DOI: 10.1016/j.rse.2020.111698
论文题名: Change point estimation of deciduous forest land surface phenology
作者: Xie Y. ; Wilson A.M.
刊名: Remote Sensing of Environment
ISSN: 344257
出版年: 2020
卷: 240 语种: 英语
英文关键词: Change point estimation
; Estimation uncertainty
; Greenup
; Logistic curve fitting
; MODIS
; Senescence
; Spatial pattern
Scopus关键词: Climate change
; Climate models
; Curve fitting
; Ecosystems
; Errors
; Mean square error
; Radiometers
; Satellite imagery
; Surface measurement
; Time series
; Uncertainty analysis
; Vegetation
; Change point estimation
; Estimation uncertainties
; Green-up
; Logistic curves
; MODIS
; Senescence
; Spatial patterns
; Forestry
; climate change
; deciduous forest
; estimation method
; global climate
; land surface
; landscape
; MODIS
; satellite imagery
; senescence
; spatial analysis
; terrestrial ecosystem
; uncertainty analysis
; Harvard Forest
; Massachusetts
; United States
英文摘要: Dramatic phenological shifts and ecosystem responses of deciduous forests to global climate change have been reported around the world. Land Surface Phenology (LSP) derived from satellite imagery is useful to estimate the phenological responses of vegetation to climate variability and inform terrestrial ecosystem models at landscape to global scales. However, there is a large (and unquantified) uncertainty in estimated phenological dates due to the relatively coarse temporal resolution of typical data and methodological limitations. To assess responses of phenology and related ecological function and services, it is essential to decrease the temporal uncertainty of estimated phenological processes. In this study, we developed a new LSP estimation method using linear change point models to determine four phenological transitions using twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) from 2000 to 2015. We evaluated the approach using long-term phenological ground observations and compare performance of four LSP estimations generated from two data sources (i.e. 8-day and twice daily EVI time series) and two methods (i.e. double logistic and change point estimation). We found that the LSP generated from change point estimation with twice daily EVI time series had the highest accuracy (i.e. lower Root Mean Square Error (RMSE), mean bias, and Mean Absolute Error (MAE)) for both spring and fall phenology evaluated by Harvard Forest phenology observations and a large citizen science database of phenological observations from the National Phenology Network. For example, change point estimation reduced the estimation error for fall senescence date from over 40 days in the standard MODIS phenology product (version 005) to 11.5–24 days of RMSE, −2.6 to −5.8 days of mean bias, and 7.9–20.1 days of MAE. The change point methodology also enables calculation of additional metrics to describe the biophysical process of vegetation, including rates of greenup, green-down, and senescence, EVI values at each phenological transition, and the estimation uncertainties for each transition date. Our LSP estimations will improve more comprehensive investigations of landscape phenology of deciduous forest and the associated ecosystem processes at regional to global scales. © 2020 Elsevier Inc. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158438
Appears in Collections: 气候变化与战略
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作者单位: Department of Geography, University at Buffalo, 105 Wilkeson Quadrangle, Buffalo, NY 14261, United States; Program in Environmental Sciences, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, United States
Recommended Citation:
Xie Y.,Wilson A.M.. Change point estimation of deciduous forest land surface phenology[J]. Remote Sensing of Environment,2020-01-01,240