and model algorithms. We simulated runoff in mountainous area-Yuan River Basin based on remote sensing early version precipitation products by using Soil & Water Assessment Tool (SWAT) model. We also simulated runoff in flat terrain area-Mun-chi River Basin based on remote sensing final version precipitation products by Variable Infiltration Capacity Model (VIC) model. We compared the runoff results against gauge-based CMORPH-AWS and World Meteorological Organization (WMO) interpolated precipitation, and also estimated flood inundation areas in Mun-chi River from 2005 to 2014 based on runoff simulations. The results show that (1) gauge-based precipitation products CMORPH-AWS and WMO precipitation have largest NSE and smallest RMSE for runoff simulation in these two basins. The runoff simulation by VIC model and SWAT model based on TRMM Multi-satellite Preciptiation Analysis (TMPA) remote sensing product have higher correlation with the observations; (2) Runoff simulations based on TMPA can be used for flood inundation area estimation in larger river basin rather than smaller basin or subbasin. Our study reveals that high-quality precipitation products significantly improved runoff simulation accuracy in these two basins. Remote sensing precipitation product TMPA has potential on runoff simulation and flood assessment in remote or observation lacking area in IndoChina Peninsula.

The knowledge and understanding of intra- and inter annual characteristics of canal water is crucial for agricultural water management. The narrow shape of canal greatly limits the application of moderate-resolution remote sensing technologies. Based on newly available Sentinel-2 Multispectral Instrument (MSI) imagery with frequent revisit and higher spatial resolution, we identified variation of water/bank boundary by Roberts, Sobel, Prewitt, Laplacian of Gaussian and Canny 5 edge detectors and furtherly compared the water width results with estimation by ground measurement. The preliminary results show that all detectors can successfully monitor seasonal variation of canal water surface. Canny detector is most stable among 5 methods for time series monitoring, although overestimated the water width during dry period. Our methods and results reveal the great potential of Sentinel-2 imagery for canal water utilization and irrigation management.

In this study, a model investigation of a ground-based active and passive microwave experiment for snow and frozen soil is presented. The experiment is carried out from October 2017 to March 2018 in Xinjiang, China. Ground based SAR and microwave radiometers are used to measure the multiple frequency and multiple polarization backscattering coefficient and brightness temperature of snow covered soil. Microwave scattering and emission model of snow and soil are used to study the measurement results, and the microwave signature of snow and frozen soil are studied by model simulations, and this is the fundamental of snow parameter retrieval from active and passive microwave observations.

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer Helsinki University of Technology (HUT) model and the microwave emission model of layered snowpacks (MEMLS). By comparing the mathematical formulation side by side, three major differences were identified: 1) by assuming that the scattered intensity is mostly (96%) in the forward direction, the HUT model simplifies the radiative transfer equation in 4π space into two one-flux equations, whereas MEMLS uses a two-flux theory; 2) the HUT scattering coefficient is much larger than the one of MEMLS; and 3) MEMLS considers the trapped radiation inside snow due to internal reflection by a six-flux model, which is not included in HUT. Simulation experiments indicate that the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of one-flux simplification and the trapped radiation still result in different TBsimulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankylä, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that the HUT model tends to underestimate TBfor deep snow. MEMLS with the physically based improved Born approximation performed best among the models, with a bias of -1.4 K and a root-mean-square error of 11.0 K.

Snow mapping is of great importance in meteorological, hydrology and global change researches. In the heavy snow event in 2008 in the south of China, the optic remote sensing fails to map snow cover due to the existence of thick clouds, and the traditional passive microwave snow detection algorithm could only be used for dry snow. Therefore, to map snowcover in the south of China where the snowpacks are mostly wet, shallow snow, AMSR-E brightness temperatures from Jan 1stto Feb 20th, 2008 is used to analyze the brightness temperature characteristics in the region of 23-43°N, 102-122°E. Eight land surface types are extracted based on the site-observed snow depth and ground temperature, IMS (Interactive Multi-sensor Snow and Ice Mapping System) and MODIS snowcover. Then, a snow detection decision tree algorithm is established. Comparison of the algorithm-detected snowcover with the IMS product in 2008 and 2011 shows that, the use of 89 GHz channel can improve the snow-detection ability in the southern part of the study region. The performance of the algorithm in the sparse-vegetated region is better than that in the forest-covered region. The total accuracy of the algorithm is about 94%.

Simulation of brightness temperature and related snow parameters is essential to understand the microwave emission property and its evolution with change of the snow soil system status. In this paper, a typical thin snow pack on North China Plain is measured on Nov 13-16th, 2009 at Luancheng test site HUT (Helsinki University of Technology) wet snow emission model is used to predict the brightness temperatures at 10.65, 18.7 and 36.5 GHz. A physically-based snow process model, SNTHERM (SNow THERmal Model), is applied to simulate the snow melting process. The measured snow density and grain size is compared with SNTHERM prediction and HUT inputs. Results show that the application of snow emission model and process model can explain the variation trend of wet snow emission properties well.

The Tibetan Plateau is the highest plateau. And snow in covered at Tibetan Plateau can exert have important influence on the study of climate change and hydrological cycle. In this paper, we found that the brightness temperature of horizontal polarization at Nam Cu Lake is very low, which is about 170K at 18.7 GHz, by the analysis of the time series of the brightness temperature observed by AMSR-E. Even if the lake got frozen, the brightness temperature of horizontal polarization of ice is about 220K at 18.7 GHz, which is much lower than that at land. Then we use HUT (Helsinki University of Technology) snow emission model for a homogeneous snowpack - ice - water system to simulate the brightness temperature at AMSR-E's frequencies which are used in the current algorithms of estimation SWE at the satellite scale. Also from the seasonal variation of the time series of the brightness temperature, we could see that the brightness temperature of lakes increased sharply when being frozen and decreased rapidly when being melting. The HUT model can match well with the observed brightness temperature at 18.7GHz at horizontal channel.

Snow plays an important role in meteorological and hydrological studies, so it makes sense to accurately predict the process of snow and the amount of snow. Exactly modeling snow properties is an important process for the combined snow process model and microwave model to simulate the amount of snow. In this paper, a mass and energy balance computer model-snow thermal model (SNTHERM.89) is used to simulate the snow properties combined with experimental data measured in Binggou basin, an alpine catchment in Gansu province, china during March 11th and April 7th in 2008. SNTHERM can simulate the snow properties well. In an attempt to make sure that the data for the input is with highest degree of confidence when some measurements are missing, sensitivity analysis of snow properties to forcing data was conducted. Through evaluating the sensitivity of SNTHERM to forcing data, a better understanding of the model and prediction can be obtained.

As the global warming intensifies, the environment changes in cold land receive more attention. In this paper, a combined microwave emission model is established for cold land researches. Through field observation experiment, the b-factor of winter wheat during winter is obtained to simulate radiation accurately from this typical ground object in China. Furthermore, the impacts of snow and vegetation cover on frozen soil radiation are investigated by sensitivity analysis.