Yurong Cui

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

Snow water equivalent (SWE) is a key parameter in the Earth’s energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.

Snowmelt Pattern Over High-Mountain Asia Detected From Active and Passive 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.

Observation system simulation experiment for a L-band microwave radiometer over rough bare soil site: A first step towards brightness temperature assimilation

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.

Estimation of snow wetness by a dual-frequency radar

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.

Estimation of snow water equivalent using X-band and Ku-band backscattering

Summary form only given. Snow water equivalent (SWE) is a key parameter in the earths energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. Forward simulation models can improve our understanding of snow and ground scattering mechanism and they are fundamental of developing surface parameters inversion algorithms. In recent years, theoretical modeling of microwave scattering from snow has been rapidly developed and improved our understanding of mechanism of the interaction between electromagnetic wave and snow pack. Structure of snow particle is actually very complex, not just sphere-shaped and ellipsoid-shaped. Here the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then accurately simulates the snow backscattering signals, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. Then relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous VRT model. 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. At last, SWE is estimated after temperature correction. Validation against two-year measurements of SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx) shows that the estimated SWE with the presented algorithm has a root mean square error (RMSE) of 19.82mm for the winter of 2009-2010 and 17.92mm for the winter of 2010-2011, respectively. The estimated SWE is well correlated with the measured SWE with an acceptable accuracy.

The potential of estimating snow depth from QuikScat scatterometer data and snow physical model

Active microwave remote sensing is a promising tool for global snow water equivalent mapping. However, many studies have shown that extra information is needed to estimate the snow water equivalent accurately. The most important problem is to characterize the snow grain size and to quantitatively separate the effect of grain size and snow mass on backscattering magnitude. In this study, QuikScat backscattering coefficient data is used for the estimation of snow depth and snow water equivalent, with the snow grain size and density estimated from SNTHERM model, driven by GLDAS forcing data. Considering the low spatial resolution and the fact that the enhanced resolution QuikScat data is calculated from data with multiple azimuth incident angles, the estimation of snow depth is applied in selected flat farm land sites in Russia and China. Semi-empirical first order backscattering model is used in the snow depth estimation algorithm. The effective scattering coefficient is estimated from the bulk grain size calculated from the snow profile simulated by SNTHERM model. The snow temperature and snow density simulated by SNTHERM model is used to estimate the permittivity and absorption coefficient of snowpack. Estimated time series snow depth is compared with in-situ snow depth measurements in selected sites. The retrieval results are encouraging. The relationship between SNTHERM model simulated snow grain size and microwave scattering coefficient is studied and it is found that the relationship is steady. The estimation result is also affected by the method of estimating bulk grain size from grain size profile. The results show that by combing simulated snow grain size and density, the performance of snow depth retrieval based on microwave measurement can be greatly improved.

Estimating snow water equivalent with backscattering at X and Ku bands

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.

Detection of terrestrial snowmelt of China based on QuikSCAT

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.

Global mapping of snow water equivalent with the Water Cycle Observation Mission (WCOM)

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.

Estimating daytime surface air temperature using multi-source remote sensing and climate reanalysis data at glacierized basins: A case study at Langtang valley, Nepal

Estimate surface air temperature (Ta) accurately in fine scale is very necessary for hydrological simulation, especial in glacierized basins. The purpose of this paper is to present a framework to mapping the Ta using multi-source remote sensing data and reanalysis dataset. The main content includes two parts: (a) filling the gaps in remotely sensed land surface temperature (LST) using spatial-temporal Kriging method and (b) developing a semi-empirical method to relate Ta and LST that is applicable in glacierized basins. The framework is further tested in the Langtang valley, Nepal which is a glacierized basin in the central Hindu-Kush-Himalaya (HKH) region. The validation results show that the estimated Ta has generally good spatial and temporal variations. The RMSE of Ta at Langtang Kyangjin station is 9.1K and 7.7K at 10:30 and 13:30, respectly.