自然资源遥感, 2024, 36(2): 1-9 doi: 10.6046/zrzyyg.2023059

综述

基于植被光学厚度的全球植被动态监测进展

杨妮,1,2, 邓树林,3, 樊艳红2, 谢国雪4

1.中国地质大学(武汉)地理与信息工程学院,武汉 430074

2.广西财经学院管理科学与工程学院,南宁 530003

3.南宁师范大学地理科学与规划学院,南宁 530001

4.广西农业科学院农业科技信息研究所,南宁 530003

Advances in research on the dynamic monitoring of global vegetation based on the vegetation optical depth

YANG Ni,1,2, DENG Shulin,3, FAN Yanhong2, XIE Guoxue4

1. School of Geography and Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China

2. School of Management Science and Engineering, Guangxi University of Finance and Economics, Nanning 530003, China

3. School of Geography Science and Planning, Nanning Normal University, Nanning 530001, China

4. Institute of Agricultural Science and Technology Information, Guangxi Academy of Agricultural Sciences, Nanning 530003, China

通讯作者: 邓树林(1989-),男,博士,副教授,主要从事资源环境遥感研究。Email:dengshulin12531@163.com。

责任编辑: 陈庆

收稿日期: 2023-03-8   修回日期: 2023-05-23  

基金资助: 国家自然科学基金项目“基于叶绿素荧光等多源卫星遥感的甘蔗旱灾机理与监测方法研究”(桂科42061071)
广西科技基地和人才专项“西南农业干旱时空变化的检测与归因研究”(AD20297027)
广西自然科学基金项目“我国东部季风区雨季变化的检测与归因研究”(2021GXNSFBA220061)
广西哲学社会科学规划研究课题“共同富裕目标下石漠化连片特困区多维相对贫困测度与治理研究”(22FTJ003)
广西高校中青年教师科研基础能力提升项目“气候变化背景下西南农业干旱的变化与机理研究”(2021KY0397)
统计学广西一流学科建设项目(桂教科研〔2022〕1号)

Received: 2023-03-8   Revised: 2023-05-23  

作者简介 About authors

杨 妮(1989-),女,博士研究生,副教授,主要从事GIS与遥感应用、空间信息技术应用与服务研究。Email: yangniyyy@163.com。

摘要

植被光学厚度(vegetation optical depth,VOD)为一种基于微波的植被含水量和生物量估算方法。与光学遥感相比,卫星VOD对大气扰动的敏感性较低,可测量植被不同方面的特征和信息,为全球植被监测提供了一个独立和互补的数据源,已经被广泛用于研究全球气候和环境变化对植被的影响。了解目前VOD在全球植被动态监测的应用研究进展,对其进一步发展和深入应用非常重要。鉴于此,文章首先重点介绍了被动微波和主动微波反演VOD的主要方法,对比分析不同传感器VOD产品的主要特点; 然后,从植被特征监测(如植被含水量、生物量)、碳平衡分析、干旱监测、物候分析等方面总结当前VOD在植被动态监测应用方面的研究进展; 最后,探讨了VOD产品的优缺点和改进方法,进一步展望了VOD技术在植被动态监测中的应用前景。

关键词： 植被光学厚度; 植被含水量; 生物量; 植被物候分析; 碳平衡分析

Abstract

The vegetation optical depth (VOD) serves as a microwave-based method for estimating vegetation water content and biomass. Compared to optical remote sensing, the satellite-based VOD, exhibiting a lower sensitivity to atmospheric disturbances, can measure the characteristics and information of vegetation in various aspects, thus providing an independent and complementary data source for global vegetation monitoring. It has been extensively applied to investigate the effects of global climate and environmental changes on vegetation. Discerning the research advances of VOD application in the dynamic monitoring of global vegetation is critical for VOD’s further development and application. Hence, this study first presented the primary methods for obtaining the VOD through inversion of passive and active microwave data, comparatively analyzing the principal characteristics of various sensor VOD products. Then, this study generalized the current research advances of VOD in the dynamic monitoring of vegetation in terms of vegetation characteristic monitoring (like vegetation water content and biomass), carbon balance analysis, drought monitoring, and phenological analysis. Finally, this study expounded the advantages, limitations, and improvement approaches of VOD products, envisioning the application prospect of VOD in the dynamic monitoring of vegetation.

Keywords： vegetation optical depth; vegetation water content; biomass; vegetation phenological analysis; carbon balance analysis

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本文引用格式

杨妮, 邓树林, 樊艳红, 谢国雪. 基于植被光学厚度的全球植被动态监测进展[J]. 自然资源遥感, 2024, 36(2): 1-9 doi:10.6046/zrzyyg.2023059

YANG Ni, DENG Shulin, FAN Yanhong, XIE Guoxue. Advances in research on the dynamic monitoring of global vegetation based on the vegetation optical depth[J]. Remote Sensing for Land & Resources, 2024, 36(2): 1-9 doi:10.6046/zrzyyg.2023059

0 引言

植被是陆面能量、水和碳平衡的关键要素,极易受气候变化和人类活动的影响[1]。卫星遥感获取的长时间、大范围、空间连续的较高空间分辨率观测数据,可用于监测全球或区域尺度的植被时空动态变化。微波遥感传感器主要有被动(辐射计)和主动(雷达)2种类型,微波波段的波长为1 mm~1 m,频率为0.3~300 GHz。微波遥感具有极强的穿透能力,不受云雨雾的影响,可获取不同于可见光和红外遥感反演的植被状态信息[2]。植被会减弱陆面发射或反射的微波辐射,而微波遥感可观测到植被对微波的衰减程度[1,3]。这种衰减程度通常称为植被光学厚度(vegetation optical depth,VOD),与植被冠层内微波消光效应强度有关[3-4]。卫星VOD具有时间分辨率高、不受云雨雾的影响等优点,弥补了光学植被产品归一化植被指数(normalized difference vegetation index,NDVI)的不足,为全球陆地植被监测提供了另一种手段,目前已经被广泛用于大范围的植被生态变化监测[1]。

自1978年以来,学者们基于多个卫星传感器成功反演了大量的长时序VOD数据产品,包括ASCAT[5],AMSR-E,AMSR-2[6-7],SMOS[8]和SMAP[9]等。基于不同光谱波段(L,C,X和Ku)微波遥感反演的VOD,包含植被含水量(vegetation water content,VWC)和地上生物量(above ground biomass,AGB)等不同类型的植被状态信息。不同光谱波段反演的VOD对冠层生物量的敏感性不一致,例如基于较长波长反演的VOD对较大植被生物量和较深植被层更敏感[10]。不同VOD产品对同一类型植被变化的评估可能存在一定的差异。目前,VOD已被用于监测植被物候[11]、长期植被变化[12]、植被对干旱的响应[13-14]、与火灾相关的植被变化[15]、森林损失[16]以及AGB评估[17-18]等。因此,有必要对VOD在全球植被动态监测应用方面的相关研究进行梳理与总结。本文将针对VOD的微波遥感反演方法、不同产品特点、植被动态监测等方面的研究进展进行述评。

1 VOD反演方法研究

1.1 基于被动和主动微波遥感的VOD反演

VOD可以通过被动和主动微波技术进行反演。辐射计测量地球表面以给定频率发射的热辐射,以亮度温度TB表示发射相同辐射的等效黑体的温度[19]。在给定的微波波长λ/频率f下,对于给定典型地球表面温度T,黑体辐射的比强度I0可由普朗克定律推导出。I0可表示为[1]:

I0=KBTλ2

式中KB为玻尔兹曼常数,为1.380 648 8×10-23 J/K。

TB定义为强度为I0的一般(非黑体)热辐射,计算公式为[1]:

TB=λ2KBI0 。

热辐射体的亮度温度TB与其发射率e和真实温度T线性相关[1],公式为:

TB=eT 。

雷达测量地球表面反向散射的电磁波功率EM。表面的反射特性通过后向散射系数σ°表示,σ°是发射功率Pe、反射功率Pr、天线特性和被照射区域特性的函数,可定义为[1]:

σ°=(4π)3R4λ2LeLrGeGrPrPeSeff

式中: R为传感器到地球表面的距离; Ge,Gr分别为天线在发射和接收处的增益; Le,Lr分别为天线在发射和接收处的损耗; Seff为有效地表。

1.1.1 基于卫星的被动观测

VOD是植被介电特性的函数,主要响应于包括绿色和非绿色成分的地上植被总含水量,随植物结构以及传感器波长和观测角度而变化[4]。基于卫星的被动微波观测的TB主要由3部分组成: ①被覆盖植被衰减的土层辐射; ②植被向上辐射; ③来自植被的向下辐射,被土层向上反射,再次被植被衰减[20]。计算公式为:

TBP=TSerPΓP+1-ωPTC1-ΓP+1-erP1-ωPTC1-ΓPΓP

式中: P为极化方式(水平和垂直); TSer为土壤温度; TC为冠层温度; ω为单次散射反照率; er为土壤发射率,由土壤湿度、温度和粗糙度决定; Γ为VOD和观测入射角μ确定的植被透过率[14]。Γ计算公式为:

Γ=exp(-VOD/cosμ) 。

土地参数反演模型(land parameter retrieval model,LPRM)[21]基于上述辐射传输模型,在单次散射反照率恒定、冠层表面温度等于夜间土壤表面温度、水平极化和垂直极化植被参数相等、表面粗糙度值固定的假设下,可以同时获取土壤湿度和VOD。LPRM可以应用于不同卫星的不同微波波长和视角的观测。因此,VOD数据集来源于不同卫星上的一系列被动微波传感器的观测,主要包括国防气象卫星计划的特殊传感器微波成像仪(SSM/I)、Aqua卫星上的先进微波扫描辐射计-地球观测系统(AMSR-E)和WindSat的辐射计[14,17]。

1.1.2 基于卫星的主动观测

水云模型(water cloud model,WCM)是一种半经验模型,用于模拟植被和裸露土壤地表的雷达后向散射信号[5]。同时,WCM是一种从雷达数据中获取VOD产品的有效工具,该模型的配置在大尺度下的计算仍较高效,并提供简单的分析解决方案[22]。在WCM中,植被-土壤表面反射的总后向散射σ°obs被分解为植被的直接后向散射σ°vege、植被和土壤之间的双反弹后向散射σvege+soil°和植被冠层衰减后的土壤表面直接后向散射σsoil°[5]。植被的衰减效应由植被透射率γ2参数化,该透射率可以根据入射角θ(一般取40°)计算。VOD的相关计算公式[5,23]分别为:

σobs°=σvege°+γ2σsoil°+σvege+soil°
σvege°=AV1cosθ(1-γ2)
σsoil(dB)°=10lgσsoil°=C+D·SM
γ2=exp(-2VOD/cosθ)

式中: A为全覆盖植被冠层后向散射[24]; V1指数一般可设为1[5]; C为极干燥条件下的雷达后向散射; D为雷达数据对土壤湿度的敏感性参数; SM为土壤湿度; σsoil(dB)° 为土壤反射散射,dB。

根据Baghdadi等[25]和Zribi等[26]的进一步推导,可忽略σ°vege+soil项,VOD可表示为:

VOD=-12cosθlnσobs°-Acosθ100.1(C+D·SM)-Acosθ

式中参数A,C和D必须对全球或区域的每个像素进行校准。即在反演VOD之前,需要先对WCM模型进行校准后才能获得A,C和D参数。一般是首先通过选择可以忽略植被影响的空间/时间条件对土壤参数(C和D)进行校准,然后对植被参数(A)进行校准。具体校准方法可参考Liu等[5,23]的相关研究。

当植被非常密集时,可以假设γ2= 0,式(7)和式(8)可以简化为:

σobs°=σvege°=Acosθ 。

植被非常密集条件下的A值(记作A0)可以简单的计算为[5]:

A0=σobs°/cosθ 。

1.2 全球主要的VOD产品

20世纪70年代末以来,基于微波遥感观测反演的大量VOD产品提供了地表植被动态的长期记录,如表1所示。VOD产品可在不同频段进行反演,如L(1~2 GHz),C(4~8 GHz),X(8~12 GHz),K(18~26.5 GHz)[1]。此外,VOD产品也来自多个具有高时间分辨率(1~3 d)的传感器,如C波段的ASCAT[5,27],X波段的AMSR-E和AMSR-2[6-7],以及L波段的SMOS [8]和SMAP[9]。在这些卫星传感器中,AMSR-2继承了AMSR-E传感器,继续提供AMSR-E类似的观测数据,融合2种传感器观测数据成功反演了第一个长时序(1987—2008年)全球微波VOD产品[12]。ESA的SMOS传感器和NASA的SMAP传感器,主要目的是监测植被中等和密集地区的地表SM[28]。但是,采用辐射传输模型准确反演SM时,需要考虑植被层由VOD参数化的消光效应[20]。特别是,SMOS卫星具有多角度能力,可以协同反演SM和VOD[29],同时为SMAP提供了多时相VOD反演算法[30]。因此,SMOS和SMAP都可以用于反演SM和VOD产品[31]。

表1   全球主要的VOD产品介绍

Tab.1  Introduction information of major VOD products

产品传感器频段/GHz空间分辨率时间分辨率有效期间参考文献
LPDR Version 2AMSR-E10.6525 km逐日2002/01—2011/12[32]
AMSR210.6525 km逐日2012/05—今
LPRM Version 5SSMR6.63,10.6925 km逐日1978/10—1995/02[12]
SSM/I19.3525 km逐日1987/06—今
TMI10.65,19.3545 km逐日1997/12—2015/04
AMSR-E6.925,10.65,18.738, 56 km逐日2002/06—2011/10
WindSat6.8,10.7,18.725 km逐日2003/01—2012/07
AMSR26.925,7.30,10.56,18.731, 46 km逐日2012/05—今
VODCA LPRM
Version 6		SSM/I19.350.25°逐日1987/06—今[3]
TMI10.65,19.350.25°逐日1997/12—2015/04
AMSR-E6.925,7.30,10.65,18.70.25°逐日2002/06—2011/10
WindSat6.8,10.7,18.70.25°逐日2003/01—今
AMSR26.925,7.30,10.65,18.70.25°逐日2012/05—2019/12
SMOS L2SMOS1.425 km逐日2010/01—今[33]
SMOS L3SMOS1.425 km逐日2010/01—今[35]
SMOS-ICSMOS1.425 km逐日2010/01—今[8]
L2_SM_PSMAP1.41336 km逐日2015/02—今[36]
L2_SM_P_ESMAP1.4139 km逐日2015/02—今[37]
MT-DCASMAP1.4139 km逐日2015/02—今[30]
ASCAT TUWASCAT5.25525 km逐日2006/10—今[27]

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由于不同传感器所使用的微波频率、测量入射角、轨道特性、辐射质量和空间足迹存在差异性,导致以上这些传感器具有不同的寿命和特性,使联合多种基于不同传感器反演的VOD产品研究其长期动态变化变得比较困难[3]。为了解决这一问题,Liu等[12]在2011年研发了一个协调多传感器的长时序(1987—2008年)VOD数据集,从此VOD长期动态变化研究方面取得了重大进展。最近,通过融合多个微波传感器已经研发了一系列长时序VOD产品, Moesinger等[3]将SSM/I,TMI,AMSR-E,WindSat和AMSR2的VOD观测数据融合到全球长期VOD产品中,研发了0.25°空间分辨率的1987—2018年全球VOD覆盖。Moesinger等[34]将SSM/I,TMI,AMSR-E,WindSat和AMSR2衍生的C、X和Ku波段VOD以概率方式进行融合,得到了1987年至今标准化的VOD指数。

2 基于VOD的植被动态监测研究进展

植被冠层吸收和散射土壤辐射,并将自身的辐射增加到总辐射通量中,因此植被覆盖对辐射计测量的亮度温度有强烈影响[38]。同样,植被冠层介电常数也影响雷达后向散射观测,而植被介电常数取决于其含水量、散射体的大小、形状和方向分布、冠层覆盖的几何形状(即方向、间距、覆盖度等)[39-40]。被动微波和主动微波观测都广泛用于估算VWC[41⇓-43]和AGB [38,44]。VOD代表植被层不透明性的量度,通过微波辐射传输模型反演得到,用于衡量地表植被对地表上行微波辐射的衰减作用大小[1]。

VOD与NDVI、增强型植被指数(enhanced vegetation index,EVI)、归一化差异水分指数(normalized difference water index,NDWI)和叶面积指数(leaf area index,LAI)等植被“绿度”光学遥感指标相关,因此也与植物生产力相关[45]。同时,VOD为常用的多光谱图像植被指数如NDVI,LAI或光合有效辐射比率(fraction of absorbed photosynthetically active radiation,FPAR)提供补充信息[1]。与光学植被指数相比,VOD在植被监测方面最大的优势在于不受云雨雾天气的干扰,对大气中的水不太敏感,能够在云层覆盖的情况下进行反演[12]。光学植被指数通常反映植被冠层顶部的状况; 而由于微波具有较强的穿透能力,VOD不仅代表叶片的变化状况,也包含了树干的含水量及结构的信息。此外,VOD饱和度较低,对高生物量具有更高的敏感性[5]。因此,VOD作为一项极具应用前景的生态指标[1,22],被广泛应用于植被特征监测(如VWC和AGB)[46-47]、干旱监测[48]、物候分析[49]、碳平衡分析[50]以及估算野火发生的可能性[51-52]等。

2.1 全球和区域尺度植被特征监测

微波辐射衰减量取决于各种因素,例如植被的密度、类型和含水量以及传感器的波长等[53]。基于不同波长微波反演的VOD对冠层生物量的敏感性不一致[3]。短波比长波受到植被的衰减更大,基于较长波长微波反演的VOD对较大的植被生物量和较深的植被层更敏感,而基于短波微波反演的VOD对叶片水分含量更敏感[54]。VOD随VWC的增加而增加[4],因此与AGB[5]及其相对含水量(relative water content, RWC)[55]有关。即VOD的变化既取决于VWC和冠层结构,又与AGB和RWC成正比[31]。例如,近年来,L波段(1.4 GHz)VOD已被确定为估算热带森林AGB动态变化的有效指标。之所以能够做到这一点,是因为冠层内低频辐射的消光率较低,使得L波段可以更有效地监测密集植被冠层中的生物量[50,56]。

VOD由植被数量(由AGB参数化)和植被水分状况Mg(由VWC参数化,即Mg=VWC/(VWC + Bs),其中Bs表示植被干生物量)决定,因此VOD可以提供关于AGB,VWC以及植被冠层应力的信息[1,31,49]。Wigneron等[57]的实验研究揭示了VOD与VWC之间的线性关系; Jackson等[4]建立了VOD和VWC随微波频率和植被结构变化的半经验关系。研究发现,VOD与AGB和VWC随微波频率呈一般幂函数响应关系[1]。假设Mg的年平均值每年相对恒定,在未受严重干旱/死亡事件影响的完整森林地区,VOD的年平均值被认为是AGB的一个很好的替代指标[14,17,31]。Liu等[17]建立了AGB与Ku/X/C-VOD产品之间的非线性关系[58],并利用这一关系研究了基于VOD估算的全球生物量动态变化。随后,Tian等[14]通过时间原位生物量测量,证实了西非萨赫勒旱地生态系统AGB与Ku/C-VOD之间的强相关关系。Rodríguez-Fernández等[59]对SMOS L-VOD产品与非洲大陆4张AGB基准地图的空间模式进行了比较分析,证明了L-VOD产品的优势。接着,Li等[31]为了评价X,C和L波段9种VOD产品对AGB的监测能力,比较了VOD与地上碳密度之间的空间相关性。该研究证明了VOD与AGB之间存在明显的非线性饱和关系,L-VOD,X-VOD和C-VOD与AGB存在高度空间相关性; 由于微波辐射的穿透能力随着频率的降低而提高,VOD与AGB的空间相关性随频率的降低而增强。

2.2 碳平衡分析

植被变化在地球碳收支及其与人为和自然气候变化相关的辐射强迫中起着关键作用[60],对全球地上生物量碳储量(above ground carbon, AGC)的估算至关重要。Liu等[17]基于卫星被动微波观测的VOD,估算了全球森林和非森林生物群落的AGC,发现1998—2002年期间全球平均AGC为362 Pg,其中65%在森林,17%在草原。1993—2012年期间,全球AGC损失估计为-0.07 Pg/a,主要原因是热带森林的损失(-0.26 Pg/a)、寒带和温带混交林(+0.13 Pg/a)以及热带草原和灌丛(+0.05 Pg/a)的净增加。年际AGC变化模式很大程度上受到水资源限制以及对降雨变化响应强烈的生态系统的影响,特别是草原。在澳大利亚北部和非洲南部的草原,与湿润条件相关的AGC增加逆转了全球AGC的损失,导致总体收益,与最近研究发现的全球碳汇趋势一致。Brandt等[50]基于低频被动微波VOD数据集(L-VOD),量化了2010—2016年间撒哈拉以南非洲地区每年的地面木质碳变化,证明了L-VOD在监测气候变化引起的碳损益动态方面的适用性。Teubner等[45]提出了一种采用VOD估计总初级生产力(gross primary productivity,GPP)的模型,由于该模型是通过VOD观察到的植被生物量驱动,是一种基碳汇驱动的方法来量化GPP的模型,在概念上不同于常见的源驱动方法。

2.3 干旱监测

受全球气候变化和人类活动等因素的影响,近年来极端气候事件(如干旱、洪涝、高温热浪和低温冷害等)频发且强度增强,严重威胁农业生产、水资源、生态系统和社会经济[61]。在这些极端气候事件中,干旱事件是持续时间最长,破坏性最强且最不易监测的极端气候事件之一[62],因此大范围、连续性的干旱监测至关重要。基于可见光/近红外波段的冠层反射率提供的植被生长信息,包括NDVI和EVI等作为植被健康和密度的指标,被广泛应用于干旱及植被监测[63]。然而,NDVI受到大气(如云和气溶胶)的影响严重,仅限于冠层顶部监测。此外,NDVI在叶面积覆盖情况下容易达到饱和,难以准确监测植被的变化信息[64]。这些因素限制了光学遥感的应用,特别是在常年受云雨天气影响或植被密集地区。而微波遥感具有极强穿透性,可基于微波遥感估算AGB的木质和叶状元素[11],为干旱监测提供了一种新的手段。

Zhou等[65]表明微波VOD比光学植被指数对刚果雨林干旱退化更敏感。为了进一步比较微波与光学遥感,Afshar等[48]比较了微波L波段VOD与光学NDVI在中欧地区农业干旱探测中的响应差异; Jiang等[66]基于光学和微波遥感植被产品,采用标准化降水蒸散发指数(standardized precipitation evapotranspiration index, SPEI)和VOD评价了弹性退化森林的空间分布和时间变化,发现了中国西南地区森林恢复力的降低。为了更好地了解植被动态变化和对干旱的响应,有必要探索NDVI和VOD之间的协同作用,因为综合的植被指数将提供这2个指数的互补信息。Lawal等[67]检验了一种基于NDVI和VOD的综合植被指数Nvod在监测植被动态和干旱响应方面的能力和有效性。

2.4 物候监测

植被物候是反映植被动态变化的重要指标和气候变化对生态系统影响的重要感应器[68]。近几十年来,气候变化通过全球变暖和降雨季节性改变深刻影响了全球植被物候[69],物候期的改变也通过生物物理反馈影响气候[70-71]。长期以来,光学和近红外遥感对植被冠层覆盖的敏感性一直被用于全球植被物候监测[11]。光学植被指数包括NDVI和EVI,利用了绿色植被在红色和近红外波段之间的反射率差异,对植被冠层覆盖包括LAI在内的相关指标很敏感[72]。全球植被物候监测的另一种数据来源于卫星微波遥感,微波遥感对植被AGB敏感,对太阳照明和大气效应造成的信号衰减相对不敏感,同时提供几乎每天的全球覆盖。与光学遥感相比,微波遥感VOD对大气扰动的敏感性较低,VOD与植被含水量呈线性相关[4],并且对叶状和木本植被成分都敏感[73],可测量不同的植被特征和功能。因此,VOD为研究地表物候提供了一个独立和互补的数据源[74]。

Jones等[11]利用VOD数据分析了2003—2008年6 a间的全球物候周期变化,发现VOD与植被物候期具有较强的相关关系,结合光学、红外遥感的植被指数和VOD,有助于更全面地了解植被物候期动态变化。Chaparro等[75]量化了不同的VOD监测不同阶段作物生长状态的能力,从而估算作物产量。发现VOD捕捉了生长和成熟阶段作物50%~70%的年际变化,并解释了最终作物产量的44%(在异种作物地区)~74%(在同种作物地区)的信息,证明VOD在作物物候监测和作物产量预测方面具有巨大潜力。Tong等[74]利用基于被动微波VOD和光学遥感AVHRR NDVI数据,分析了1992—2012年热带干旱区(25°N~25°S)地表物候变化趋势。

3 讨论与展望

首先,介绍了被动微波和主动微波反演VOD的主要原理; 然后,对比分析不同传感器VOD产品的主要特征,总结VOD微波遥感反演发展历程; 最后,着重综述当前VOD在植被动态监测应用方面的研究成果,从植被特征监测(如VWC和AGB)、碳平衡分析、干旱监测、物候分析等方面进行梳理和总结。虽然微波遥感的VOD产品在反演和应用研究方面取得了丰硕成果,但是仍有许多方面值得进一步研究与探索。

3.1 VOD产品的优缺点

与基于可见光和红外遥感的植被指数相比,VOD不受云雨雾天气影响,对大气中水不敏感,具有较高的时间分辨率和较长的可用性,可用于监测植被的短期和长期变化。VOD提供了与NDVI或LAI有关植被动态的补充信息,可以补充现有的光学指标,在监测植被状况方面具有较大潜力。

然而,目前反演的全球VOD产品的空间分辨率较粗(约25 km),限制了其在区域尺度(1~5 km)的农业管理和植被动态监测中的应用[22]。VOD产品每个网格单元所包含的面积较大,通常包含各种土地覆盖类型。在VOD像元内,微波信号受到植被冠层结构、地表粗糙度和VWC等的强烈影响,导致VOD具有较大的不确定性。因此,提高VOD产品的空间分辨率对于许多应用和研究领域越来越重要,并有潜力填补光学植被指数的应用短板。最近,Zhou等[22]改进ASCAT-IB算法,利用黑河流域草地C波段Sentinel-1后向散射数据成功反演了2018—2020年期间草地1 km空间分辨率的VOD。

3.2 VOD星载传感器的发展

最近许多的被动(如SMAP和 AMSR2)和主动(如ASCAT和SCAT)微波传感器仍在轨正常工作,未来几年用于反演VOD的观测数据源有了一定的保障。例如,欧洲哥白尼扩展计划的首要任务之一是哥白尼成像微波辐射计(CIMR)任务,该辐射计首次在L(1.4 GHz),C(6.9 GHz),X(10.65 GHz),K(18.7 GHz)和Ka(36.5 GHz)频段工作,相应的空间分辨率分别为55 km,15 km,5 km和5 km[76],将有可能反演低频和高频VOD,以联合分析AGB和VWC的时间变化[1]。然而,国产卫星传感器在反演微波遥感产品方面还较落后,反演VOD的数据源主要来源于国外卫星传感器。同时,国内的关于VOD应用研究还比较欠缺,需要加强VOD数据的挖掘与应用探索研究。星载传感器可用的不同频率和不同传感模式观测并反演大量的植被变量,如较高频率的冠层水分状况和较低频率的生物量。然而,需要进一步探索不同频率和传感模式(主动和被动)的观测之间的协同作用,以更好地理解气候变化和人为强迫增加背景下植被生态系统的动态变化。此外,研究长期VOD动态变化通常受到单个微波传感器覆盖的相对较短的时间跨度的阻碍,如何进一步实现不同卫星传感器VOD数据的同化,提高VOD遥感监测能力仍是待解决的重要科学问题。

3.3 VOD在全球植被动态监测的应用方向

VOD作为一种基于微波的AGB和VWC估算方法,越来越多地被用于研究全球气候和环境变化对植被的影响。尽管大量研究表明VOD可用于对全球和区域尺度陆地生态系统的植被特征、干旱、物候、GPP、碳平衡等的监测,但VOD和植被动态变化特征之间在不同时间和空间尺度上的机理联系仍然不清楚。如AGB和VWC对VOD影响的耦合机理过程需要进一步研究。此外,当前有光学、热红外、微波等不同的遥感手段,可反映植被生长不同方面的信息[77],如NDVI反映植被的绿度[78]、叶绿素荧光与光合作用有关[79]、热红外可反演植被地表温度和蒸散发[80]、而微波可反映植被AGB和VWC[81]。然而,用于监测植被动态变化的不同遥感数据的共同/独特信息尚不明晰,如何进一步集成不同光谱范围的卫星遥感数据,以提高植被动态监测能力也值得深入研究。

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DOI:10.1016/j.rse.2017.08.025      PMID:32943797      [本文引用: 1]

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones. Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAP/Sentinel-1 Active-Passive Soil Moisture Product in April 2017. This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (T) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated T data then go through various correction/calibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (LiC_TB_E). The LiC_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and data were performed for the duration of April 1, 2015 - October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m/m unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized T channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

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Teubner I E, Forkel M, Camps-Valls G, et al.

A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

[J]. Remote Sensing of Environment, 2019, 229:100-113.

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Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems.

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[J]. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(24):9899-9904.

DOI:10.1073/pnas.1019576108      PMID:21628575      [本文引用: 1]

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a "benchmark" map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ± 6% to ± 53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ± 5% and ca. ± 1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Rodríguez-Fernández J, Mialon A, Mermoz S, et al.

An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets:High sensitivity of L-VOD to above-ground biomass in Africa

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[J]. Philosophical Transactions of the Royal Society B-Biological Sciences, 2012, 367(1588):601-612.

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Savannahs are a mixture of trees and grasses often occurring as alternate states to closed forests. Savannah fires are frequent where grass productivity is high in the wet season. Fires help maintain grassy vegetation where the climate is suitable for woodlands or forests. Saplings in savannahs are particularly vulnerable to topkill of above-ground biomass. Larger trees are more fire-resistant and suffer little damage when burnt. Recruitment to large mature tree size classes depends on sapling growth rates to fire-resistant sizes and the time between fires. Carbon dioxide (CO(2)) can influence the growth rate of juvenile plants, thereby affecting tree recruitment and the conversion of open savannahs to woodlands. Trees have increased in many savannahs throughout the world, whereas some humid savannahs are being invaded by forests. CO(2) has been implicated in this woody increase but attribution to global drivers has been controversial where changes in grazing and fire have also occurred. We report on diverse tests of the magnitude of CO(2) effects on both ancient and modern ecosystems with a particular focus on African savannahs. Large increases in trees of mesic savannahs in the region cannot easily be explained by land use change but are consistent with experimental and simulation studies of CO(2) effects. Changes in arid savannahs seem less obviously linked to CO(2) effects and may be driven more by overgrazing. Large-scale shifts in the tree-grass balance in the past and the future need to be better understood. They not only have major impacts on the ecology of grassy ecosystems but also on Earth-atmosphere linkages and the global carbon cycle in ways that are still being discovered.

Yu H Q, Zhang Q, Sun P, et al.

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[J]. Global Change Biology, 2014, 20(11):3457-3470.

DOI:10.1111/gcb.12625      PMID:24797086      [本文引用: 1]

Land Surface Phenology (LSP) is the most direct representation of intra-annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing land-surface fluxes, and is central to accurately parameterizing terrestrial biosphere-atmosphere interactions, as well as climate models. In this article, we present an evaluation of Pan-European LSP and its changes over the past 30 years, using the longest continuous record of Normalized Difference Vegetation Index (NDVI) available to date in combination with a landscape-based aggregation scheme. We used indicators of Start-Of-Season, End-Of-Season and Growing Season Length (SOS, EOS and GSL, respectively) for the period 1982-2011 to test for temporal trends in activity of terrestrial vegetation and their spatial distribution. We aggregated pixels into ecologically representative spatial units using the European Landscape Classification (LANMAP) and assessed the relative contribution of spring and autumn phenology. GSL increased significantly by 18-24 days decade(-1) over 18-30% of the land area of Europe, depending on methodology. This trend varied extensively within and between climatic zones and landscape classes. The areas of greatest growing-season lengthening were the Continental and Boreal zones, with hotspots concentrated in southern Fennoscandia, Western Russia and pockets of continental Europe. For the Atlantic and Steppic zones, we found an average shortening of the growing season with hotspots in Western France, the Po valley, and around the Caspian Sea. In many zones, changes in the NDVI-derived end-of-season contributed more to the GSL trend than changes in spring green-up, resulting in asymmetric trends. This underlines the importance of investigating senescence and its underlying processes more closely as a driver of LSP and global change. © 2014 John Wiley & Sons Ltd.

Tian F, Brandt M, Liu Y Y, et al.

Mapping gains and losses in woody vegetation across global tropical drylands

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Woody vegetation in global tropical drylands is of significant importance for both the interannual variability of the carbon cycle and local livelihoods. Satellite observations over the past decades provide a unique way to assess the vegetation long-term dynamics across biomes worldwide. Yet, the actual changes in the woody vegetation are always hidden by interannual fluctuations of the leaf density, because the most widely used remote sensing data are primarily related to the photosynthetically active vegetation components. Here, we quantify the temporal trends of the nonphotosynthetic woody components (i.e., stems and branches) in global tropical drylands during 2000-2012 using the vegetation optical depth (VOD), retrieved from passive microwave observations. This is achieved by a novel method focusing on the dry season period to minimize the influence of herbaceous vegetation and using MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to remove the interannual fluctuations of the woody leaf component. We revealed significant trends (P < 0.05) in the woody component (VOD) in 35% of the areas characterized by a nonsignificant trend in the leaf component (VOD modeled from NDVI), indicating pronounced gradual growth/decline in woody vegetation not captured by traditional assessments. The method is validated using a unique record of ground measurements from the semiarid Sahel and shows a strong agreement between changes in VOD and changes in ground observed woody cover (r  = 0.78). Reliability of the obtained woody component trends is also supported by a review of relevant literatures for eight hot spot regions of change. The proposed approach is expected to contribute to an improved assessment of, for example, changes in dryland carbon pools.© 2016 John Wiley & Sons Ltd.

Tong X, Tian F, Brandt M, et al.

Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992—2012

[J]. Remote Sensing of Environment, 2019, 232:111307.

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Chaparro D, Piles M, Vall-Ilossera M, et al.

L-band vegetation optical depth for crop phenology monitoring and crop yield assessment

[C]// IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2018:8225-8227.

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Kilic L, Prigent C, Aires F, et al.

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[J]. Journal of Geophysical Research-Oceans, 2018, 123(10):7564-7580.

[本文引用: 1]

张菊, 房世波, 刘汉湖.

基于微波数据与光学数据集成的机器学习技术在作物产量估算中的应用

[J]. 地球信息科学学报, 2021, 23(6):1082-1091.

DOI:10.12082/dqxxkx.2021.200413      [本文引用: 1]

各类光学植被指数已成功地应用于各种植被监测与作物产量估算中,但这些指数易受大气状况的影响。由星载微波辐射计得到的植被光学厚度数据（VOD）与植被密度、含水量密切相关,数据可全天候获得,在农业遥感监测中呈现着巨大的潜力。作为来自不同传感器的遥感数据,微波遥感数据与光学遥感数据可以提供不同波长范围内的植被信息。为了更准确地进行作物产量估算,本研究提出将微波遥感数据与光学遥感数据共同应用于冬小麦单产估算中。研究选择L波段微波辐射计SMAP卫星的VOD数据与MODIS的标准归一化植被指数NDVI、增强型植被指数EVI、叶面积指数LAI、光合有效辐射分量FPAR数据作为研究变量,分别使用BP神经网络、GA-BP神经网络和PSO-BP神经网络建立冬小麦产量估算模型。结果表明： 3种神经网络回归模型的P值均小于0.001,通过了显著性检验。GA-BP神经网络回归模型的估算值与真实值在3种神经网络回归模型中表现了最高的相关性（R=0.755）与最低的均方根误差（RMSE=529.145 kg/hm2）,平均绝对误差(MAE=425.168 kg/hm2)和平均相对误差（MRE=6.530%）。为了分析多源遥感数据的结合在作物产量估算中的优势,研究同时构建了仅使用NDVI和LAI,使用NDVI、EVI、LAI、FPAR等光学数据进行冬小麦产量估算的3种GA-BP神经网络回归模型作为对比。结果表明,使用微波遥感数据与光学遥感数建立的GA-BP神经网络回归模型较上述3种作为对比的GA-BP神经网络回归模型的相关系数R值分别提高了0.163,0.229与0.056,均方根误差RMSE分别降低了122.334、158.462和46.923 kg/hm2,使用多源遥感数据的组合可以很好地提高作物产量估算的准确性。

Zhang J, Fang S B, Liu H H.

Machine learning approach for estimation of crop yield combining use of optical and microwave remote sensing data

[J]. Journal of GeoInformation Science, 2021, 23(6):1082-1091.

[本文引用: 1]

竞霞, 白宗璠, 张腾, 等.

3FLD和反射率荧光指数估测小麦条锈病病情严重度的对比分析

[J]. 中国农机化学报, 2019, 40(11):136-142.

DOI:10.13733/j.jcam.issn.2095-5553.2019.11.22      [本文引用: 1]

为研究小麦条锈病病情严重度和日光诱导叶绿素荧光强度的关系,确定适合于探测小麦条锈病病情严重度的叶绿素荧光因子。本文分别利用3FLD(three bands Fraunhofer Line Discrimination)和反射率指数2种方法提取了日光诱导叶绿素荧光强度,对比分析了这2种方法估测的日光诱导叶绿素荧光强度在小麦条锈病病情严重度遥感探测中的应用潜力。利用3FLD方法计算的O2-A和O2-B波段叶绿素荧光强度与小麦条锈病病情严重度均达到了极显著相关,复相关系数分别为0.677 2和0.492 4。基于反射率指数估测日光诱导叶绿素荧光时,叶绿素荧光反射率比值指数R_(740)/R_(720)、R_(440)/R_(690)、R_(740)/R_(800)以及叶绿素荧光一阶导数光谱指数D_(705)/D_(722)、D_(730)/D_(706)与小麦条锈病病情严重度均达到了极显著相关,尤其是比值指数R_(440)/R_(690)与小麦条锈病病情指数的相关性最高,复相关系数达到了0.718 7。基于辐亮度的3FLD算法和基于反射率的叶绿素荧光比值指数2种方法提取的叶绿素荧光强度均能够实现小麦条锈病病情严重度的遥感探测,但利用反射率方法提取的日光诱导叶绿素荧光强度构建的小麦条锈病病情严重度估测模型优于3FLD算法,更适合小麦条锈病病情严重度的遥感探测。论文的研究结果为基于卫星平台的叶绿素荧光遥感探测小麦条锈病提供了重要的理论依据。

Jing X, Bai Z F, Zhang T, et al.

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干旱遥感监测技术进展

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