Monitoring snow cover using Chinese meteorological satellite data over China

Yang, Juntao; Jiang, Lingmei; Shi, Jiancheng; Wu, Shengli; Sun, Ruijing; Yang, Haiguang

doi:10.1016/j.rse.2013.12.022

Cited by 34 publications

(32 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The preprocessing of the FY-2E/F VISSR imagery was consistent with the techniques presented by Yang et al [45], who instead focused on FY-2D/E VISSR images. Top-of-atmosphere reflectance data from the VIS band and temperature brightness data from the IR channels were calculated using look-up tables and then projected onto the geographic projection with a grid size of 0.05 • .…”

Section: Fy-2 Vissr Snow Detectionsupporting

confidence: 58%

“…We developed a new version of the snow cover detection algorithm proposed by Yang et al [44] and Yang et al [45] to map the binary snow cover over China. The preprocessing of the FY-2E/F VISSR imagery was consistent with the techniques presented by Yang et al [45], who instead focused on FY-2D/E VISSR images.…”

Section: Fy-2 Vissr Snow Detectionmentioning

confidence: 99%

“…The pixelwise daily snow cover was only assigned if snow was identified more than three times in hourly images under low solar zenith angles. In this study, we did not blend the FY-2 VISSR images with FY-3B MWRI snow cover data to reduce the cloud coverage [45], because the coarse spatial resolution of the FY-3B MWRI data could lead to substantial uncertainties.…”

Section: Fy-2 Vissr Fsc Mappingmentioning

confidence: 99%

“…To monitor snow cover over China, a two-phase classification scheme to produce binary snow cover maps using the Multifunction Transport Satellite-2 (MTSAT-2, also known as Himawari-7) was proposed and developed into a new multisensor algorithm using the VISSR onboard FY-2D/E in addition to the Microwave Radiation Imager (MWRI) onboard FY-3B [44,45]. The visible (VIS) band and IR4 channel of the FY-2 VISSR can be used to construct multiple snow indices, including the NDSI and ratio snow index (RSI), in order to estimate the FSC through an empirical relationship.…”

Section: Introductionmentioning

confidence: 99%

See 3 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: Fy-2 Vissr Snow Detectionsupporting

confidence: 58%

Section: Fy-2 Vissr Snow Detectionmentioning

confidence: 99%

Section: Fy-2 Vissr Fsc Mappingmentioning

confidence: 99%

Section: Introductionmentioning

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

show abstract

“…For example, satellite-derived products can be used to characterize bio-optical properties in coastal waters [1], better understand large-scale drought [2], monitor snow cover [3], estimate plant species richness [4], understand the relative abundance of disease vectors [5], and understand many other ecological processes [6]. While satellite-derived products cannot replace in situ observations, they can be used in conjunction with in situ observations for better understanding of small and large-scale processes.…”

Section: Introductionmentioning

confidence: 99%

From Space to the Rocky Intertidal: Using NASA MODIS Sea Surface Temperature and NOAA Water Temperature to Predict Intertidal Logger Temperature

Sutton

Lakshmi

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:The development of satellite-derived datasets has greatly facilitated large-scale ecological studies, as in situ observations are spatially sparse and expensive undertakings. We tested the efficacy of using satellite sea surface temperature (SST) collected by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and local water temperature collected from NOAA buoys and onshore stations to estimate submerged intertidal mussel logger temperatures. Daily SST and local water temperatures were compared to mussel logger temperatures at five study sites located along the Oregon coastline. We found that satellite-derived SSTs and local water temperatures were similarly correlated to the submerged mussel logger temperatures. This finding suggests that satellite-derived SSTs may be used in conjunction with local water temperatures to understand the temporal and spatial variation of mussel logger temperatures. While there are limitations to using satellite-derived temperature for ecological studies, including issues with temporal and spatial resolution, our results are promising.

show abstract