9 results on '"Xiong, Chuan"'
Search Results
2. The Potential of COSMO-SkyMed SAR Images in Monitoring Snow Cover Characteristics
- Author
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Pettinato, S., Santi, E., Brogioni, M., Paloscia, S., Palchetti, E., and Xiong, Chuan
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Synthetic aperture radar ,Flood myth ,Meteorology ,Backscatter ,COSMO-SkyMed ,snow ,Geotechnical Engineering and Engineering Geology ,Snow ,snow water equivalent (SWE) ,Water resources ,Radar imaging ,Environmental science ,Risk prevention ,Electrical and Electronic Engineering ,snow depth (SD) ,Snow cover ,Remote sensing - Abstract
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 $ \hbox{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. © 2004-2012 IEEE.
- Published
- 2013
3. An assessment of the performance of two snow kernels in characterizing snow scattering properties.
- Author
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Ding, Anxin, Jiao, Ziti, Dong, Yadong, Zhang, Xiaoning, He, Dandan, Yin, Siyang, Cui, Lei, Chang, Yaxuan, Qu, Ying, and Xiong, Chuan
- Subjects
DISTRIBUTION (Probability theory) ,REMOTE sensing ,ZENITH distance ,RADIATIVE transfer ,PHOTONS - Abstract
The kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model has been widely used in the quantitative remote sensing community. However, the performance of this model is challenged when modelling the optical scattering properties of pure snow surfaces. Recently, two snow kernels have been developed to improve the snow anisotropic reflectance in the kernel-driven RTLSR model framework. However, the performances of these two snow kernels must be assessed to identify their potential applications. Therefore, we assess the performances of these two kernels using various BRDF data sources. Our findings demonstrate their differences in several aspects. (1) These two kernels differ in characterizing the variability in BRDF shape as a function of the solar zenith angle (SZA). As the SZA increases, the shape of snow kernel derived by the asymptotic radiative transfer (ART) model (hereinafter named the ART method) changes from a dome shape to bowl shape, which agrees well with the simulation data of the bicontinuous photon tracking (bic-PT) model. The shape of the snow kernel proposed by Qu et al. (hereinafter named the Qu method) based on the Rahman-Pinty-Verstraete (RPV) model maintains a bowl shape for all SZAs. These differences in the kernel performances affect their abilities to fit snow BRDF data with different SZAs. (2) The corrected RTLSR models, with their respective snow kernels, are generally able to model the forward-scattering properties of snow surfaces compared with the original RTLSR model. However, the ART method performs better in capturing the BRDF variations in snow surfaces than the Qu method. This assessment provides an improved understanding of the performance of these two snow kernels and, thus, suggests further applications for the ART snow kernel in the kernel-driven BRDF model framework in the near future. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
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4. Recovering Land Surface Temperature Under Cloudy Skies Considering the Solar‐Cloud‐Satellite Geometry: Application to MODIS and Landsat‐8 Data.
- Author
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Wang, Tianxing, Shi, Jiancheng, Ma, Ya, Husi, Letu, Comyn‐Platt, Edward, Ji, Dabin, Zhao, Tianjie, and Xiong, Chuan
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LAND surface temperature ,ATMOSPHERIC temperature ,REMOTE sensing ,LANDSAT satellites ,ARTIFICIAL satellites - Abstract
Clouds play a significant role in the derivation of land surface temperature (LST) from optical remote sensing. The estimation of LST under cloudy sky conditions has been a great challenge for the community for a long time. In this study, a scheme for recovering the LST under cloudy skies is proposed by accounting for the solar‐cloud‐satellite geometry effect, through which the LSTs of shadowed and illuminated pixels covered by clouds in the image are estimated. The validation shows that the new scheme can work well and has reasonable LST accuracy with a root mean square error < 4.9 K and bias < 3.5 K. The application of the new method to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat‐8 data reveals that the LSTs under cloud layers can be reasonably recovered and that the fraction of valid LSTs in an image can be correspondingly improved. The method is not data specific; instead, it can be used in any optical remote sensing images as long as the proper input variables are provided. As an alternative approach to derive cloudy sky LSTs based only on optical remote sensing data, it gives some new ideas to the remote sensing community, especially in the fields of surface energy balance. Key Points: A novel scheme for recovering the cloudy sky LST was proposed by accounting for the solar‐cloud‐satellite geometry effectThe LSTs of shadowed and illuminated pixels covered by the clouds in the image can be well estimated for both MODIS and Landsat‐8 dataThe method is not data specific and has a reasonable accuracy; it provides some new ideas to the surface energy balance community [ABSTRACT FROM AUTHOR]
- Published
- 2019
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5. Improving ground surface temperature and heat flux simulation with satellite derived emissivity in arid and semiarid regions
- Author
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Peng Bin, Li Dongyang, Xiong Chuan, Shi Jiancheng, Lei Yonghui, and Zhao Tianjie
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Heat flux ,Advanced very-high-resolution radiometer ,Imaging spectrometer ,Emissivity ,Environmental science ,Climate model ,Satellite ,Sensible heat ,Atmospheric sciences ,Energy budget ,Remote sensing - Abstract
Land surface emissivity is a critical factor controlling the energy budget on earth surface. However, this important parameter is poorly represented utilizing the “constant-e” 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.
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- 2014
6. The Potential for Estimating Snow Depth With QuikScat Data and a Snow Physical Model.
- Author
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Xiong, Chuan and Shi, Jiancheng
- Abstract
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. [ABSTRACT FROM PUBLISHER]
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- 2017
- Full Text
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7. Snowmelt Pattern Over High-Mountain Asia Detected From Active and Passive Microwave Remote Sensing.
- Author
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Xiong, Chuan, Shi, Jiancheng, Cui, Yurong, and Peng, Bin
- Abstract
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. [ABSTRACT FROM PUBLISHER]
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- 2017
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8. High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area.
- Author
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Liu, Mingyu, Xiong, Chuan, Pan, Jinmei, Wang, Tianxing, Shi, Jiancheng, and Wang, Ninglian
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SNOW cover , *REMOTE sensing , *MODIS (Spectroradiometer) , *SNOW , *ALPINE regions , *ATMOSPHERIC temperature , *SNOW accumulation - Abstract
Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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9. A total precipitable water retrieval method over land using the combination of passive microwave and optical remote sensing.
- Author
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Ji, Dabin, Shi, Jiancheng, Xiong, Chuan, Wang, Tianxing, and Zhang, Yuhuan
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METEOROLOGICAL precipitation , *REMOTE sensing , *ATMOSPHERIC water vapor , *HYDROLOGIC cycle , *CLIMATE change - Abstract
Atmospheric water vapor plays an important role in hydrologic cycle and climate change of the Earth. A number of studies have focused on retrieval of the total precipitable water (TPW) using microwave or optical remote sensing. In this paper, the global quarter-degree gridded TPW over land was retrieved using water vapor sensitivity parameter ∆ Tb 18.7 /∆ Tb 23.8 based on the combination of AMSR-E and MODIS observations. There are two major improvements in the retrieval algorithm, including optimization of the estimation model of surface emissivity ∆ ε 18.7 /∆ ε 23.8 and correction of the terrain influence to the retrieval of TPW using DEM. To obtain a high resolution TPW, we also developed an algorithm to downscale the retrieved quarter-degree gridded TPW to a fine scale of 0.05° × 0.05° using DEM and NDVI. In addition, the downscaled TPW was further calibrated using high precision TPW from MODIS in the clear-sky condition to improve its accuracy. Finally, both quarter-degree and 0.05° × 0.05° gridded TPW were validated against SuomiNet GPS retrieved TPW on a global scale. The RMSE for the retrieved quarter-degree gridded global TPW is 3.45 mm, with a correlation coefficient of 0.95. In addition, the RMSE for the downscaled 0.05° × 0.05° gridded global TPW is 4.18 mm, with a correlation coefficient of 0.95. An obvious advantage of our algorithm compared with MODIS TPW product is that it can retrieve TPW under cloudy sky condition over land. The algorithm developed in this study can be easily transferred to AMSR2 on board GCOM-W1 and provides the long-term global daily TPW over land since the launch of Aqua to present day to support hydrologic cycle and climate change studies. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
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