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

Ke Huang; Yangjian Zhang; Torbern Tagesson; Martin Brandt; Lanhui Wang; Ning Chen; Jiaxing Zu; Hongxiao Jin; Zhanzhang Cai; Xiaowei Tong; Nan Cong; Rasmus Fensholt

doi:10.1016/j.scitotenv.2020.144011

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

Sci Total Environ. 2021 Feb 20:756:144011. doi: 10.1016/j.scitotenv.2020.144011. Epub 2020 Nov 21.

Authors

Ke Huang¹, Yangjian Zhang², Torbern Tagesson³, Martin Brandt⁴, Lanhui Wang⁵, Ning Chen⁶, Jiaxing Zu⁷, Hongxiao Jin⁸, Zhanzhang Cai⁹, Xiaowei Tong¹⁰, Nan Cong¹¹, Rasmus Fensholt¹²

Affiliations

¹ Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark.
² Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China. Electronic address: zhangyj@igsnrr.ac.cn.
³ Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark; Department of Physical Geography and Ecosystems Analysis, Lund University, Lund 22100, Sweden. Electronic address: torbern.tagesson@nateko.lu.se.
⁴ Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark. Electronic address: mabr@ign.ku.dk.
⁵ Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark. Electronic address: lanw@ign.ku.dk.
⁶ Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China. Electronic address: chenning.16b@igsnrr.ac.cn.
⁷ Nanning Normal University, Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning 530001, China; Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning 530001, China.
⁸ Department of Physical Geography and Ecosystems Analysis, Lund University, Lund 22100, Sweden; Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark. Electronic address: honji@env.dtu.dk.
⁹ Department of Physical Geography and Ecosystems Analysis, Lund University, Lund 22100, Sweden. Electronic address: zhanzhang.cai@nateko.lu.se.
¹⁰ Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark. Electronic address: xit@ign.ku.dk.
¹¹ Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. Electronic address: congnan@igsnrr.ac.cn.
¹² Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark. Electronic address: rf@ign.ku.dk.

PMID: 33316646
DOI: 10.1016/j.scitotenv.2020.144011

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 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.

Keywords: Physiological phenology; SOS; Snow cover; Structural phenology; Tibetan Plateau; Trend detection.