Abstract. A temporal variation and spatial distribution of the snow-covered area (SCA) over the Tibetan Plateau (TP) are analyzed using moderate-resolution imaging spectrometer (MODIS)/ Terra 8-day snow cover products (MOD10A2) from 2001 to 2013 and the SCA is compared with in situ snow cover days (SCD) from the meteorological network in the TP. Results show that at monthly levels the minimum SCA occurs in July, followed by August, and the SCA increases rapidly from September, reaching the maximum in March; on average, 2002, 2005, and 2008 are snowy years, whereas 2001, 2003, 2007, and 2010 are less-snow years. Apart from strong seasonal variations, the general trend of interannual snow cover variations from 2001 to 2013 is not obvious, remaining at a relatively stable status. The snow cover over the TP is characterized by uneven geographic distribution. In general, snow is abundant with a long duration in the high mountains while it is less abundant and with a short duration in the vast interior of the TP. The interannual variations of snow cover over the TP from ground-based meteorological stations using SCD are very consistent with MODIS SCA, with a correlation coefficient of 0.80 (P < 0.01), indicating that MOD10A2 data have high accuracy to capture and monitor spatiotemporal variations of snow cover over the TP.
Abstract. Based on MOD09GA/MYD09GA surface reflectance data, a new MODIS snow-cover-extent (SCE) product from 2000 to 2020 over China has been
produced by the Northwest Institute of Eco-Environment and Resources
(NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains
two preliminary clear-sky SCE datasets – Terra-MODIS and Aqua-MODIS SCE
datasets and a final daily cloud-gap-filled (CGF) SCE dataset. The first two datasets are generated mainly through optimizing snow-cover discriminating rules over land-cover types, and the latter dataset is produced after a series of gap-filling
processes such as aggregating the two preliminary datasets, reducing cloud
gaps with adjacent information in space and time, and eliminating all gaps
with auxiliary data. The validation against 362 China Meteorological
Administration (CMA) stations shows that during snow seasons the overall
accuracy (OA) values of the three datasets are larger than 93 %, all of the omission error (OE) values are constrained within 9 %, and all of the commission error (CE) values are constrained within 10 %. Bias values of 0.98, 1.02, and 1.03 demonstrate on a whole that there is no significant overestimation nor a significant underestimation. Based on the same ground reference data, we found that the new product accuracies are obviously higher than standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For example, compared with the CE of 23.78 % that the MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 % versus 89.54 % of MOD10A1F product and 84.36 % of MYD10A1F product. Besides, as expected, snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates that the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China; thereby, it can better serve for hydrological, climatic, environmental, and other related studies there.
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