Regional evaluation of satellite‐based methods for identifying end of vegetation growing season. Issue 4 (28th June 2021)

• Record Type: Journal Article
• Authors: Shen, Ruoque; Lu, Haibo; Yuan, Wenping; Chen, Xiuzhi; He, Bin
• Editors: Disney, Mat; Boyd, Doreen
• Is Part Of: Remote sensing in ecology and conservation. Volume 7:Issue 4(2021)
• Page Start: 685
• Page End: 699
• Publication Date: 2021-06-28
• Subjects: Autumn phenology -- end of growing season dates -- remote sensing -- satellite‐based methods -- vegetation index; Remote sensing -- Periodicals; Ecology -- Research -- Periodicals; Ecology -- Methodology -- Periodicals; Ecology -- Remote sensing -- Periodicals; Nature conservation -- Methodology -- Periodicals; 577.0723
• DOI: 10.1002/rse2.223
• Languages: English
• ISSNs: 2056-3485

Abstract

Abstract: Autumn phenology plays an important role in regulating ecosystem carbon and water cycling, but it has received less attention than spring phenology. Satellite‐based methods have been widely applied in monitoring autumn phenology at large spatial scales. However, few studies have evaluated and compared the performance of different satellite‐based methods in autumn phenology identification. Here, we compared the spatiotemporal variations of end of vegetation growing season dates (EOS) as determined from eight prevailing satellite‐based methods against long‐term field observations at 31 sites in China. We found that field‐based observations in forest and grassland sites, respectively, had rates of EOS delay of 2.11 and 3.85 days per 1°C increase in mean annual temperature (MAT) during 2001–2014. However, nearly all the eight satellite‐based methods underestimated these delay rates compared with the ground observations over all sites. We also found that the eight methods weakly agreed with the field‐observed interannual variations of EOS. At the regional scale, the identified average EOS differed up to 38 and 40 days among the investigated satellite‐based methods in forest and grassland ecosystems respectively. The delayed rate of identified EOS with the increase of MAT ranged from 0.77 to 3.51 days °C −1 for forests and from 0.41 to 2.95 days °C −1 for grasslands. The identified EOS by most of the eight methods had delayed temporal trends in forests during 2001–2014 while we found advanced trends in grassland ecosystems. The large discrepancy in EOS identification among the prevailing satellite‐based methods highlight the need for more accurate satellite‐based methods in data gap‐filling and phenometrics detection, and more extensive, multi‐species based field observations that can be used to constrain and validate the satellite‐based methods. Abstract : Autumn phenology plays an important role in regulating ecosystem carbon and water cycling, but it has received less attention than spring phenology. Satellite‐based methods have been widely applied in monitoring autumn phenology at large spatial scales. However, few studies have evaluated and compared the performance of different satellite‐based methods in autumn phenology identification. Here, we compared the spatiotemporal variations of end of vegetation growing season dates (EOS) as determined from eight prevailing satellite‐based methods against long‐term field observations at 31 sites in China. Spatial variations of the observed and satellite‐based methods identified EOS with the mean annual temperature (MAT) increasing for forest (a) and grassland (c) sites. (b) and (d) showed the slope of regression lines which indicates the delay rate of EOS with the rising MAT. The letters indicate the statistically significant ( P < 0.05) difference among observations and estimates.