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The confounding effect of snow cover on assessing spring phenology from space : A new look at trends on the Tibetan Plateau

Huang, Ke ; Zhang, Yangjian ; Tagesson, Torbern LU ; Brandt, Martin ; Wang, Lanhui ; Chen, Ning ; Zu, Jiaxing ; Jin, Hongxiao LU ; Cai, Zhanzhang LU and Tong, Xiaowei , et al. (2021) In Science of the Total Environment 756.
Abstract

The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a “climate change hot-spot”. Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during... (More)

The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a “climate change hot-spot”. Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003–2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R2 = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R2 = 0.77) and snow covered sites (R2 = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R2 = 0.35 for snow free and R2 = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Physiological phenology, Snow cover, SOS, Structural phenology, Tibetan Plateau, Trend detection
in
Science of the Total Environment
volume
756
article number
144011
publisher
Elsevier
external identifiers
  • pmid:33316646
  • scopus:85097351064
ISSN
0048-9697
DOI
10.1016/j.scitotenv.2020.144011
language
English
LU publication?
yes
id
e6f29794-ced3-4615-a8d6-ad8fa93a651e
date added to LUP
2020-12-21 10:26:58
date last changed
2021-03-31 03:43:55
@article{e6f29794-ced3-4615-a8d6-ad8fa93a651e,
  abstract     = {<p>The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a “climate change hot-spot”. Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003–2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R<sup>2</sup> = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R<sup>2</sup> = 0.77) and snow covered sites (R<sup>2</sup> = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R<sup>2</sup> = 0.35 for snow free and R<sup>2</sup> = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems.</p>},
  author       = {Huang, Ke and Zhang, Yangjian and Tagesson, Torbern and Brandt, Martin and Wang, Lanhui and Chen, Ning and Zu, Jiaxing and Jin, Hongxiao and Cai, Zhanzhang and Tong, Xiaowei and Cong, Nan and Fensholt, Rasmus},
  issn         = {0048-9697},
  language     = {eng},
  publisher    = {Elsevier},
  series       = {Science of the Total Environment},
  title        = {The confounding effect of snow cover on assessing spring phenology from space : A new look at trends on the Tibetan Plateau},
  url          = {http://dx.doi.org/10.1016/j.scitotenv.2020.144011},
  doi          = {10.1016/j.scitotenv.2020.144011},
  volume       = {756},
  year         = {2021},
}