Investigation of land surface phenology detections in shrublands using multiple scale satellite data

Dailiang Peng, Yan Wang, George Xian, Alfredo R. Huete, Wenjiang Huang, Miaogen Shen, Fumin Wang, Le Yu, Liangyun Liu, Qiaoyun Xie, Lingling Liu, Xiaoyang Zhang

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Shrublands occupy about 13% of the global land surface, contain about one-third of the biodiversity, store about half of the global terrestrial carbon, and provide many ecosystem services to a large amount of world's human population and livestock. Because phenology is a sensitive indicator of the response of shrubland ecosystems to climate change, the alteration of ecosystems following species invasions, and the dynamics of shrubland ecosystem function, biodiversity, and carbon budgets, it is critical to monitor and assess phenological dynamics in shrubland ecosystems. However, most current land surface phenology (LSP) products derived from satellite data do not provide phenology detections in some semiarid shrublands where the amplitude of seasonal vegetation greenness is small. Therefore, we investigated the LSP detection using multiple spatial resolution satellite data and examined the impacts of spatial scales and shrubland ecosystem components (shrub and herb cover) on LSP detections over the western United States. Specifically, greenup onset date (GUD) in shrublands was detected from 30 m Harmonized Landsat and Sentinel-2 (HLS) data and 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) data to quantify scale effects. The GUD spatial patterns were explored with 30 m pixel variations in shrubland ecosystem components. The results show that GUD values varied with percent vegetation cover and shifted to earlier dates with increasing vegetation cover, demonstrating that satellite observations were not able to capture greenup onset until a threshold of green vegetation cover is reached. GUD was mostly undetectable from both HLS and VIIRS pixels with vegetation cover less than 10% and became fully detectable with vegetation covers larger than 50%. Similarly, the differences of GUD between HLS and VIIRS detections also decreased with increased vegetation cover. As a result of high shrubland heterogeneity, GUD from 30 m HLS pixels could be partially detected within a 500 m pixel despite GUD being undetectable from VIIRS time series. Moreover, vegetation cover heterogeneity also made it difficult for GUD at 30 m to be aggregated to coarse scales (such as to 500 m VIIRS pixels). These findings have significant implications to the detection and characterization of shrubland LSP responses to environmental and climate changes.

Original languageEnglish (US)
Article number112133
JournalRemote Sensing of Environment
Volume252
DOIs
StatePublished - Jan 2021
Externally publishedYes

Bibliographical note

Funding Information:
We would like to thank the editor and three anonymous reviewers for constructive comments on earlier versions of this manuscript. The data used in this work were from NASA HLS product (https://hls.gsfc.nasa.gov/data/v1.4/) and VIIRS product (https://e4ftl01.cr.usgs.gov/VIIRS/). This work was partially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant numbers XDA19090112) and National Natural Science Foundation of China (grant number 41571423) to Dailiang Peng.

Funding Information:
We would like to thank the editor and three anonymous reviewers for constructive comments on earlier versions of this manuscript. The data used in this work were from NASA HLS product ( https://hls.gsfc.nasa.gov/data/v1.4/ ) and VIIRS product ( https://e4ftl01.cr.usgs.gov/VIIRS/ ). This work was partially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant numbers XDA19090112 ) and National Natural Science Foundation of China (grant number 41571423 ) to Dailiang Peng.

Publisher Copyright:
© 2020 Elsevier Inc.

Keywords

  • Greenup onset date
  • HLS
  • Scale effect
  • Shrubland ecosystem components
  • Time series
  • VIIRS

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