Assessing Snow Phenology over the Large Part of Eurasia Using Satellite Observations from 2000 to 2016
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Data
2.2.1. Snow Cover Products from Satellite Remote Sensing
2.2.2. Data from Ground Observations
3. Methodology
3.1. Daily Cloud-Free Snow Cover Product
- (a)
- Daily synthesis of MOD10A1 and MYD10A1: Since the difference of imaging time between MOD10A1 and MYD10A1 is about 3 h and clouds will move during this period, the two products can be synthesized to remove cloud pixels. For two pixels at the same location on the same day, if one pixel represents cloud but the other one is snow or no snow, then we replaced the cloud pixel with snow or no snow. If two pixels are both recorded as cloud, then we kept the cloud pixel.
- (b)
- Adjacent day fusion: The snow cover will last several days on the ground. By comparing adjacent images, if a pixel is classified as “snow” or “no snow” on the previous day and next day, then the pixel of image on the current day can be classified as “snow” or “no snow”. If the classification of the previous and the next day of a pixel is inconsistent, that is, the previous day is classified as “snow”, and the next day is classified as “no snow”, or vice versa, then the image of the current day remains as cloud pixel.
- (c)
- Multiple snow products fusion: Using the above steps, a large portion of the cloud pixels are eliminated. Then the AMSR-E SWE and IMS products are used to eliminate the remaining cloud cover. For each day’s images, if a pixel in the AMSR-E SWE product shows SWE greater than 0, and IMS is snow, then the remaining cloud pixel is classified as snow; if SWE is greater than 0, but IMS is snow free, then the fusion product pixel is classified as snow free; if SWE is 0, then the cloud pixel is determined to be snow free.
- (d)
- Date gap filled: The correction method is similar as the AMSR-E correction, but is used to fill the date gap after AMSR-E data stopped on September 2, 2011. To ensure the daily snow product integrity, the data before the MOD10A1 data release was also filled in by IMS resampling data, supplemented by January 1, 2000 to February 23, 2000.
3.2. Validation
3.3. Snow-Covered Area Index (SCAI)
3.4. Snow Cover Parameters
3.5. Trend Analysis
4. Results
4.1. Snow Cover Product Validation
4.2. Spatiotemporal Dynamics of SCE
4.3. Temporal Dynamics of SCAI
4.4. SCDs Dynamics
4.5. Snow Phenology Dynamics
4.5.1. SOD
4.5.2. SED
4.5.3. SDDs
5. Discussion
5.1. Daily Cloud-Free Snow Cover Products
5.2. Cross-Validation of Snow Cover Phenomenon between Remote Sensing and Ground Observations
5.3. Heterogeneity Analysis
5.4. Comparisons with Previous Results
6. Conclusions
- (1)
- The snow cover accumulation begins in late September and reaches its maximum in early January, and the average maximum SCE reaches 79.8%. From the end of February, the snow cover begins to melt, and the ablation lasts until the end of May and reaches a minimum in early July. Although the snow cover in the Northern Hemisphere has decreased over the past 50 years [61,62], the results of this study indicate that SCE in the study area decreased only in June from 2000 to 2016; there is no obvious trend of daily SCE in the study area during the 17 years.
- (2)
- The stable snow cover areas, accounting for 78.8% of the study area, are mainly located north of latitude 45° N and the mountainous area of western China. The SCDs increase significantly in 6.4% and decrease significantly in 9.1% of the regions, respectively. The decreasing areas are mainly located in the Siberian Plateau, Mongolian Plateau and Tibetan Plateau, while the significant increase occurs mainly in the instantaneous snow cover areas of northeastern and southern China.
- (3)
- The variation in snow phenology is spatially heterogeneous. In the central Siberia, Pamir and Tibetan Plateau, the SOD tends to be delayed, the SED shows an advancing tendency, and the SDDs decrease from 2000 to 2016. While in the relatively low altitude plain areas, such as the West Siberian Plain and the Eastern European Plain region, the SOD tends to advance, the SED tends to be delayed, and the SDDs increase, but the increase is not significant.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Dataset | Resolution (km) | Period |
---|---|---|
MOD10A1 | 0.5 | February 24, 2000–December 31, 2016 |
MYD10A1 | 0.5 | September 4, 2002–December 31, 2016 |
AMSR-E | 25 | September 4, 2002–September 2, 2011 |
IMS | 24 | January 1, 2000–September 4, 2002 |
4 | September 4, 2011–December 1, 2014 | |
1 | December 2, 2014–December 31, 2016 |
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Sun, Y.; Zhang, T.; Liu, Y.; Zhao, W.; Huang, X. Assessing Snow Phenology over the Large Part of Eurasia Using Satellite Observations from 2000 to 2016. Remote Sens. 2020, 12, 2060. https://doi.org/10.3390/rs12122060
Sun Y, Zhang T, Liu Y, Zhao W, Huang X. Assessing Snow Phenology over the Large Part of Eurasia Using Satellite Observations from 2000 to 2016. Remote Sensing. 2020; 12(12):2060. https://doi.org/10.3390/rs12122060
Chicago/Turabian StyleSun, Yanhua, Tingjun Zhang, Yijing Liu, Wenyu Zhao, and Xiaodong Huang. 2020. "Assessing Snow Phenology over the Large Part of Eurasia Using Satellite Observations from 2000 to 2016" Remote Sensing 12, no. 12: 2060. https://doi.org/10.3390/rs12122060