The fraction snow cover (FSC) is the ratio of the snow cover area SCA to the pixel space. The data set covers the Arctic region (35 ° to 90 ° north latitude). Using Google Earth engine platform, the initial data is the global surface reflectance product with a resolution of 1000m with mod09ga, and the data preparation time is from February 24, 2000 to November 18, 2019. The methods are as follows: in the training sample area, the reference data set of FSC is prepared by using Landsat 8 surface reflectance data and snomap algorithm, and the data set is taken as the true value of FSC in the training sample area, so as to establish the linear regression model between FSC in the training sample area and NDSI based on MODIS surface reflectance products. Using this model, MODIS global surface reflectance product is used as input to prepare snow area ratio time series data in the Arctic region. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.
MA Yuan, LI Hongyi
In this study, an algorithm that combines MODIS Terra and Aqua (500 m) and the Interactive Multisensor Snow and Ice Mapping System (IMS) (4 km) is presented to provide a daily cloud-free snow-cover product (500 m), namely Terra-Aqua-IMS (TAI). The overall accuracy of the new TAI is 92.3% as compared with ground stations in all-sky conditions; this value is significantly higher than the 63.1% of the blended MODIS Terra-Aqua product and the 54.6% and 49% of the original MODIS Terra and Aqua products, respectively. Without the IMS, the daily combination of MODIS Terra-Aqua over the Tibetan Plateau (TP) can only remove limited cloud contamination: 37.3% of the annual mean cloud coverage compared with the 46.6% (MODIS Terra) and 55.1% (MODIS Aqua). The resulting annual mean snow cover over the TP from the daily TAI data is 19.1%, which is similar to the 20.6% obtained from the 8-day MODIS Terra product (MOD10A2) but much larger than the 8.1% from the daily blended MODIS Terra-Aqua product due to the cloud blockage.
ZHANG Guoqing
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