Spatial uncertainty in multi-year mean phenology based on remote sensing data

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Spatial uncertainty in multi-year mean phenology based on remote sensing data
Vol. 26, Issue 9, Pages: 1814-1823(2022)
Received：29 January 2021，

Published：07 September 2022
DOI： 10.11834/jrs.20221043
稿件说明：

移动端阅览

金佳鑫，季盈盈，郭丰生，于涵，肖园园.2022.基于遥感数据的多年平均物候不确定性研究.遥感学报，26（9）： 1814-1823 DOI： 10.11834/jrs.20221043.

Jin J X，Ji Y Y，Guo F S，Yu H and Xiao Y Y. 2022. Spatial uncertainty in multi-year mean phenology based on remote sensing data. National Remote Sensing Bulletin， 26（9）：1814-1823 DOI： 10.11834/jrs.20221043.

摘要

多年平均物候能够反映植被生长发育节律的均衡状态，是植被物候模拟与预测的关键参数之一。遥感已广泛用于地表物候监测，是空间多年平均物候信息的重要来源。然而，基于遥感的多年平均物候存在不同计算方法，如先确定每年时序曲线的物候点再求平均值（平均法），以及先求多年平均时序曲线再确定物候点（参考曲线法）。上述方法的结果可能存在差异，但目前尚缺乏对这一不确定性及其影响的认识。针对该问题，本研究利用2001年—2016年遥感植被指数数据，分别在平均法和参考曲线法下提取中国森林生长季起始时间的多年平均值（

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），比较

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的差异（

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）及其空间异质性；进一步选取物候研究中常用指标，即以

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为基础的温度“季前时长PD（Preseason Duration）”，分析

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不同计算方法对物候—气候关系的潜在影响。结果表明，（1）不同方法下的

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差异显著，总体上平均法小于参考曲线法（-2.6±2.2 d，占88%），其中存在8.0%和6.0%的有效像元其动态平均法和固定平均法小于参考曲线法超过7 d，主要分布在东南丘陵地区。（2）

△

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具有显著的空间异质性，主要表现为随年均温的升高而减小（Slope=0.07 d/℃，

P

<

0.01），随年均降水的增加而增大（Slope=-0.0005 d/mm，

P

<

0.01）。（3）不同方法下的PD存在差异，约40%有效像元的差异（

△

PD）超过5 d（其中近50%的像元

△

PD超过15 d），主要分布在东南丘陵和西南山区。研究结果将为遥感地表物候的模型空间参数化应用提供有益参考。

Abstract

Multi-year mean phenology reflects the average state of vegetation growth and development rhythm and is one of the key parameters for predicting vegetation phenology. As an important source of spatial multi-year mean phenology

remote sensing is widely used for phenology detection. Different methods of multi-year mean phenology calculation are based on remote sensing. One is determining the phenological point of the annual time series curve first and then calculating the average (referred as the average method)

and another is gaining the multi-year mean time series curve first and then determining the phenological point (referred as the reference curve method). The results of the above methods may be different. However

the uncertainty and its impacts need further elucidation. Hence

this study used the remote sensing vegetation index from 2001 to 2016 to extract the multi-year mean dates of the start of the growing season (

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) using two methods in forests in China and detected the differences between the

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derived from the two methods (

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) and the spatial pattern. Furthermore

a commonly used indicator in phenological research

that is

the temperature (Preseason Duration (PD)) based on

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was used to explore the potential impact of the

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derived from different methods on the phenology–climate relationship. Results show that (1) the

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derived from different methods was significant different. The

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of the average method was generally smaller than that of the reference curve method (-2.6±2.2 days

accounting for 88%). The pixels with

△

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>

7 between the dynamic average method and the reference method and that between the fixed average method and the reference method accounted for 8.0% and 6.0% of the effective pixels

respectively

which are mainly distributed in the southeastern hilly area. (2) A significant spatial heterogeneity of

△

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showed a decrease with the increase of the annual average temperature (

Slope

=0.07 days/℃

P

<

0.01) and the decrease of the average annual precipitation (

Slope

=-0.0005 days/mm

P

<

0.01). (3) The PD derived from different methods was distinct. Approximately 40% of the effective pixels show a difference with PD

>

5 days

and a half of them show a difference with PD

>

15 days

which are mainly located in the southeast hills and the southwest mountains. Overall

the achievements of this study provide a beneficial reference for the spatial parameterization of satellite-based phenology for modeling.

关键词

Keywords

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相关作者

JiaXin JIN 河海大学水文水资源学院;国家科技资源共享服务平台-国家地球系统科学数据中心

Yingying JI 河海大学水文水资源学院

FengSheng GUO 河海大学水文水资源学院

Han YU 河海大学水文水资源学院

Yuanyuan XIAO 河海大学水文水资源学院

TAO Shengli 北京大学城市与环境学院生态研究中心植被结构功能与建造全国重点实验室

WANG Di 西安交通大学软件学院

XIE Huan 同济大学测绘与地理信息学院

相关机构

National Earth System Science Data Center, National Science & Technology Infrastructure of China

College of Forestry, Beijing Forestry University

Institute of Ecology, College of Urban and Environmental Sciences, and State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), Peking University

School of Software Engineering, Xi'an Jiaotong University

College of Surveying and Geo-Informatics, Tongji University

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