Segmenting unknown domains using a model trained in the source domain still faces challenges. Although some approaches tried to resolve the problem through various data generation and network architecture designs, they cannot achieve satisfactory segmentation results compared with single domain segmentation of consistent data distribution. Therefore, we propose a data augmentation method based on amplitude perturbation to expand the distribution of data types, thereby covering target data. A feature suppression strategy is proposed to reduce the network's over-reliance on important features of the source domain data to improve generalization performance. In addition, we design a luminance contrast consistency (LCC) learning module to harmonize the data styles between different domains and a multiscale convolutional attention (MSCA) module to enhance the network's perception of small target objects and improve the segmentation performance of the model, which further improves segmentation performance. Our method achieves the state-of-the-art (SOTA) results on two public datasets of ATLAS2.0 and Prostate. The code is available at https://github.com/butterflyGN/DGSFTAFS.

Snow cover dynamics in the High Mountain Asia (HMA) region serve as key indicators of global climate change. Although Fractional Snow Cover (FSC) products derived from MODIS data have advanced significantly, their relatively short temporal span limits long-term snow trend analyses. In contrast, the Advanced Very High Resolution Radiometer (AVHRR) offers the longest continuous record of optical remote sensing data, presenting a unique opportunity to capture interannual variability in snow cover across decades. However, given the relatively limited observational capability of AVHRR, conventional physically based methods face considerable challenges in achieving accurate retrievals. To address this, we propose a two-stage, data-driven FSC retrieval approach for the HMA region, employing Random Forest (RF) and XGBoost models separately in the regression stage and a RF in the classification stage to correct the results. Based on this approach, two FSC products (AVHRR FSC L1) spanning 1984–2018 were developed, incorporating cloud indicators. FSC L1 datasets were validated with Landsat 8 FSC and in situ snow depth, and benchmarked against the SnowCCI AVHRR products from the European Space Agency (ESA FSC). The results demonstrate clear improvements: the FSC L1 datasets achieve RMSE values of 0.079 (XGBoost) and 0.082 (RF), with corresponding R² values of 0.955 and 0.953, respectively, outperforming the ESA FSC (RMSE 0.097, R² 0.934). These models demonstrate superior accuracy in forested and topographically complex areas, as well as improved consistency in spatial snow distribution. To further enhance data usability, we introduce a multi-step spatiotemporal gap-filling algorithm and produce a cloud-free FSC product (AVHRR FSC L2). Its performance shows RMSE 0.084 (high cloud cover) and 0.154 (medium–low cloud cover), with R² 0.862 and 0.919. A key innovation of this study lies in the automatic construction of an extensive, high-quality training dataset on the Google Earth Engine (GEE) cloud platform, enabling the first AVHRR-based, long-term, high-accuracy FSC dataset specifically tailored for the HMA region. The resulting FSC products are expected to support a broad range of applications in cryospheric research, climate change monitoring, hydrological modeling, and regional water resource management.

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.

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.

Melt ponds frequently form on sea ice in the Arctic during the summer. Their albedo, much lower than ice and snow, amplifies the absorption of shortwave solar radiation in the Arctic. Consequently, precise estimation of melt pond depth (MPD) and meltwater volume is crucial for investigating Earth's energy balance and understanding climate change. In this study, a radiative transfer model is used to simulate the reflectance of sea ice and melt ponds. Subsequently, employing the dynamic pixel unmixing method, Sentinel-2 satellite imagery is used to estimate the depth and cover fraction of melt ponds, and then the volume of the water in melt ponds (MPV) can be calculated. Validation results indicate that compared to IceBridge and ICESat-2 data, Sentinel-2 estimates a melt pond fraction with an RMSE of approximately 10.2% and a melt pond depth with an RMSE of around 27.7 cm.

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.

The Arctic region is sensitive to climate change, and polar sea ice is a crucial indicator of global climate change. Microwave radiometry has been applied to retrieve Arctic sea ice concentration (SIC) for over 50 years. During summer, the retrieval algorithm for SIC based on microwave brightness temperature is affected by the melt ponds or wet sea ice, which may cause bias in the estimated SIC. In this study, a machine learning (ML) model is constructed using special sensor microwave imager (SSM/I)-special sensor microwave imager/sounder (SSMIS) brightness temperature data as input variables and the 2001–2020 moderate resolution imaging spectroradiometer (MODIS) SIC as a reference dataset to retrieve SIC, followed by a validation analysis using Landsat SIC, ship-based visual observations of SIC, and MODIS SIC. The comparison results show that the precision of SIC retrieved by the ML model is higher and effectively improves the bias of SIC by using microwave radiometry in summer conditions. The results show that, whether in the entire Arctic or localized regions with intense melt ponds, the ML-based SIC is superior to the four canonical microwave SIC products [Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI), Ocean and Sea Ice Satellite Application Facility (OSI), National Aeronautics and Space Administration (NASA) Team (NT), and Bootstrap (BT)]. Based on the Arctic sea ice dataset obtained from 1988 to 2020 in this study, the spatiotemporal trends of Arctic SIC are analyzed. The results indicate a significant declining trend of SIC in the Arctic, which agrees with classic SIC products. The most pronounced ice reduction is observed in the Barents Sea, Chukchi Sea, East Siberian Sea, Kara Sea, Laptev Sea, and Beaufort Sea.

The thermal infrared (TIR) emissivity and physical temperature of snow together determine the thermal radiation of snow. The modeling of snow and ice TIR emissivity is important for climate models and remote sensing. Previous snow and ice TIR emissivity models fail in predicting the sensitivity of emissivity to snow type and snow microstructure, which was measured in experiments. Empirical models were proposed to simulate such sensitivity but not in a unified theoretical framework. In this study, we propose a snow and ice TIR emissivity model based on photon tracking by assuming that the geometric optics approximation is still valid in TIR spectral region. It is proved that the proposed model can predict both the TIR emissivity’s sensitivity to grain size for small grain sizes and the TIR emissivity’s sensitivity to snow density. These features can fully explain the experiment observed features. Moreover, the proposed model simulates snow and ice TIR emissivity in a unified theoretical framework. We also explain that the observed emissivity’s sensitivity to snow type is actually caused by the sensitivity to snow density, not grain size. This proposed model can be further used in climate models and remote sensing.

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.

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 normalized difference snow index (NDSI) plays an important role in mapping snow cover with spaceborne visible and shortwave-infrared imagery. The NDSI variation depends on illuminating-viewing geometry and snow physical properties, including equivalent grain size (EGS), snow depth (SD) and impurity concentration, as well as fractional snow cover (FSC) within a mixed pixel; however, it is still not fully understood. To quantifiably characterize the pattern of snow NDSI variation, we use a light scattering model of snow to calculate bidirectional reflectance and consequent NDSI values for a wide range of illuminating-viewing geometries, SD, and EGS values. In the model designated bicontinuous snow model using Geometric Optics theory and Radiative Transfer Equation (bicontinuous-GO/RTE), snowpack is represented by a bicontinuous microstructure, and bidirectional reflectance is simulated based on geometric optics and vector radiative transfer equation. The discrete ordinates radiative transfer (DISORT) algorithm is used to simulate the soot concentration effect on snow NDSI. A soil spectral reflectance model (SOILSPECT) is utilized with the assumption of the linear spectral mixture of snow and soil to quantify the effect of FSC on NDSI. As for discontinuous forests, an analytical hybrid geometric-optical and radiative transfer (GORT) model in conjunction with the bicontinuous-GO/RTE model and the PROpriétésSPECTrales (PROSPECT) model is used to examine the effect of canopy cover, which is related to the maximal FSC viewable to satellites. Modeling results indicate that: 1) snow NDSI is comparably low at off-nadir viewing angles, and this effect is exacerbated by the decline in solar elevation but limited by large EGS; 2) snow NDSI increases with EGS yet becomes saturated at EGS of 500$\mu \text{m}$; 3) the effect of SD that is as low as 1 cm on NDSI is rarely noticeable; 4) the concentration of internally mixed soot up to 1 ppm has little reducing effect on snow NDSI ($> 20^{\circ }$in forests; and 8) forests complicate the nonlinear relationship between NDSI and canopy cover with fully snow-covered ground beneath canopies. These findings imply important uncertainty sources of binary and FSC mapping with NDSI.

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.

Lake ice phenology is regarded as a good proxy for the past and present climates. Long time series passive microwave radiometry data are used to estimate lake ice phenology variations in the Qinghai–Tibet Plateau (QTP), and a contrasting pattern of phenology change trend is found that the time series trend of lake ice freeze-up or break-up time is obviously reversed for lakes in the QTP. The reason for this contrasting trend of lake ice phenology is discussed based on factors such as salinity, water volume change, and air temperature change. Lake ice phenology data are separated based on lake salinity for the climate study: lake ice phenology of lakes with low salinity can be used as air temperature and climate change indicator, whereas lake ice phenology of lakes with high salinity and a low water volume can be used as an indicator of water volume variation under climate change. Correlation analysis of air temperature and the lake ice phenology show that air temperature is the main driving factor behind lake ice phenology variations. The lake ice phenology results suggest overall rising air temperatures during the period 1987–2017 in all regions of the QTP.

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.

Water Cycle Observation Mission (WCOM) is a mission dedicated to synergetic observations of global water cycle parameters, with emphasis on soil moisture, ocean surface salinity, snow water equivalent and frozen/thaw. WCOM implements its observation requirements by measurement of microwave emission/scattering of both the frequencies sensitive to the key parameters and also the auxiliary frequencies providing necessary atmospheric and surface roughness corrections. In order to satisfy this measurement requirements, WCOM is equipped with payloads with combination of active and passive microwave sounding capabilities of frequency from L-band to W-band. Dual Frequency Polarized Scatterometer (DFPSCAT) is one of the three payloads onboard the satellite of Water Cycle Observation Mission (WCOM). DFPSCAT is an X/Ku band rotating pencil beam scatterometer with 2-5 km resolution and 1000km swath for mapping of snow water equivalent (SWE) and freeze-thaw process. DFPSCAT achieves fine resolution by linear frequency modulation pulse compression along the elevation direction, and by unfocused synthetic aperture processing (a technique where the Doppler effect is exploited to synthesize a longer aperture to achieve an improved resolution), as well as super-resolution reconstruction by oversampling in the direction of the azimuth. Based on the payloads of WCOM mission, especially with X/Ku scatterometer and L/Ku/Ka radiometer active/passive observations, there are obvious advantages in snow water equivalent retrieval. The atmospheric correction of active and passive data should be performed before the retrieval. The estimation of SWE mainly rely on the high resolution X and Ku band scatterometer, and combined active/passive retrieval can provide more reliable SWE product. The retrieval method of SWE from X/Ku scatterometer is described, and combined active/passive retrieval is also briefly described.

In hydrological investigation, the liquid water content in snow pack is required which is important for modeling and forecasting snow melt runoff. Active microwave remote sensing has the potential of estimating snow parameters. In this study, we estimates snow wetness based on quasi-crystalline approximation - dense media radiative transfer (QCA-DMRT) model at X (10.2 GHz) and Ku (16.7 GHz) bands and at dual-polarization (VV and VH). At first, snow volume backscattering and air-snow surface backscattering were decomposed from wet snow backscattering by analyzing X-band and Ku-band radar wet snow database generated from QCA-DMRT model. The database covers the most possible wet snow and air-snow surface physical properties conditions. Then using the surface scattering component to estimation snow wetness based on the relationship between the surface scattering and snow wetness.

In this paper, we study the effect of layered snow in alpine regions on microwave emission at Ku and Ka bands, using both experimental data and model simulations. A recent implementation of the multi-layer dense-medium radiative transfer model (DMRT) under the quasi-crystalline approximation (ML-QCA) was used to account for the effects of snow layers on the emission from dry snow covers. Model simulation have been compared with radiometric measurements, collected with ground based instruments during several long-term experiment carried out over three winter seasons between 2007 and 2011 in the Eastern part of Italian Alps. This comparison has the twofold purpose of validating the model and interpreting some particular aspects of snow microwave emission. The measured brightness temperatures at Ku and Ka bands were compared with those simulated through the ML-QCA model, by using the observed snow parameters as inputs. A direct comparison of measured and simulated data showed that the slope of correlation ranged between 0.7 and 1.0, with determination coefficients between 0.51 and 0.75 and Root Mean Square Error (RMSE) between 11 K and 15 K.

Land surface longwave (LW) radiation (or longwave radiative flux), including longwave upwelling (LWUR), downward (LWDR) and net radiation (LWNR), are key components of the total energy that drives the surface energy balance at the interface between the earth's surface and the atmosphere. The importance of LW radiation in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a tremendous attempts have been made to derive LW radiation from space only valid under clear-sky conditions leading to difficulty of utility of remote sensing-based LW radiation products in most land models due to their spatial discontinuity. Although few studies focused on LW radiation estimation under cloudy-sky conditions, while their global application are still problematic. In this paper, novel strategies are proposed aiming to derive high resolution cloudy-sky LWDR and LWUR by fusing collocated optical and microwave satellite data. The results reveal that the new approaches work rather well, thus, more importantly, providing unprecedented possibilities for generating high resolution global LW radiation datasets.

Light scattering models of snow are very important for remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient, and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of grain shape and microstructure modeling in light scattering models of snow is confirmed by the simulation results' comparisons. Using the new model, snow surface grain size and pollution concentration can be retrieved using MODIS land surface reflectance, the retrieved snow grain size and snow surface area is validated using ground observations.

Ground-based microwave radiometric measurements were carried out in 2016 by using a multi-frequency microwave radiometer at L, C and X bands (1.4, 6.925 and 10.65 GHz). The aim of the experiments was to explore multi-frequency microwave emission characteristics of the soil freeze-thaw process for model and algorithm development for the future Water Cycle Observation Mission (WCOM). Measurements were carried out on pastureland in Chengde, Hebei Province, which belongs to seasonally frozen ground of China. Soil temperature and soil moisture profiles, the frost depth, and meteorological observations were synchronously collected. It has been found that microwave radiation has different responses to soil freezing and thawing process at different frequencies.

We validate an absorption based radar retrieval algorithm of snow water equivalent (SWE) using X- and Ku-band backscatter with airborne SAR data. The bicontinuous dense media radiative transfer (Bic-DMRT) model is first applied to generate a look-up table of snow properties against backscattering at X- and Ku-bands. In the retrieval algorithm, the background scattering is subtracted from the total scattering giving the volume scattering of snow. With the look-up table, we generate regression equations between multiple and single scattering and correlations between the scattering albedo and optical thickness at the two bands. With these relationships and the volume scattering of the snowpack, the best solution for the radar observation is found using a priori constrained least-squares cost function. Next, the absorption loss of the snowpack is derived from the solution, which is directly proportional to the SWE. We have applied the algorithm to airborne SAR observations from Finland and Canada. The retrieval algorithm is shown to be effective, achieving root mean square error (RMSE) of ~19 mm for both SnowSAR data, which is smaller than the 20mm RMSE requirement of SCLP.

Active microwave remote sensing is a promising tool for global snow water equivalent (SWE) mapping. However, many studies have shown that more information is needed to estimate the SWE accurately. A very important problem is characterizing the snow grain size and quantitatively separating the effects of grain size and snow mass on the backscattering magnitude. In this letter, QuikScat backscattering coefficient data are used to estimate snow depth, with the snow grain size, density, and temperature estimated from the snow thermal model, driven by the Global Land Data Assimilation System forcing data. Considering the spatial resolution and the incident angle of the enhanced resolution QuikScat data, the estimation is applied to flat farm land and grass land. The snow thermal simulated snow grain size was found to be well correlated with the QuikScat measurements of the effective scattering coefficient, and the relationship between them is calibrated using data from one site in 2008-2009. Then, this calibrated relationship is used to estimate the snow depth at other sites. The results show that the snow thermal model simulated grain size can be used to improve the snow depth estimation from active microwave remote sensing.

The snow in high-mountain Asia (HMA) is of great importance, as it is very sensitive to the climate change. Air temperature and precipitation shifts/increases will be reflected in the timing of snowmelt onset. In this letter, a new algorithm is proposed to determine the snowmelt onset date from active and passive microwave remote sensing data, and the spatial and temporal pattern of snowmelt onset in HMA is studied using active and passive microwave remote sensing for the first time. Over 35 years of passive microwave data and ten years of active microwave data are used to derive the melt onset date in HMA. The active microwave data has 4.5-km resolution so that more detailed spatial pattern of snowmelt onset date can be derived compared to the 25-km resolution passive microwave data. Under climate change background, time series analyses of the snowmelt onset date in HMA are conducted to study the snowmelt onset time changes in recent 35 years. This letter provides an objective evidence of climate change impact on the cryospheric system. Time series analysis shows that the snowmelt onset date is becoming earlier in HMA region during 1988–2015, except the Karakorum Mountains and part of the western Kunlun Mountains. Mean air temperature is compared with the time series snowmelt onset date and the results show that there is strong correlation between mean air temperature and average snowmelt onset date. A 4.5 days/degree rate of snowmelt onset date advancing is found.

This paper will investigate the effect of layered snow in alpine regions on microwave emission at Ku and Ka bands, using both experimental data and model simulations. A multilayer dense-media radiative transfer model (DMRT), was implemented under the quasi-crystalline approximation (ML-QCA), to account for the effects of snow layers on the emission from dry snow covers. The model then evaluated the sensitivity of two microwave indices, based on frequency and polarization combinations, to snow parameters. Model simulations were compared to radiometric dual frequency/polarization measurements of snow covers, collected during long-term experiments carried out over three winter seasons between 2007 and 2011 in the Eastern Italian Alps. This comparison has the twofold purpose of validating the model with experimental data and verifying the influence of snow layering on microwave emission and related frequency and polarization indices. The wide variations in snow characteristics over several winter seasons allowed for an extended validation of the model, which was demonstrated to account for the complex stratigraphy (up to 15 layers) of snow. The measured brightness temperatures at Ku and Ka bands were compared to those simulated through the multi (ML-QCA) and single-layer (SL-QCA) models, by using the observed snow parameters as inputs. In the case of SL, we used the average value of all layers weighted for the layer thickness. The results showed that the ML-QCA model was better correlated to the radiometric measurements than the SL-QCA. A direct comparison of measured and simulated data showed that the slope of correlation for the single-layer ranged between 0.4 and 0.5, with determination coefficient lower than 0.3; whereas the slope in the multi-layer approach ranged between 0.7 and 1.0, with determination coefficients between 0.51 and 0.75 and Root Mean Square Error (RMSE) between 11K and 15K.

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies. The WCOM is expected to be implemented during the 13thfive-year-plan period (2016–2020).

Snow cover is one of the most important components in predicting global water and influence the global heat budget. In this study, we reported the spatial and temporal distribution of seasonal wet snow cover derived from enhanced resolution (4.45 km/pix) QuikSCAT Ku band backscatter measurements in the winters of 2002-2009 of China. A threshold method was used to detect melt events. The main melt event was identified by the longest of melt duration. The wet snow map derived from satellite data over China was compared with in situ snow and air temperature measurements from Global Historical Climatology Network.

Snow water equivalent is a key parameter in hydrology and climatology. In this study, we estimates snow water equivalent based on bi-continuous vector radiative transfer (VRT) model at X (9.6 GHz) and Ku (17.2 GHz) bands radar scatter. First, the relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from bi-continuous VRT model. Then, cost function with constraints is used to solve effective albedo and optical thickness and absorption part of optical depth can be obtained from these two parameters. The backscattering signals before snowfall are regarded as ground backscattering signals under snow cover. We finally retrieve snow water equivalent from backscattering signals with X and Ku bands at VV and VH polarizations. The retrieval algorithm is validated utilizing ground measurements from NoSREx (Nordic Snow Radar Experiment) campaign.

Water vapor plays an important roles in the Earth's energy and water cycles. Compared to optical remote sensing, microwave remote sensing has the advantage to acquire information of atmosphere under cloudy condition. Up to now, there is no published reliable total precipitable water product over land from AMSR2 due to effect of high land surface emissivity in microwave band. In this study, an improved total precipitable water retrieved algorithm for AMSR2 will be developed based on previous studies. In the retrieval algorithm, a land surface emissivity parameter estimation model is developed using combination of AMSR2 and MODIS observation. The precisely estimated surface emissivity parameter is the key parameter in the retrieval of total precipitable water. Finally, the total precipitable water was retrieved using a look-up table, and it is validated using total precipitable water observed from global distributed GPS.

We apply computer generation of anisotropic bicontinuous media with different vertical and horizontal correlation functions. We then use NMM3D (Numerical solutions of Maxwell equations in 3-Dimensions) to calculate the uniaxial effective permittivities and the effective propagation constants of V and H polarizations. The co-polarization phase differences (CPD) between VV and HH backscattered signal are derived. The CPD has recently been applied to the retrieval of snow water equivalent (SWE) and snow depth. The NMM3D simulation results are also compared with the results from that of the strong permittivity fluctuations (SPF) in the low frequency limit.

In this study, a novel approach is proposed to simultaneously detect clouds and cloud shadows for remotely sensed images. Unlike the existing methods that based on spectral tests, it is based on the simulated band radiance, so that it can be applied to any remotely sensed images. The results showed that it very effective compared to existing algorithms.

Global mapping methods of snow water equivalent (SWE) are developed in this study using WCOM (Water Cycle Observation Mission) active/passive multichannel observations. Based on the payloads of WCOM mission, especially with X/Ku scatterometer and L/Ku/Ka radiometer active/passive observations, there are obvious advantages in snow water equivalent retrieval. The estimation of SWE mainly rely on the high resolution X and Ku band scatterometer, and combined active/passive retrieval can provide more reliable SWE product. The retrieval method of SWE from X/Ku scatterometer is described in this study, and combined active/passive retrieval is also briefly described. These validation of SWE retrieval from X/Ku band scatterometer showed that we can get high accurate and high resolution SWE products from WCOM and then meet the science requirement of WCOM for water cycle studies.

Frozen ground is soil or rock in which part or all of the pore water has turned into ice. Freeze/thaw state is simply water-ice phase change, but it is an important sign like a giant on-off “switch” of the land surface processes. The freezing of soil greatly reduces the water infiltration and migration in the soil, and in consequence generates a substantial increase in snowmelt runoff. The seasonal cycles of freezing and thawing significantly influence the surface energy exchanges with atmosphere. Therefore, freeze/thaw state monitoring is becoming essential under the context of global changes. The WCOM integrates all the advantages of previous satellites, and is expected to provide more accurate information of freeze/thaw state through the synergy use of active and passive, high and low resolution measurements.

This letter presents an algorithm for retrieving soil moisture using only H-polarized multiangular brightness temperature at L-band. We developed a parameterized surface model based on a simple-empirical model, the Hpmodel, for this retrieval algorithm. By analyzing a simulated database using the advanced integral equation model (AIEM), it was found that the roughness variable Hhcan be parameterized as a function of an effective roughness parameter Sr = (kL· s)2-N(s/l)N. Influences of three surface roughness parameters (e.g., rms height, correlation length, and type of autocorrelation function) required to describe a rough surface on surface reflectivity were all considered in this parameterized model. Comparison with AIEM simulations over a wide range of soil conditions indicates a good performance of this model. Then, based on the ω - τ model, this algorithm is applied on refined SMOS H-polarized multiangular brightness temperature. Retrieved soil moisture in Africa exhibits reasonable patterns and temporal changes. Validation using in situ soil moisture from Little Washita watershed and Yanco over 2010-2011 showed fine accuracy with root-mean-square errors of 0.031 and 0.045 m3/m3 for two areas, respectively.

In this letter, we generate anisotropic bicontinuous media with different vertical and horizontal correlation functions. With the computer-generated bicontinuous medium, we then use numerical solutions of Maxwell equations in 3-dimensions (NMM3D) to calculate the anisotropic effective permittivities and the effective propagation constants of V and H polarizations. The copolarization phase difference (CPD) of VV and HH is then derived. The CPDs have recently been applied to the retrieval of snow water equivalent, snow depth, and anisotropy. The NMM3D simulation results are also compared with the results of the strong permittivity fluctuations in the low frequency limit and compared against the Maxwell-Garnett mixing formula.

L-band radiometry is a promising pathway for soil moisture estimation at global scale. An observation system simulation experiment was conducted for LEWIS over the SMOSREX bare soil site in 2006 through coupling the Variable Infiltration Capacity(VIC) land surface model and a Multi-Option L-band Microwave Emission Model(MOLMEM) in this study. Impacts from different dielectric constant models and roughness correction schemes on brightness temperature simulation were analyzed.

Light scattering models of snow are very important for the remote sensing of snow. Many previous models have used unrealistic assumptions about the snow particle shape and microstructure. In this paper, a new model is proposed, wherein a bicontinuous medium is used to simulate the snow microstructure, and geometric optics theory is used in combination with the Monte Carlo method to simulate the scattering properties of snow. Then, using the radiative transfer equation, the snow reflectance, including the polarized reflectance, can be simulated. Unlike other models that use Monte Carlo ray tracing, the new model is computationally efficient and can be used for massive simulations and practical applications. The simulation results of the new model are compared with the ground measurements and simulation results of a traditional model based on the Mie theory. Through validations and comparisons, the new model is shown to demonstrate a significantly improved capability in simulating the bidirectional reflectance of snow. The importance of the grain shape and microstructure modeling in the light scattering models of snow is confirmed by the comparison of the simulation results.

In this paper, the effects of multilayer structure of snowpack and its temporal evolution on backscattering are investigated by model simulations. The study is focused on layering structures of dry snow that may represent a risk of avalanches in Alpine regions. The implemented model has been validated using X-band Cosmo SkyMed (CSK ®) acquisitions collected in the winters between 2009 and 2013 on a test area located in the Eastern part of the Italian Alps, and corresponding direct measurements of the main snow parameters. After the validation, the models are applied to simulate the backscattering from snow profiles typical of snow covers characterized by a high risk of avalanches.

Accurate microwave brightness temperature is important for the retrieval of land surface parameter. However, the existence of atmosphere affect acquisition of brightness temperature by microwave sensor onboard satellite. In this paper, atmosphere sensitivity of each band of AMSR-E is analyzed and an atmosphere correction method is developed with ancillary water vapor and cloud liquid water data for both clear and cloudy condition. As a validation, time series of microwave vegetation index is used to qualitatively verify the atmosphere corrected brightness temperature, and it shows that the atmosphere correction method make a good improvement on microwave vegetation index.

Land surface emissivity is a critical factor controlling the energy budget on earth surface. However, this important parameter is poorly represented utilizing the “constant-ε” assumption in the state-of-the-art land surface models as well as climate models due to lack of observations. Satellite sensors such as the Advanced Very High Resolution Radiometer(AVHRR) and Moderate-resolution Imaging Spectrometer(MODIS) can provide Narrow Band Emissivity (NBE) products. These NBE products need to be preprocessed to produce reliable Broad Band Emissivity (BBE) which can be then assimilated into land surface models. This paper presents a preliminary sensitivity study of land surface energy balance simulation utilizing the long-term Global Land Surface Satellite (GLASS) BBE product in the arid and semiarid regions of northwestern China. We find that the GLASS-based land surface emissivities in the study region show great spatial and temporal variabilities. Satellite derived emissivity for bare soil ranges from 0.90 to 0.985 and more than half of bare soil grids over our study region have emissivity values less than 0.94. Decreased emissivity would lead to increased surface temperature and sensible heat flux. In-situ simulation results indicate that the ground surface temperature and heat fluxes simulations can be improved when satellite derived emissivity is assimilated.

Earth observation satellites play a critical role in providing information for understanding the global water cycle, which dominates the Earth-climate system. However, limitations in observations will restrict our current ability to reduce the uncertainties in the information used to make decisions regarding to water use and management. Under the support of “Strategic Priority Research Program for Space Sciences” of the Chinese Academy of Sciences, a new satellite concept of global Water Cycle Observation Mission (WCOM) is proposed, aiming to provide higher accuracy and consistent measurements of key elements of water cycle from space, including soil moisture, ocean salinity, freeze-thaw, snow water equivalent and etc. The expected more consistent and accurate datasets would be used to refine existing long-time series of satellite measurements, to constrain hydrological model projections and to detect the trends necessary for global change studies.

Longwave radiation is a key component of total energy that drives surface energy balance at the interface between the surface and atmosphere. To date, a number of algorithms have been developed toward accurately estimating surface longwave radiation from remotely sensed data. While most of these existing algorithms can only derive longwave radiation under clear-sky conditions due to the limited penetration of optical remote sensing thus leading to spatial incontinuity in derived radiation map. Wherein the land surface temperature (LST) play a key role in longwave radiation estimation, especially for surface emitted (upwelling) and net longwave flux. If LSTs under cloudy area can be recovered, the derivation of surface longwave ration under cloudy conditions would be straightforward. To this end, in this paper, a fusing strategy is proposed to combine the LST measurements from MODIS and AMSR-E. The results show that the proposed fusing strategy for combining microwave and optical space-based measurements in recovering surface LST under cloudy conditions is very effective. By fusion, the spatial coverage of valid LSTs over the globe is highly improved.

Snow water equivalent is an important parameter for natural science studies. One promising sensor configuration for quantitative snow water equivalent remote sensing is the X and Ku band dual-polarization SAR, such as the CoreH2O mission. The retrieval algorithm of terrestrial snow water equivalent from this sensor configuration suffers two major problems which are the decomposition of volume and surface backscattering, and the calibration of snow grain size effect. In previous study, we proposed a preliminary algorithm for snow water equivalent retrieval using X and Ku band dual-polarization radar. In the meantime, in the past years there has been progress in both ground experiment techniques and electromagnetic scattering modeling of snow cover. This enables us to make a further refinement and improvement of the retrieval algorithm based on the new experimental data and newly developed electromagnetic scattering models. In this study, we propose a refined version snow water equivalent inversion algorithm based on X and Ku band dual-polarization radar. The volume backscattering of snow is decomposed using the depolarization ratio, and the single scattering albedo and the optical depth is retrieved by the dual-frequency volume backscattering signal. Then the snow water equivalent is retrieved using the absorption part of the optical depth. The retrieval algorithm is tested using the Nosrex experiment carried out in Finland.

Current and future satellite missions with L-band passive microwave radiometers could provide useful information for monitoring the soil moisture and freeze/thaw state at a global scale. The soil surface roughness plays a significant role in microwave emission from land surfaces. In this study, a simple parameterized model from exponentially correlated surface was developed. Results indicated the model can be very useful in understanding the effects of surface roughness on microwave emission.

In this paper, a multilayer version of the Dense Medium Radiative Transfer (DMRT) model has been implemented for the active remote sensing. The effects of multilayer structure of snowpack and its temporal evolution on backscattering have been investigated. The study has been focused on the effect of layering structure of snowpack typical of the Alpine regions. The ground measurements used as model inputs have been collected on the Italian Alps during the 2009–2010 winter season.

The analysis of the relationships between FI & SPD and SWE/SD was carried out using experimental data and simulations obtained using the DMRT-QCA Multilayer model. The comparison of experimental results and model analyses made it possible to investigate the polarizing effect of snow layering and to better assess the sensitivity of FI and SPD to the snow accumulation.

Dry snow is a two-phase random medium with air and ice, which can be modeled as either sphere particles or random medium. The newly developed bicontinuous scattering model greatly enhanced the ability to model the snow scattering characteristics based on microstructure with most similarity to real snowpack. In this study, the geometric equivalent parameters of bicontinuous scattering model are derived and validated by using stereological method. By combining with dense media radiative transfer equations, the snow covered ground backscattering coefficient and brightness temperature are simulated. The bicontinuous-DMRT model is compared with sphere based model - QCA-DMRT model. The snow scattering characteristics predicted by the two models are studied. The models are validated with scatterometer and radiometer measurements.

Monitoring of snow cover is crucial to the study of global climate changes for water resource management, as well as for flood and avalanche risk prevention. The sensitivity to snow characteristics of X-band backscattering of COSMO-SkyMed mission has been analyzed in the framework of experimental and model activities. X-band data have been found to contribute to the retrieval of the snow water equivalent (SWE), provided that the snow cover is characterized by a snow depth (SD) of roughly 60-70 cm (SWE >; 100-150 mm) and with relatively large crystal dimensions. Subsequently, an algorithm for retrieving SD or SWE has been developed and tested with experimental data collected on several ground stations.

Monitoring of snow cover is crucial in water resource management and hydrological risk prevention. Experiments have shown the ability of C-band SAR in mapping the extent of wet snow. But, detection of dry snow at this frequency is difficult due to the high transmissivity of the snowpack. A model sensitivity study, corroborated by experimental data, has demonstrated that COSMO-Skymed X-band data can give significant information for generating maps of SWE for snow depth higher than about 50-60 cm.

The development of bi-continuous scattering model greatly enhanced the ability to model the snow scattering characteristics based on microstructure with most similarity to real snowpack. In this study, snow section images of snow microstructure were used to study the scattering characteristics of snow by using the reconstructed snow 3D microstructure. The equivalent grain size of the continuous random structure is derived by using stereological method. By combining with dense media radiative transfer equations, the snow backscattering is simulated. The polarimetric and frequency characteristics were studied.

Monitoring of snow cover is crucial to the study of global climate changes, for water resource management, as well flood and avalanche risk prevention. The sensitivity of X band backscattering of Cosmo-Skymed mission has been first exploited by using model simulation and experimental data. An algorithm for retrieving snow depth or snow water equivalent has been then developed and test with experimental data.

Active microwave sensors, especially high-frequency radar systems, are highly sensitive to snow pack parameters, including Snow Water Equivalent (SWE). With the availability of several X-band space-borne SAR systems, the study attempts to make use of multiple-angle SAR observations and develop relevant SWE inversion algorithms. Analysis was carried out based on parameterized scattering models for both soil surface and snowpack. It is found that the backscattering signals at two incident angles are well correlated for both soil surface and snowpack; and snow optical thickness can be well defined and estimated through snow volume scattering at two different angles. The snow and soil parameters can be estimated through two pairs of adjacent observations. The technique was tested using theoretical simulated database. Initial analysis shows that current technique needs to be further improved and a better estimation of single scattering albedo is needed.