Subpixel snow mapping of the Qinghai–Tibet Plateau using MODIS data

Zhu, Ji; Shi, Jiancheng; Wang, Yuanhui

doi:10.1016/j.jag.2012.02.001

Cited by 27 publications

(12 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the performance of the FY-2E/F VISSR FSC at 0.05 • (approximately 5 km) resolution and investigate its cloud-removal abilities, we chose the 500 m resolution MODIS FSC based on MESMA as reference data [20,21]. When aggregated into 0.05 • grids, this MODIS FSC product has an average RMSE of approximately 0.05 and is fairly reliable for the evaluation of the FY-2E/F FSC.…”

Section: Validation Datamentioning

confidence: 99%

“…To account for the effects of land cover types on the FSC maps, we also used the 2012 International Geosphere-Biosphere Programme (IGBP) land cover data from the MCD12C1 product and clustered 17 land cover classes into 6 categories: inland water, forest, grassland, cropland, barren land, and snow/glacier (see Figure 1) [20,21]. When aggregated into 0.05° grids, this MODIS FSC product has an average RMSE of approximately 0.05 and is fairly reliable for the evaluation of the FY-2E/F FSC.…”

Section: Auxiliary Datamentioning

confidence: 99%

“…With the surface reflectance data of seven bands from the MOD09GA product, we retrieved the FSC over China and aggregated it to match the spatial resolution of the FY-2E/F FSC maps. The cloud removal scheme of Zhu et al [21] was excluded from the snow cover mapping process, because snow cover information that is substituted with corresponding cloud-free observations from another MODIS image might be inconsistent with FY-2E/F high temporal resolution observations. The cloud cover in the reference FSC data was determined based on the cloud state in the MOD09GA product, which is likely to exhibit less erroneous cloud detection results than the MOD35 cloud mask employed in the MOD10 suite.…”

Section: Auxiliary Datamentioning

confidence: 99%

See 2 more Smart Citations

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Wang

Jiang

et al. 2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Abstract:Daily fractional snow cover (FSC) products derived from optical sensors onboard low Earth orbit (LEO) satellites are often discontinuous, primarily due to prevalent cloud cover. To map the daily cloud-reduced FSC over China, we utilized clear-sky multichannel observations from the first-generation Chinese geostationary orbit (GEO) satellites (namely, the FY-2 series) by taking advantage of their high temporal resolution. The method proposed in this study combines a newly developed binary snow cover detection algorithm designed for the Visible and Infrared Spin Scan Radiometer (VISSR) onboard FY-2F with a simple linear spectral mixture technique applied to the visible (VIS) band. This method relies upon full snow cover and snow-free end-members to estimate the daily FSC. The FY-2E/F VISSR FSC maps of China were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) FSC data based on the multiple end-member spectral mixture analysis (MESMA), and with Landsat-8 Operational Land Imager (OLI) FSC maps based on the SNOWMAP approach. The FY-2E/F VISSR FSC maps, which demonstrate a lower cloud coverage, exhibit the root mean squared errors (RMSEs) of 0.20/0.19 compared with the MODIS FSC data. When validated against the Landsat-8 OLI FSC data, the FY-2E/F VISSR FSC maps, which display overall accuracies that can reach 0.92, have an RMSE of 0.18~0.29 with R 2 values ranging from 0.46 to 0.80.

show abstract

Section: Validation Datamentioning

confidence: 99%

Section: Auxiliary Datamentioning

confidence: 99%

Section: Auxiliary Datamentioning

confidence: 99%

See 1 more Smart Citation

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Wang

Jiang

et al. 2017

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chen et al 70 extracted snow area at a subpixel scale using a linear spectral mixture model from MOD02HKM imagery. Zhu et al 71 retrieved the subpixel snow-covered area for the Qinghai-Tibet Plateau from MODIS data by employing a linear spectral mixture method based on multiple-endmember spectral analysis. Zhang 72 tested one linear mixture model (LMM) and three different nonlinear subpixel analysis methods: fuzzy c-means clustering, back-propagation neural networks, and support vector machine (SVM).…”

Section: Fraction Of Snow Covermentioning

confidence: 99%

Remote sensing for snow hydrology in China: challenges and perspectives

Wang

Hao

et al. 2014

J. Appl. Remote Sens

View full text Add to dashboard Cite

Snow is one of the most important components of the cryosphere. Remote sensing of snow focuses on the retrieval of snow parameters and monitoring of variations in snow using satellite data. These parameters are key inputs for hydrological and atmospheric models. Over the past 30 years, the field of snow remote sensing has grown dramatically in China. The 30-year achievements of research in different aspects of snow remote sensing in China, especially in (1) methods of retrieving snow cover, snow depth/snow water equivalent, and grain size and (2) applications to snowmelt runoff modeling, snow response on climate change, and remote sensing monitoring of snow-caused disasters are reviewed/summarized. The importance of the first remote sensing experiment on snow parameters at the upper reaches of the Heihe River Basin, in 2008, is also highlighted. A series of experiments, referred to as the Cooperative Observation Series for Snow (COSS), focus on some key topics on remote sensing of snow. COSS has been implemented for 3 years and will continue in different snow pattern regions of China. The snow assimilation system has been established in some regions using advanced ensemble Kalman filters. Finally, an outlook for the future of remote sensing of snow in China is given.

show abstract

“…In Hall et al's algorithm, the effects of atmosphere have not been corrected for, which may cause some errors for estimating snow cover in mountainous areas. 49 Taking into account the computational speed and accurate technique for the atmospheric correction, the surface reflectances are retrieved from the TOA reflectances by using an updated simplified method for the atmospheric correction model in this work. 50 If p c is the spectral surface reflectance of the target, surrounded by a homogeneous environment of spectral reflectance p e , the TOA spectral reflectance, pÃ at the satellite level can be expressed as [Eq.…”

Section: Atmospheric Correctionmentioning

confidence: 99%

Application of moderate resolution imaging spectroradiometer snow cover maps in modeling snowmelt runoff process in the central Zab basin, Iran

Shahabi

Khezri

Ahmad

et al. 2014

J. Appl. Remote Sens

View full text Add to dashboard Cite

Abstract. Snow, as one form of precipitation, plays a very significant role in the water cycle and in water resource management. However, the spatial and temporal variations in snow cover complicate the monitoring of this role. Field measurements, especially in mountainous areas, are often impossible without the use of new technologies. In this study, moderate resolution imaging spectroradiometer (MODIS) at 500-m resolution has been used to provide a map of snow cover area (SCA) using the normalized difference snow index in the central Zab basin in West Azerbaijan, Iran. Eight-day composite data are used to minimize the effect of cloud cover and maximize the amount of useable SCA images. The importance of snow in this basin was simulated using a snowmelt runoff model (SRM) as one of the major applications of daily MODIS-8 images based on various algorithms. The location of snow gauge stations on digital elevation model (DEM) of central Zab basin extracted from advanced space borne thermal emission and reflection radiometer images by using bilinear interpolation method. The SCA index, along with spectral threshold on bands 2 and 4, provided a stable relationship for extraction of the snow cover map in the study area. The simulated flow in the water year 2010 to 2011 had a coefficient of determination (R 2 ) of 0.8953 and a volume difference (Dv) of 0.1498%, which shows a good correlation between the measured and computed runoff by using the SRM in the central Zab basin. The first results of the modeling process show that MODIS snow covered area product can be used for simulation and measuring value of snowmelt runoff in central Zab basin. The studies found that the SCA results were more reliable in the study area. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Subpixel snow mapping of the Qinghai–Tibet Plateau using MODIS data

Cited by 27 publications

References 44 publications

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China

Remote sensing for snow hydrology in China: challenges and perspectives

Application of moderate resolution imaging spectroradiometer snow cover maps in modeling snowmelt runoff process in the central Zab basin, Iran

Contact Info

Product

Resources

About