地理学报 ›› 2022, Vol. 77 ›› Issue (5): 1102-1119.doi: 10.11821/dlxb202205005
邱思静1(), 胡涛1, 胡熠娜2,3, 丁子涵1, 刘焱序4, 彭建1(
)
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:
QIU Sijing1(), HU Tao1, HU Yina2,3, DING Zihan1, LIU Yanxu4, PENG Jian1(
)
Received:
2021-02-22
Revised:
2021-10-17
Published:
2022-05-25
Online:
2022-07-25
Supported by:
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。
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.
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 | 式中: | [ |
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 | 式中: | [ |
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 | 式中:L为土壤亮度指数,与植被密度有关; | [ |
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 | 式中: | [ |
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 | 式中:x为光谱反射率或植被指数;a、b、c为系数 | [ |
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 | 式中: | [ |
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 | 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 | [ |
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 | 式中:APAR表示植被所吸收的光合有效辐射; | [ |
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: 式中:Height为树高;DBH为胸径; | [ |
表2
代表性植被光谱遥感数据集
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 | [ |
LTDR | NDVI | NOAA-AVHRR | 1 d、10 d;0.05° | 1981—今 | 波段计算 | [ |
GIMMS | NDVI | NOAA-AVHRR | 14 d;1/12° | 1981—2015 | 前向型神经网络 | [ |
NCEI | LAI/FAPAR | NOAA-AVHRR | 1 d;0.05° | 1981—今 | 人工神经网络 | [ |
SNPP-VIIRS | NDVITOA/NDVITOC/EVITOC | Suomi-NPP-VIIRS | 7 d;4 km | 2013—今 | 波段计算 | [ |
MODIS | NDVI/EVI | Terra/Aqua | 16 d、月;250 m、1 km、0.05° | 2000—今 | 波段计算 | [ |
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 | [ | |
NPP/GPP | Terra/Aqua | 1 a (NPP)、8 d(GPP);1 km | 2000—今 | 辐射传输模型 | [ | |
VCF | Terra | 1 a;250 m | 2000—2020 | 线性混合模型 | [ | |
SPOT-NDVI | NDVI | SPOT-VGT | 10 d;1 km | 1998—2014 | 波段计算 | [ |
CYCLOPES | 有效LAI/FAPAR | SPOT-VGT | 10 d;1 km | 1999—2007 | 辐射传输模型、神经网络 | [ |
PROBA-V | NDVI | PROBA | 10 d;300 m | 2016—今 | 波段计算 | [ |
Sentinel 3- OLCI | NDVI | Sentinel3- OLCI | 10 d;300 m | 2020—今 | 波段计算 | [ |
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 | [ |
CGLS V2 | LAI/FAPAR/FCPVER | MODIS;CYCLOPES | 10 d;1 km | 1999—今 | 神经网络 | [ |
GLOBCARBON | LAI | SPOT-VGT;ENVISAT-ATSR | 10 d;1/11.2° | 1998—2007 | 半经验模型 | [ |
VIP | NDVI | NOAA-AVHRR;MODIS | 1 d、7 d、15 d、1月、1 a;0.05° | 1981—2016 | 波段计算 | [ |
GIMMS | LAI/FAPAR | NOAA-AVHRR;MODIS | 15 d;1/12° | 1981—2016 | 前馈神经网络 | [ |
CLOBMAP | LAI | GIMMS/NDVI;MODIS | 8 d;0.08° | 1981—2016 | 经验植被指数 | [ |
GLASS-MODIS | LAI | CYCLOPES;MODIS;BELMANIP | 8 d;250 m、500 m、0.05° | 2000—2020 | 广义神经网络 | [ |
FAPAR | GLASS LAI | 8 d;500 m 8 d;0.05° | 2000—2020 2000—2020 | 查找表法 | [ | |
FVC | Landsat TM/ETM+;MODIS | 8 d;500 m、0.05° | 2000—2018 | 多元自适应回归样条函数 | [ | |
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 | [ | |
GLASS-AVHRR | LAI | AVHRR;CYCLOPES;MODIS;BELMANIP | 8 d;0.5° | 1981—2018 | 广义神经网络 | [ |
FAPAR | GLASS LAI | 8 d;0.5° | 1982—2018 | 查找表法 | [ | |
FVC | AVHRR;GLASS MODIS FVC | 8 d;0.5° | 1982—2019 | 多元自适应回归样条函数 | [ | |
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 | [ | |
BESS | GPP | MODIS;OCO-2;ICESat/GLAS;STRM | 8 d、1月;1 km、0.5° | 2000—2015 | 生理生态模型 | [ |
GOSIF | SIF | OCO-2;MODIS | 8 d、1月;0.05° | 2000—2020 | 分类回归树模型 | [ |
GPP | OCO-2;MODIS | 8 d、1月;0.05° | 2000—2020 | |||
CSIF | SIF | OCO-2;MODIS | 4 d;0.05°、0.5° | 2000—2016 | 前馈神经网络 | [ |
RSIF | SIF | GOME-2;MODIS | 8 d;500 m、0.5° | 2002—今 | 前馈神经网络 | [ |
| SIF | OCO-2;MODIS | 16 d;0.05° | 2014—2018 | 人工神经网络 | [ |
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