Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

Riggs, George A.; Hall, Dorothy K.

doi:10.3390/rs12223781

Cited by 22 publications

(10 citation statements)

References 33 publications

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“…We derived data for a suite of winter habitat indices, which well characterized winter environment across China. Our research highlights the use of solely remote sensing observations from multiple sources to generate a dataset, which is able to update annually given the rapidly increasing optical and microwave sensors retrieving snow cover and ground freeze/thaw status [41,44]. This could overcome the limitations of gridded datasets interpolated from station measurements or estimated by data assimilation models (e.g., SNODAS), which are not always available and have higher uncertainties [19].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is of special importance to identify suitable environmental conditions that foster species richness in China, which is helpful for setting conservation policies and achieving the targets of global initiatives and SDGs. Satellite remote sensing is well suited to retrieve ground snow cover and freeze/thaw status at high spatial and temporal resolutions and provides valuable information to characterize winter environmental conditions for biodiversity research [32,40,41]. Given this, our research aims to test how remotely sensed winter habitat indices contribute to the explanation of species richness across China.…”

Section: Introductionmentioning

confidence: 99%

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Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

Zhu

Guo

2022

Remote Sensing

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Climate change is transforming winter environmental conditions rapidly. Shifts in snow regimes and freeze/thaw cycles that are unique to the harsh winter season can strongly influence ecological processes and biodiversity patterns of mammals and birds. However, the role of the winter environment in structuring a species richness pattern is generally downplayed, especially in temperate regions. Here we developed a suite of winter habitat indices at 500 m spatial resolution by fusing MODIS snow products and NASA MEaSUREs daily freeze/thaw records from passive microwave sensors and tested how these indices could improve the explanation of species richness patterns across China. We found that the winter habitat indices provided unique and mutually complementary environmental information compared to the commonly used Dynamic Habitat Indices (DHIs). Winter habitat indices significantly increased the explanatory power for species richness of all mammal and bird groups. Particularly, winter habitat indices contributed more to the explanation of bird species than mammals. Regarding the independent contribution, winter season length made the largest contributions to the explained variance of winter birds (30%), resident birds (27%), and mammals (18%), while the frequency of snow-free frozen ground contributed the most to the explanation of species richness for summer birds (23%). Our research provides new insights into the interpretation of broad-scale species diversity, which has great implications for biodiversity assessment and conservation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

Zhu

Guo

2022

Remote Sensing

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“…SCF [29] represents the proportion of snow cover in the specified area; that is, the percentage of snow cover on the day to the total area of the study area. The calculation formula is as follows:…”

Section: Cmip6 Datamentioning

confidence: 99%

“…The spatial resolution of passive microwave remote sensing data is low, and it is impossible to accurately observe the regional snow [8,28]. However, MODIS data have the advantages of easy access, low cost, continuous observation time, and a large monitoring area [29,30]. MODIS also has a good monitoring effect in mountainous areas [31,32].…”

Section: Introductionmentioning

confidence: 99%

Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future

Zou

Sun

et al. 2022

Remote Sensing

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In the context of global warming, relevant studies have shown that China will experience the largest temperature rise in the Qinghai–Tibet Plateau and northwestern regions in the future. Based on MOD10A2 and MYD10A2 snow products and snow depth data, this study analyzes the temporal and spatial evolution characteristics of the snow cover fraction, snow depth, and snow cover days in the three stable snow cover areas in China, and combines 15 modes in CMIP6 snow cover data in four different scenarios with three kinds of variables, predicting the spatiotemporal evolution pattern of snow cover in China’s three stable snow cover areas in the future. The results show that (1) the mean snow cover fraction, snow depth, and snow cover days in the snow cover area of Northern Xinjiang are all the highest. Seasonal changes in the snow cover areas of the Qinghai–Tibet Plateau are the most stable. The snow cover fraction, snow depth, and snow cover days of the three stable snow cover areas are consistent in spatial distribution. The high values are mainly distributed in the southeast and west of the Qinghai–Tibet Plateau, the south and northeast of Northern Xinjiang, and the north of the snow cover area of Northeast China. (2) The future snow changes in the three stable snow cover areas will continue to decline with the increase in development imbalance. Snow cover fraction and snow depth decrease most significantly in the Qinghai–Tibet Plateau and the snow cover days in Northern Xinjiang decrease most significantly under the SSPs585 scenario. In the future, the southeast of the Qinghai–Tibet Plateau, the northwest of Northern Xinjiang, and the north of Northeast China will be the center of snow cover reduction. (3) Under the four different scenarios, the snow cover changes in the Qinghai–Tibet Plateau and Northern Xinjiang are the most significant. Under the SSPs126 and SSPs245 scenarios, the Qinghai–Tibet Plateau snow cover has the most significant change in response. Under the SSPs370 and SSPs585 scenarios, the snow cover in Northern Xinjiang has the most significant change.

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“…The selection of the major basin areas affected by snow was guided by 𝑆𝐶𝐷 𝑓𝑢𝑙𝑙 ̅̅̅̅̅̅̅̅̅̅ (Figure 2). For each basin, 𝑆𝐶𝐷 𝑓𝑢𝑙𝑙 ̅̅̅̅̅̅̅̅̅̅ was clipped using the shapefile and the Snow Cover Fraction (SCF) [47,48] was derived using the mean value of all valid pixels. To obtain full days, this value was multiplied by 365.25 days.…”

Section: Selection Of Major Basinsmentioning

confidence: 99%

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

Roessler

Dietz

2022

Earth

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Globally, the seasonal snow cover is the areal largest, the most short-lived and the most variable part of the cryosphere. Remote sensing proved to be a reliable tool to investigate their short-term variations worldwide. The medium-resolution sensor MODIS sensor has been delivering daily snow products since the year 2000. Remaining data gaps due to cloud coverage or polar night are interpolated using the DLR’s Global SnowPack (GSP) processor which produces daily global cloud-free snow cover. With the conclusion of the hydrological year 2022 in the northern hemisphere, the snow cover dynamics of the last 23 hydrological years can now be examined. Trends in snow cover development over different time periods (months, seasons, snow seasons) were examined using the Mann–Kendall test and the Theil–Sen slope. This took place as both pixel based and being averaged over selected hydrological catchment areas. The 23-year time series proved to be sufficient to identify significant developments for large areas. Globally, an average decrease in snow cover duration of −0.44 days/year was recorded for the full hydrological year, even if slight increases in individual months such as November were also found. Likewise, a large proportion of significant trends could also be determined globally at the catchment area level for individual periods. Most drastic developments occurred in March, with an average decrease in snow cover duration by −0.16 days/year. In the catchment area of the river Neman, which drains into the Baltic Sea, there is even a decrease of −0.82 days/year.

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Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

Cited by 22 publications

References 33 publications

Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

Remotely Sensed Winter Habitat Indices Improve the Explanation of Broad-Scale Patterns of Mammal and Bird Species Richness in China

Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future

Development of Global Snow Cover—Trends from 23 Years of Global SnowPack

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