Paper Info
Title
LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region
Abstract
Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen's trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of -0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March-May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 degrees C increase in the average temperature in March-May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August-October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August-October.
Year
DOI
Venue
2022
10.3390/rs14153748
REMOTE SENSING
Keywords
DocType
Volume
vegetation phenology, leaf area index, Three-River headwaters region, climate change, sensitivity analysis
Journal
14
Issue
ISSN
Citations 
15
2072-4292
0
PageRank 
References 
Authors
0.34
0
17
Name
Order
Citations
PageRank
Xiaoai Dai101.35
Wenjie Fan22824.98
Yunfeng Shan301.01
Yu Gao400.34
Chao Liu512.72
Ruihua Nie601.69
Donghui Zhang701.01
Weile Li804.06
Lifu Zhang98728.77
Xuejian Sun1001.01
Tiegang Liu1100.68
Zhengli Yang1202.03
Xiao Fu1311.03
Lei Ma14335.90
Shuneng Liang1500.68
Youlin Wang1601.35
Heng Lu1702.03