从光谱指数到融合数据集的全球植被遥感数据产品

doi:10.11821/dlxb202205005

地理学报 ›› 2022, Vol. 77 ›› Issue (5): 1102-1119.doi: 10.11821/dlxb202205005

• 土地利用与土地覆被变化 • 上一篇下一篇

从光谱指数到融合数据集的全球植被遥感数据产品

邱思静¹(), 胡涛¹, 胡熠娜²^,³, 丁子涵¹, 刘焱序⁴, 彭建¹()

1.北京大学城市与环境学院地表过程分析与模拟教育部重点实验室,北京 100871
2.上海师范大学环境与地理科学学院,上海 200234
3.上海师范大学城市发展研究院,上海 200234
4.北京师范大学地理科学学部地表过程与资源生态国家重点实验室,北京 100875

收稿日期:2021-02-22 修回日期:2021-10-17 出版日期:2022-05-25 发布日期:2022-07-25
通讯作者: 彭建(1976-), 男, 四川彭州人, 博士, 教授, 主要从事景观生态学与综合自然地理学研究。E-mail: jianpeng@urban.pku.edu.cn
作者简介:邱思静(1994-), 女, 吉林延边人, 博士生, 研究方向为植被动态与社会—生态过程。E-mail: geosijing@pku.edu.cn
基金资助:
国家重点研发计划(2017YFA0604704)

Remote sensing based global vegetation products: From vegetation spectral index to fusion datasets

QIU Sijing¹(), HU Tao¹, HU Yina²^,³, DING Zihan¹, LIU Yanxu⁴, PENG Jian¹()

1. Ministry of Education Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2. School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
3. Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
4. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Received:2021-02-22 Revised:2021-10-17 Published:2022-05-25 Online:2022-07-25
Supported by:
National Key R&D Program of China(2017YFA0604704)

摘要/Abstract

摘要：

遥感对地观测为理解陆地植被动态与全球环境变化规律提供了数据基础,基于卫星观测的全球植被遥感数据产品近年来层出不穷,但缺乏基于数据使用者视角的系统梳理。本文回顾了全球植被遥感数据产品的发展历程,梳理了相关卫星计划及对应数据产品,分析了从植被光谱指数到生物物理特征参量、传统光学遥感反演到叶绿素荧光及星载微波反演产品、从单一传感器生产到多数据源—多数据集融合产品的发展脉络,阐述了全球植被遥感数据产品的来源、特征与关联关系。认为当前全球植被遥感数据产品的生产与应用存在一定脱节,遥感产品精度制约了对地球系统的深入理解。全球植被遥感数据产品正从宏观状态监测向专业化、精细化、标准化转变,建议未来制备全球遥感数据产品应充分利用目前已积累的长时序观测数据,融合多源观测资料,提升现有数据集的时空分辨率、精度及连续性;注重提高特殊区域与特定类型生态系统的反演精度,加强多维植被特征的一体化监测;建立全球植被产品数据共享平台,提供标准的全流程数据处理与分发服务,将数据不确定性信息高效、清晰提供给用户。

关键词: 植被遥感, 植被动态, 遥感数据产品, 不确定性, 数据集

Abstract:

In recent years, earth observation and remote sensing technology has enabled a better assessment of the terrestrial vegetation dynamics and improved our understanding of critical global environmental change issues. Various long-term satellite derived vegetation datasets have been produced at the global scale. However, a systematical review of these new resources from the perspective of various end-users is still lacking. We review the history of remote sensing derived global vegetation datasets by focusing on satellite missions and corresponding data products, and paying special attention to the sources, characteristics and connections of them. These products have undergone remarkable transitions, i.e. (1) from vegetation spectral index to biophysical characteristics, (2) from traditional optical remote sensing to Solar-induced Chlorophyll Fluorescence (SIF) and satellite radar system, and (3) from single-sensor-derived products to datasets fusing multiple remote-sensing sources. In the future, in order to improve the quality of existing products considerably, including its temporal and spatial resolutions, accuracy, consistency and continuity, new datasets can be developed through fusing multiple data sources based on the raw data of long-term satellite observations. Regions and ecosystems characterized by unique natural conditions should be given more attention in the way of developing algorithms. Meanwhile, multiple vegetation traits need to be monitored. In addition, the development of sharing platforms for remote-sensing datasets, enabling targeted vegetation monitoring, is urgently required so as to facilitate standard data processing and distribution services, and deliver data quality information to end-users in an efficient and transparent way.

Key words: vegetation remote sensing monitoring, vegetation dynamics, remote sensing products, uncertainty, dataset

邱思静, 胡涛, 胡熠娜, 丁子涵, 刘焱序, 彭建. 从光谱指数到融合数据集的全球植被遥感数据产品[J]. 地理学报, 2022, 77(5): 1102-1119.

QIU Sijing, HU Tao, HU Yina, DING Zihan, LIU Yanxu, PENG Jian. Remote sensing based global vegetation products: From vegetation spectral index to fusion datasets[J]. Acta Geographica Sinica, 2022, 77(5): 1102-1119.

图/表 4

图1

表1

常见植被遥感参数及代表性反演方法

植被参量	表征含义	主要特点	代表性计算公式举例	参考文献
归一化差异植被指数NDVI	植物生长状态、植被空间分布密度	随着植被覆盖度上升趋于饱和	$N D V I = ρ N I R - ρ R E D / (ρ N I R + ρ R E D)$ 式中： $ρ N I R$ 为近红外波段; $ρ R E D$ 为红光波段	[19]
增强植被指数 EVI	在植被高覆盖地区探测植物生长状态、植被空间分布密度具有优势	减少大气和土壤噪声影响;不受植被覆盖度影响	$E V I = 2.5 (ρ N I R - ρ R E D) / (ρ N I R + 6.0 ρ R E D - 7.5 ρ B L U E + 1)$ 式中： $ρ N I R$ 为近红外波段; $ρ R E D$ 为红光波段; $ρ B L U E$ 为蓝光波段	[20]
土壤调节植被指数SAVI	削弱土壤背景对植被指数表征的影响,指示植物生长状态、植被空间分布密度	引入了土壤亮度指数,减少土壤和植被冠层背景的双层干扰	$S A V I = ρ N I R - ρ R E D ρ N I R + ρ R E D + L (1 + L)$ 式中：L为土壤亮度指数,与植被密度有关; $ρ N I R$ 为近红外波段; $ρ R E D$ 为红光波段	[21]
植被覆盖度 FVC/FCover	植被（包括叶、茎、枝）在地面的垂直投影面积占统计区总面积的百分比	描述生态系统的重要基础数据,植被群落覆盖地表状况的综合量化指标,全球、区域变化监测模型中所需要的重要信息	$F V C = (N D V I - N D V I s o i l) / (N D V I v e g - N D V I s o i l)$ 式中： $N D V I s o i l$ 为完全是裸土或无植被覆盖区域的NDVI值; $N D V I v e g$ 代表完全被植被所覆盖的像元NDVI值	[22]
叶面积指数LAI	多种定义,目前研究中多采用的是单位水平土地面积上绿叶表面积总和的一半	表征植被叶片疏密程度,研究植物冠层表面物质和能量交换的重要参数,在植物生长模型、能量平衡模型、气候模型和冠层反射模型等诸多方面研究中广泛应用	$L A I = a x 3 + b x 2 + c x + d$ $L A I = a + b x c$ $L A I = - 1 2 a l n (1 - x)$ 式中：x为光谱反射率或植被指数;a、b、c为系数	[23]
光合作用有效辐射吸收比率 FAPAR	指植被冠层阻截太阳光（400~700 nm）和有效辐射的比例	植被冠层吸收光合有效辐射的重要参数,光利用模型遥感估算净初级生产力的重要参数	$f A P A R = 1 - ρ 1 - 1 - ρ 2 × e - G θ s β L e / c o s (θ s)$ 式中： $ρ 1$ 是冠层双向反射率; $ρ 2$ 是背景反射率; $L e$ 是有效LAI; $θ s$ 是太阳天顶角;G( $θ s)$ 是太阳天顶角处消光系数; $β$ 是多次散射因子	[24]
总初级生产力 GPP	单位时间内和单位面积上绿色植物通过光合作用途径所固定的有机碳总量	进入生态系统的初始物质和能量,碳循环基础	$G P P = f A P A R × P A R × L U E$ 式中：fAPAR为光合有效辐射吸收比率;PAR为光合有效辐射;LUE为光能利用率	[25]
净初级生产力 NPP	单位时间、单位面积上由绿色植被光合作用产生的有机物质总量扣除呼吸消耗后的剩余部分	植物固定和转化光合产物的效率,也决定了可供异养生物利用的物质和能量	$N P P x, t = A P A R x, t × ε (x, t)$ 式中：APAR表示植被所吸收的光合有效辐射; $ε$ 表示光能转化率;x表示空间位置;t表示时间	[26]
地上生物量 AGB	某一时刻单位面积或体积内,林中除地下树根外,活体树木所有地上有机物质去除水分后的总重量	指示植物生态系统生产力,反映生态系统结构和功能高低的最直接的表现	单一树地上生物量的估算精度： $A G B H i g h = f H i g h H e i g h t, D B H, ρ$ $A G B M o d e r a t e = f M o d e r a t e (H e i g h t, ρ)$ $A G B L o w = f L o w (D B H, ρ)$ 式中：Height为树高;DBH为胸径; $ρ$ 为密度;High为高精度、Moderate为中精度估算、Low为低精度估算	[27]

表1

表2

表3

多源数据融合代表产品

数据集	数据产品	数据基础	时空分辨率	覆盖时段	反演算法	参考文献
CGLS V3	NDVI	SPOT-VGT;PROBA-V	10 d;1 km	1999—今	波段计算	[60]
CGLS V2	LAI/FAPAR/FCPVER	MODIS;CYCLOPES	10 d;1 km	1999—今	神经网络	[61]
GLOBCARBON	LAI	SPOT-VGT;ENVISAT-ATSR	10 d;1/11.2°	1998—2007	半经验模型	[62]
VIP	NDVI	NOAA-AVHRR;MODIS	1 d、7 d、15 d、1月、1 a;0.05°	1981—2016	波段计算	[63]
GIMMS	LAI/FAPAR	NOAA-AVHRR;MODIS	15 d;1/12°	1981—2016	前馈神经网络	[64]
CLOBMAP	LAI	GIMMS/NDVI;MODIS	8 d;0.08°	1981—2016	经验植被指数	[65]
GLASS-MODIS	LAI	CYCLOPES;MODIS;BELMANIP	8 d;250 m、500 m、0.05°	2000—2020	广义神经网络	[66]
	FAPAR	GLASS LAI	8 d;500 m 8 d;0.05°	2000—2020 2000—2020	查找表法	[67]
	FVC	Landsat TM/ETM+;MODIS	8 d;500 m、0.05°	2000—2018	多元自适应回归样条函数	[68]
	GPP	FLUXNET;GLASS LAI	8 d;500 m	2000—2020	贝叶斯多算法集成多光能利用率模型	[69]
GLASS-AVHRR	LAI	AVHRR;CYCLOPES;MODIS;BELMANIP	8 d;0.5°	1981—2018	广义神经网络	[70]
	FAPAR	GLASS LAI	8 d;0.5°	1982—2018	查找表法	[67]
	FVC	AVHRR;GLASS MODIS FVC	8 d;0.5°	1982—2019	多元自适应回归样条函数	[68]
	GPP	FLUXNET;GLASS LAI	8 d;5 km	1982—2018	贝叶斯多算法集成光能利用率模型	[70]
BESS	GPP	MODIS;OCO-2;ICESat/GLAS;STRM	8 d、1月;1 km、0.5°	2000—2015	生理生态模型	[71]
GOSIF	SIF	OCO-2;MODIS	8 d、1月;0.05°	2000—2020	分类回归树模型	[72]
GOSIF	GPP	OCO-2;MODIS	8 d、1月;0.05°	2000—2020	分类回归树模型	[72]
CSIF	SIF	OCO-2;MODIS	4 d;0.05°、0.5°	2000—2016	前馈神经网络	[73]
RSIF	SIF	GOME-2;MODIS	8 d;500 m、0.5°	2002—今	前馈神经网络	[74]
$S I F ¯ o c o 2_005$	SIF	OCO-2;MODIS	16 d;0.05°	2014—2018	人工神经网络	[75]

表3

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从光谱指数到融合数据集的全球植被遥感数据产品

Remote sensing based global vegetation products: From vegetation spectral index to fusion datasets

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数据集	数据产品	数据来源	时空分辨率	年份	反演算法	参考文献
GFCC30TC	GFCC	Landsat	5 a;30 m	2000、2005、2010、2015	分段线性函数	[32]
LTDR	NDVI	NOAA-AVHRR	1 d、10 d;0.05°	1981—今	波段计算	[33]
GIMMS	NDVI	NOAA-AVHRR	14 d;1/12°	1981—2015	前向型神经网络	[34]
NCEI	LAI/FAPAR	NOAA-AVHRR	1 d;0.05°	1981—今	人工神经网络	[35]
SNPP-VIIRS	NDVI^TOA/NDV^ITOC/EVI^TOC	Suomi-NPP-VIIRS	7 d;4 km	2013—今	波段计算	[36]
MODIS	NDVI/EVI	Terra/Aqua	16 d、月;250 m、1 km、0.05°	2000—今	波段计算	[29]
	LAI/FAPAR	Terra/Aqua	4 d、8 d;500 m	2000—今	主算法：三维辐射传输模型模拟数据建立查找表;备用算法：LAI与NDVI统计关系	[29]
	NPP/GPP	Terra/Aqua	1 a (NPP)、8 d(GPP);1 km	2000—今	辐射传输模型	[37]
	VCF	Terra	1 a;250 m	2000—2020	线性混合模型	[38]
SPOT-NDVI	NDVI	SPOT-VGT	10 d;1 km	1998—2014	波段计算	[39]
CYCLOPES	有效LAI/FAPAR	SPOT-VGT	10 d;1 km	1999—2007	辐射传输模型、神经网络	[30]
PROBA-V	NDVI	PROBA	10 d;300 m	2016—今	波段计算	[40]
Sentinel 3- OLCI	NDVI	Sentinel3- OLCI	10 d;300 m	2020—今	波段计算	[41]

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