周磊
, 武建军, 张洁
ZHOU Lei, WU Jian-jun, ZHANG Jie
中图分类号: X43
文献标识码: A
文章编号: 1000-0690(2015)05-0630-07
通讯作者:
收稿日期: 2014-01-2
修回日期: 2014-06-20
网络出版日期: 2015-05-20
版权声明: 2015 《地理科学》编辑部 本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
基金资助:
作者简介:
作者简介：周 磊（1983-）,男,山东费县人,博士,高级工程师,主要从事地表过程与灾害遥感、环境质量评价研究。E-mail:zhoulei8341@163.com
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摘要
总结了目前广泛应用的气象监测模型和基于遥感数据的干旱监测模型,将目前的遥感监测方法分为植被状态监测方法、微波土壤水分监测方法、热红外遥感监测方法和基于能量平衡的遥感监测方法进行综述,深入分析了基于遥感数据的监测方法的特点、适用条件和存在的问题。通过综述基于多源数据的干旱综合监测模型,对未来干旱监测方法的发展方向进行研究和探讨,指出集成多源数据的干旱综合监测模型是解决复杂的干旱监测问题的新方法。
关键词：
Abstract
Drought is a serious natural disaster. It is doing increasingly damage to the human environment as the drought events occur more frequently. Real-time and effective drought monitoring is an effective means to reduce the losses caused by drought. Since the beginning of 20th century, a lot of drought indices have been developed for monitoring the occurrence and variation of drought. Drought is a complex natural disaster. However, each drought index has its own advantages and weaknesses in drought monitoring. Almost all the drought indices are based on specific geographical and temporal scales; it is difficult to spread its applicability all over the world. Because of the meteorological drought indices using discrete, point-based meteorological measurements collected at weather station locations, the results have restricted level of spatial precision for monitoring drought patterns. Remote sensing technology provides alternative data for operational drought monitoring, with advanced temporal and spatial characteristics. However, additional information still needs to be incorporated so as to thoroughly explain the anomaly in vegetation caused by drought. Besides, to achieve a more accurate description of drought characteristics, drought intensity differences caused by vegetation type, temperature, elevation, manmade irrigation, and other factors under the same water condition must be considered. Therefore, effective drought monitoring indicator should both reflect soil moisture, vegetation condition and take into account vegetation type, temperature, and man-made factors leading to regional drought differences. Aiming at the problem mentioned above, the satellite based drought indices, and integrated meteorological and remote sensed drought indices was reviewed in our research. Firstly, this paper summarized the widely used drought monitoring models which were based on remote sensing data. The remote sensing drought monitoring approach was summarized by dividing it into four classes i.e. vegetation condition monitoring methods, microwave soil moisture monitoring methods, thermal infrared remote sensing monitoring methods and indices based on energy balance theory. The characteristics, application conditions and problems of the monitoring method which were based on remote sensing data and multi-source data (meteorological data, remote sensing data and biophysical data) were deeply analyzed. Then, the future development direction of drought monitoring model was studied and discussed by concluding comprehensive drought monitoring model which was based on multi-source data. Integrated multi-source data to construct comprehensive drought monitoring model was pointed out as a new approach to solve complex problems of drought monitoring. It can solve the inconsistency problems of space and temporal resolution from different data types. But the present study concluded that research on this area is still in the experimental and exploratory stage and need further improvement and development.
Keywords：
干旱是影响最为严重的自然灾害之一,全球平均每年因干旱造成的损失为60~80亿美元,有超过一半的地区受到干旱的影响[1]。农业受干旱影响最为直接、最为严重[2]。以中国为例,1949~2006年全国平均每年受旱面积2 122万hm2[3],而且近年来干旱发生的次数逐年增加,尤其是西南部地区的极端干旱事件频发[4,5]。实时而有效的监测干旱是降低干旱影响,减少农业损失的主要途径。
干旱的发生有着极其复杂的机理,在受到各种自然因素如降水、温度、地形等影响的同时也受到人为因素的影响,如作物布局、作物品种及生长状况等。有效的干旱监测不仅需要反映土壤水分、植被的生长状态,而且应该考虑植被类型、温度、地形以及人为因素的影响,建立基于多源数据的干旱综合监测方案。本文针对以上问题和现状,综述基于气象数据的干旱监测模型、基于遥感数据的干旱监测模型和干旱综合监测模型的原理和特点,为干旱监测方法的发展和完善提供支持。
在植被遥感中,发展较早而且应用最为广泛的是归一化植被指数（Normalized Difference Vegetation Index, NDVI）,已成为植被生长状态及植被覆盖度的最佳指示因子,被广泛用来估算植被生物量和评估环境状况[6]。目前,很多干旱监测遥感指数都是以NDVI为基础发展而来的。
与NDVI的原理类似,用于监测植被状态的指数还包括：比值植被指数（RVI）、绿度植被指数（GVI）和垂直植被指数（PVI）以及以此原理为基础发展起来的其他多个指数。植被状态指数具有明显的地域性和时效性,受植被本身、大气、环境等条件的影响。而且土壤背景对用于计算植被指数的反射率具有不同的程度的影响,尤其是植被覆盖率中等偏下的情况。因此,基于NDVI大量观测数据提出了土壤调节植被指数（SAVI）用于减少土壤背景的影响。基于SAVI发展了多种新的指数,包括：TSAVI、ATSAVI、MSAVI、SAVI2、SAVI3、SAVI4等[7]。总的来说,这些指数均受土壤背景的影响大,植被非完全覆盖时,土壤背景影响较大,在干旱监测中的应用并不广泛。
植被生长状况的变化是干旱的重要特征,当光照、温度条件变化不大时,植被生长状况主要与水分有关。因此可以用植被健康状况的差异反应干旱的程度,距平植被指数（Anomaly Vegetation Index,AVI）即是通过与历史多年月平均植被指数的差异判断植被的长势和干旱的程度。同样利用多年累积的植被指数数据,Kogan提出了植被状态指数（Vegetation Condition Index,VCI）并在以后的干旱监测研究中进行了多次应用。该指数对当前植被指数值和历史平均的植被指数值的差异进行了归一化[8]。由于VCI在干旱监测中的有效性,基于VCI发展了其他更加综合的干旱监测模型比如综合时间的植被状态指数（Time-integrated vegetation condition index, TIVCI）也在干旱监测中被成功应用,并和气象干旱指数具有较好的相关性[9]。
基于历史累积植被指数的遥感监测方法对同一种植被类型而言,在不同的地区有了一定的可比性,但是都属于定性的描述,很难定量划分干旱的等级,因为值的变化随地形地貌、植被类型、气候区等因素的不同而发生变化;同时,模型计算结果随着时间序列的变化而发生变化。一般来讲,该类指数对于植被覆盖率高,并且是生长季内的干旱效果较好,而裸土和其他时段的干旱监测可能导致不真实的结果,同时不适合小尺度的干旱监测。该类指数未考虑植被状态的变化是由降水还是其他因素导致,地表类型的年际变化可能使监测的结果失真。
微波遥感土壤水分反演方法具有较好的物理基础,土壤的介电特性和土壤的水分含量之间有着密切的关系,介电常数随着土壤水分的增加而迅速增大[10]。根据方式的不同,可以分为被动微波遥感和主动微波遥感两种方法。微波遥感监测土壤水分具有全天时全天候的特点,对土壤水分敏感,对地表植被具有一定的穿透能力,是大区域土壤水分监测的有效手段[11]。
被动微波遥感监测土壤水分,主要依赖微波辐射计对土壤本身的微波发射或者亮温进行测量,该方法可以有效反演植被水分含量和2~5 cm深度的土壤水分,低频波段对植被水分含量敏感,而使用L波段（波长21 cm）对土壤水分的研究最佳。被动微波遥感在大区域土壤水分监测中发挥了重要作用,一系列的机载、星载被动微波辐射计提供了大尺度空间土壤水分数据[12],包括：多通道扫描微波辐射计SMMR,微波辐射计成像仪SSM/I,TRMM,和地球观测系统（EOS）的Terra和Aqua卫星所搭载的高级微波辐射计AMSR-E。基于微波遥感获取的降水量、土壤湿度、和地表温度数据,综合的微波遥感干旱指数如Microwave Integrated Drought Index （MIDI）也被开发并成功的用于干旱监测[10]。被动微波遥感反演土壤水分的关键问题是空间分辨率较低,在小区域的应用受到很大的限制,而且目前尚缺少大尺度微波辐射计土壤水分的有效验证[13]。
主动微波遥感方法主要通过接收的雷达反射反演土壤水分。Weimann等通过土壤水分的实测实验发现了土壤含水量和雷达后向散射系数之间的线性关系[14]。Ulaby等则发现土壤水分在田间持水量的50%~60%范围时,植被的影响比较大,必须建立线性关系排除植被在监测中的干扰成分[15]。另外,有些学者分析了粗糙度对后向散射系数的影响[16]。田国良等讨论了后向散射系数随入射角的变化关系,指出交叉极化比同极化更好,后向散射系数与地表粗糙度之间存在着函数关系[17]。已被学者成功用于土壤含水量监测的雷达系统包括：ESA ERS-1/2C-Band SAR,ESA ENVISAT C-Band ASARs,Canadian C-Band RADARSAR-1/2,日本ALOS/PALSAR亦为多极化、多工作模式雷达系统,此外还有计划中的卫星项目,如NASA主、被动传感器相结合的SMAP计划[18]。主动微波遥感通过建立土壤水分和雷达后向散射系数之间的函数关系来反演土壤水分,但是在这个过程中很难定量的分离出非土壤水分的因子对后向散射系数的影响,如粗糙度、土壤纹理、植被覆盖度等,这些影响因素的准确获取存在较大难度,是主动微波遥感反演的一大难点。
为了在反演植被状态的同时反演地表水分含量,Kogan 发展了温度状态指数（Temperature Condition Index,TCI）,该指数反应了植被对温度的不同响应,高温表示植被水分亏缺,蒸腾作用减少导致温度升高,低温表示植被健康状况正常[19]。该指数只利用多年的热红外遥感数据就可进行干旱监测,但是未考虑其他气象条件对热红外遥感反演的影响,如净辐射、风速、湿度等。地表温度的季节性周期变化也是TCI的一个重要影响。归一化温度指数（Normalized Difference Temperature Index,NDTI）可消除地表温度季节变化的影响,但是计算较复杂,需要同时获得卫星过境时间的气温、太阳辐射、相对湿度、风速和叶面积指数等数据。而这些数据单纯用遥感的方法很难测量,传统的测量方法和遥感数据之间又存在空间和时间的尺度转化问题,这些缺陷很大程度上限制了NDTI的应用。
最初很多学者用Ts和NDVI两者之间的比值来计算区域蒸散研究。温度植被指数（Temperature/Vegetation Index,TVI）和地表供水指数（Vegetation Supply Water Index, VSWI）在此基础上被发展而来。之后根据两者之间的关系发展了Ts和NDVI特征空间的方法建立和气象因素的关系,更好的利用遥感数据进行干旱监测。Ts和NDVI的特征空间方法主要分为两类：① 认为Ts和NDVI散点成三角形特征,以三角形为基础建立土壤湿度的反演模型;② 认为两者的散点成梯形特征空间[20],以此来估计作物的水分胁迫状况。应用广泛的温度植被干旱指数（Temperature Vegetation Dryness Index, TVDI）就是基于三角形特征空间,该指标是根据简化的Ts-NDVI三角形特征空间提出的水分胁迫指标[21]。
综合植被和温度信息的遥感监测模型是一种具有物理意义而且计算简便的干旱监测方法,和土壤湿度及降水具有较高的相关性。但是该类指数也存在着自身的缺陷：① 采用遥感数据得到的NDVI和Ts数据往往存在时间尺度和空间尺度的不一致性;② 对区域具有非常大的依赖性,要获得较完整的特征空间,必须保证所选的区域内地表覆盖类型包含了从裸土到密闭植被的变化。③ 传感器在获得图像信息的过程中容易受到传感器自身噪声和环境因素（如云）的影响,而干边的模拟完全是依靠统计方法回归,异常值的出现会对模拟的结果产生较大的影响。
为了将遥感的方法应用到能量平衡理论中,使监测的结果更具机理,Waston等人首次提出一个适于反演低植被覆盖区土壤含水量的简单热惯量模式[22]。Price在原来能量平衡的基础上提出了简化的热惯量模型,即表观热惯量（Apparent Thermal Inertia,ATI）[23],具有较好的物理基础,在估算土壤水分含量时具有很高的精度,但是也有自身的局限,主要表现在：① 当有植被覆盖时,植被覆盖度的大小直接影响了地表温差的变幅,因此热惯量和土壤水分之间的相关性受到很大的影响,因此热惯量只适用于裸土或者低植被覆盖时期的土壤水分监测（如11月至次年3月间）。② 温度信息在热惯量模型中起着决定作用,因此就需要测定一天最高温和最低温的温差,而对于遥感数据而言难以准确获得对应时间的遥感数据。同时,热红外信息容易受云等环境因素的影响,如何提高热红外遥感的反演的精度也是定量遥感需要研究的重要问题。
在植被全覆盖条件下,可以把土壤和植物作为一个整体来建立它和大气间热交换模型,用单层模型来估算地表蒸散。当植被部分覆盖时,因两者的热特性不同,因此需要将土壤和植被分开产生了双层模型[24]。该模型分别计算土壤表面温度、冠层温度、植被覆盖度,模型的复杂度相比单层模型增加了很多。后来又出现了多层模型,更加细致的区分生态系统中的不同层面[25]。
以能量平衡为基础的作物缺水指数法物理意义明确,精度高,适合于区域土壤水分的反演。然而也有自身的适用条件：① 单层模型在适合密闭植被区地表水分反演,对于低植被覆盖区却不适用。而适合于低植被覆盖区的双层模型和多层模型在低植被覆盖区具有明确的物理意义,却计算复杂,需要较多的输入参数,很难在实际中应用。② 此类模型需要遥感数据和气象数据结合,但是两种类型的数据在时空尺度匹配上存在一定的问题。
概括以上4类干旱遥感监测模型,以遥感数据为手段的干旱监测总的来说可以分为两个方向：① 采用植被指数模型反映植被的健康状况,再找到植被的健康状况异常和降水、土壤水分之间的关系,以此来反映干旱的强度和范围。② 以遥感为手段来反演植被含水量、土壤水分、或者植被-地表综合的地表水分含量,认为水分的多少可以直接反映干旱的程度和旱灾的范围。第一种方法的一个重要前提是,干旱的主要因素是因缺乏降水造成的土壤水分缺失而使植被的健康状况受到影响。然而,植被健康状况容易受到多种因素的影响,除了干旱之外还有洪涝、火灾、病虫害等其他自然因素和人为因素的干扰。而且随着地貌、植被类型、植被所处的物候期的不同,干旱对植被的影响程度也不同。第二种方法较第一种方法更能直接和干旱产生关系。但是,因为干旱定义的不统一,水分的多少和干旱之间的联系难以定量的刻画。同时,植被、土壤的水分含量因地形、土壤属性、植被类型等因素的不同而不同,使干旱的定量描述变的更加复杂。单纯的依靠遥感的方法无法考虑其他因素在干旱监测中的影响[26]。
单纯利用一种数据或者某一方面的信息无法刻画干旱这样复杂的事件,越来越多的学者开始认识到发展干旱综合监测模型的重要性。作物水分指数（CMI）就是在Palmer干旱指数（Palmer Drought Severity Index, PDSI）[27]的基础上综合了作物需水信息而建立的更适合于农业干旱监测的模型[28]。地表供水指数（SWSI）[29]则融入了更多的信息,把水文和气候特征耦合成了一个综合的指标值。遥感为获取区域地表参数提供了有效途径,不少学者开始探索将遥感和部分气象要素结合应用到复杂的能量平衡理论中建立更加具有物理意义的监测模型,其中作物缺水指数（CWSI）[30]就是其中代表性的方法。仝兆远等对土壤水分监测方法做了较全面的总结,提出需要综合利用各种遥感手段进行土壤水分监测[31]。杨绍锷等综述了目前国内外的农业干旱遥感监测研究进展,提出需要综合农业、气象、水文、植物等众多学科信息进行旱情监测[32]。
有效的干旱监测不仅需要反映土壤水分、植被的生长状态,而且应该考虑植被类型、温度、地形以及人为灌溉等因素的差别和影响。需要将传统的气象数据干旱监测方法和遥感监测方法进行有效的结合,同时考虑地形、植被类型和人为因素对干旱监测的影响而建立基于多源数据的干旱综合监测方案。目前很多学者在这方面进行了探索和研究,一些国家利用多指标权重赋值法发展了综合多种信息的干旱监测业务化系统。近年来数据挖掘的方法逐渐被用于干旱监测研究,取得较好的效果。
基于权重赋值法的综合监测模型主要是各个国家和地区通过自动气象站网络、卫星和Internet多种监测手段综合利用而建立的干旱业务系统。美国国家级的干旱监测业务系统（The Drought Monitor）始于20世纪80年代,该系统采用基于各监测模型概率分布的百分位法将各个指标综合到一起,该业务系统被认为是最好的综合干旱信息监测系统之一[33],因为它综合考虑了6个关键的干旱监测指标和多个辅助性的参考指标。6个主要的干旱监测指数包括：PDSI、CPC土壤湿度模式、美国地质测量局日流量指标、标准降水百分位数、标准降水指数（Standardized Precipitation Index, SPI）、VCI。
中国气象局国家气候中心1995年开发研制了“全国旱涝气候监测、预警系统”,该系统采用的监测指数包括：降水距平百分率、标准化降水指数、Ki指数、湿润指数和综合旱涝指标Ci等进行干旱监测。澳大利亚国家农业监测系统（National Agriculture Monitoring System, NAMS）由气象和澳大利亚联邦科学与工业研究组织（CSIRO）合作完成,主要考虑的干旱监测指标包括：降水百分位、降水历史评估指标及显著性、降水可信度、NDVI距平、作物产量模拟值等[3]。
数据挖掘技术采用机器学习、模式识别、统计、数据库等技术对数据进行分析、聚类并可视化,解决了从大量的数据库中进行信息提取的问题,越来越多的学者应用该方法分析环境问题[34]。同时,对于干旱监测这样复杂的科学问题,数据挖掘的方法也同样适用,Tadesse等在这方面做了积极探索。
1） 时序分析模型的应用。Tadesse等在其研究中用数据挖掘探索干旱和一些海洋和气候指数之间的关系,以帮助决策者在干旱发生之前进行正确的决策处理以应对干旱[35]。以Nebraska为研究区,采用两种时间序列数据挖掘模型研究海洋和气候指数与干旱指数之间的关系[36]。两种模型分别是：Representative Episode Association Rule (REAR)、Minimal Occurrences With Constraints and Time Lags (MOWCATL)。
2） 基于规则的回归树模型应用。Tadesse等首先将这种数据挖掘的新方法应用到干旱监测中。该方法考虑到植被生长的不同阶段,将整个生长季分成3个阶段,并分别用基于规则的回归树模型同时分析干旱和遥感数据获取的植被健康状况、气象干旱指数、生态数据（包括地表类型数据、土壤有效含水量、灌溉数据）等之间的关系。随着该技术的不断完善,最终形成了有效地综合干旱指数（Vegetation Drought Response Index,Veg DRI）并应用于美国多个州的干旱监测,取得了良好的效果[37]。
采用各个指标权重赋值的方法无法解决各监测指标空间尺度和时间尺度不匹配的问题。这类模型一般具有较多的参数,而且每个参数一般都需要较长的历史数据积累,而且一般要求各个参数都具有相同的历史标定期,但实际中这种条件同时满足比较难达到要求[38]。同时该类模型的监测指标权重的确定困难,往往具有主观因素,很难反映客观的实际情况。基于数据挖掘的干旱监测综合方法提供了干旱研究的新思路,其主要的进步是解决了气象监测指数、遥感监测指数、其他影响因素的时间尺度、和空间尺度的不一致问题,能够将不同类型的数据综合到一起形成反映综合信息的干旱监测模型。但也存在一定的缺陷和不足,主要表现在：未加入地表温度信息,无法反映植被生长的水热条件[39];综合模型只应用了少量的遥感和气象指标,未深入对比分析其他遥感监测模型在综合模型中应用的效果[40];未评价综合模型的各参数对综合监测结果影响的特征。通过比较不同类型干旱指数的特性,基于决策回归树模型集成气象、遥感和地表物理属性数据,选取合适的因子建立全面刻画干旱特征的地表干旱综合指数（Integrated Surface Drought Index, ISDI）在中国的干旱监测中获得了精度较高的结果,并通过了降水、土壤湿度等多种实测数据的验证[41]。
干旱气象监测模型具有明确的数理机理,可以定量刻画干旱的等级。然而气象数据只能依靠站点获取,空间分布上往往不均匀,而且基于气象数据的干旱监测模型很难准确反映植被的健康状况。干旱遥感监测模型具有区域、动态、便于进行实时干旱监测的优势,遥感植被状态监测方法、微波遥感土壤水分反演法、热红外遥感干旱监测方法、和基于能量平衡的遥感监测方法在干旱监测中均有较多应用。其中微波遥感在反演土壤水分方面的应用越来越广泛,但存在空间分辨率较低的问题,较适合大区域的土壤水分动态监测。很多学者探索微波遥感土壤水分降尺度的方法,包括：集成地形和土壤信息的方法;结合高分辨率雷达数据、可见光和热红外数据的方法;以及通过地表模型集成微波数据和光学数据的方法。总的来说,遥感即使在反演土壤水分上表现出较强优势,但在监测土壤水分到监测干旱之间仍然存在一个较难过度的过程,因为仅依靠遥感数据无法准确判断干旱的导致因素——降水量,同时干旱的强度和等级的定量描述也非常困难。
实时而有效的监测干旱可以降低干旱造成的损失,而干旱的发生有着极其复杂的机理,在受到各种自然因素如降水、温度、地形等影响的同时也受到人为灌溉等因素的影响。单纯的气象或者遥感的方法无法达到反映干旱特征的目的。综合多源数据的干旱综合监测模型是研究复杂的干旱监测问题的新途径,在解决干旱监测的复杂问题中有着较大的应用潜力。但总的来说这方面的研究大都处于试验和探索阶段,如何选取最佳的监测指标将气象监测指标和遥感监测指标进行有效结合尚需开展深入的研究,同时,气象因素、植被因素、植被类型、生态区划、人为灌溉因素等对干旱监测结果影响也有待进一步评价。
The authors have declared that no competing interests exist.
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<p>借鉴相关领域研究概念,对1950～2010年中国干旱灾情的变化动态,特别是频率与规模的关系做定量分析,划分风险的可接受与不可接受区。研究结果发现:① 总体上,60 a来干旱灾害中受灾、成灾面积和粮食损失在逐年不断的增加。② 干旱灾害中粮食损失与累积频率的关系与著名的古登堡-里查德关系类似,呈现良好的幂律性。而受灾、成灾面积与累积频率的关系则符合线性关系。③ 通过借鉴地质灾害领域相关可接受风险概念,划定干旱灾害中年粮食损失、受灾和成灾面积的可接受与不可接受灾情的界线。④ 借鉴相关领域研究概念,尝试对干旱灾害进行探索,因此,在将来的研究中还有待继续和深化。</p>
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Based on RS and GIS technologies,the temporal and spatial dynamics of sandy desertification in China under climate disturbance were studied by pixel based grey correlation method during the recent 32 years.The data used in this paper incorporated MSS imageries in 1975,ETM imageries in 2000,CBERS imageries in 2007,temperature,and precipitation data.The results showed that the area and degree of sandy desertification reduced from 2000 to 2007,but,still showed slightly increasing trend from 1975 to 2007.Over the past 32 years,the whole area increased 6 179.8 km2,and the greatest increased area occurred in heavy desertification.With the application in Inner Mongolia and Xinjiang,the grey correlation analysis in China indicated that,compared with precipitation(0.884,0.882),temperature played a more important role(0.973,0.976) in sandy desertification change.Under the disturbance of climate factors,the general tendency of sandy desertification was positively correlated with temperature and negatively related to precipitation.The tendency was more distinct in the west of Gansu and Shaanxi.The most obvious area concentrated in the north and east of Xinjiang,and the north of Tibet.
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In the field of remote sensing applications, scientists have developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements. The spectral response of vegetated areas presents a complex mixture of vegetation, soil brightness, environmental effects, shadow, soil color and moisture. Moreover, the VI is affected by spatial鈥恡emporal variations of the atmosphere. Over forty vegetation indices have been developed during the last two decades in order to enhance vegetation response and minimize the effects of the factors described above. This paper summarizes, refers and discusses most of the vegetation indices found in the literature. It presents different existing classifications of indices and proposes to group them in a new classification.
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In recent years the National Oceanic and Atmospheric Administration (NOAA) has designed a new AVHRR-based Vegetation Condition Index (VCI) that has showed to be useful for drought detection and tracking. Validations showed that the VCI has excellent ability to detect drought and to measure time of its onset, intensity, duration, and impact on vegetation. The VCI provides accurate drought information not only for well-defined, prolonged, widespread, and intensive droughts, but also for very localized, short-term, and non well-defined droughts. In addition to the VCI, the AVHRR-based observations in thermal bands were used to develop the Temperature Condition Index (TCI). This index was used to determine temperature-related vegetation stress and also stress caused by an excessive wetness. This paper provides principles of these indices, describes data processing, and gives examples of VCI/TCI application in different ecological environments of the United States.
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the Hexi region lies in the arid and semiarid regions of China,its fragile ecological environment and severity desertification were caused by natural environments change and humanity influences.With the development of remote sensing and GIS,it provides an effective method to research desertification.In this paper,MODIS data between June and August in 2002 and 2008 were used to analyze desertification,and five indexes {including MSAVI(Modified Soil-Adjusted Vegetation Index),Surface Albedo,LST(Land surface temperature),FAVI and TVDI} were used to assess degrees of desertification.The results indicate that the desertification land in Hexi region is larger and occupies more than 50% land in total land.The desertification land was decreased by 6431.64 km2 in 2008,the non-desertification land and slight desertification land increased by 2.25 and 0.26%,respectively;moderate,severer and extremely severer desertification land decreased 1.4,1.09 and 0.31%,respectively.Summery,the reverse rate of land desertification is larger than development rate,and shows that reversing is common tendency but part region is in desertification development.In the study region,it mainly is slight desertification land,23.64 and 23.90% in 2002 and 2008,respectively,and then belongs to moderate,severer and extremely severer desertification lands.
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对土壤光谱遥感监测的国内外发展情况进行了回顾，简单总结了影响土壤光谱的原因、土壤光谱的类型、土壤遥感的最佳波段、土壤的遥感分类以及土壤遥感的定量研究。重点对土壤水分遥感监测的方法和原理进行了详细的论述。最后对土壤遥感监测的发展趋势进行了分析和展望。
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Remote sensing provides a means of observing hydrological state variables over large areas. The ones which we will consider in this paper are land surface temperature from thermal infrared data, surface soil moisture from passive microwave data, snow cover using both visible and microwave data, water quality using visible and near-infrared data and estimating landscape surface roughness using lidar. Methods for estimating the hydrometeorlogical fluxes, evapotranspiration and snowmelt runoff, using these state variables are also described.
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Not Available
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A campaign started in September 1993 and finished in December 1994 in which ground truth measurements were taken during 40 ERS-1 over-flights. Synthetic Aperture Radar (SAR) Precision images (PRI) of ERS-1 of the descending and the ascending passes were used to investigate the dependency of the backscattering coefficient on the soil moisture content. Under certain circumstances a linear relation between the backscattering coefficient and the soil moisture is found.
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Not Available
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A sensitivity analysis of C-band (5.3 GHz) and Ku-band (14.85 GHz) synthetic aperture radar (SAR) data to the soil moisture content of agricultural fields was conducted in this study. The C-band data were obtained with a 23° incidence angle, whereas the Ku-band data were obtained with 35°, 55°, and 75° incidence angles. The fields presented either a small-scale or an intermediate-scale periodic soil roughness components, associated with level-basin and furrow irrigation systems, respectively. For fields with a small-scale roughness component, the SAR data were sensitive to soil moisture, particularly at the C-band with a 23° incidence angle and Ku-band with a 35° incidence angle. For fields with a intermediate-scale roughness component, both C- and Ku-band data were nearly insensitive to soil moisture. By using a theoretical surface scattering model, this study also analyzed the effects of different soil roughness components [root mean square (RMS) height h, correlation length, and periodic row structure] in the SAR data. For fields with RMS height <0.3 cm, a small variation in h (from 0.1 to 0.3 cm) provoked a significant variation in the SAR data (up to 8 dB).
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Not Available
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A simplified land surface dryness index (Temperature-Vegetation Dryness Index, TVDI) based on an empirical parameterisation of the relationship between surface temperature (T) and vegetation index (NDVI) is suggested. The index is related to soil moisture and, in comparison to existing interpretations of the T/NDVI space, the index is conceptually and computationally straightforward. It is based on satellite derived information only, and the potential for operational application of the index is therefore large. The spatial pattern and temporal evolution in TVDI has been analysed using 37 NOAA-AVHRR images from 1990 covering part of the Ferlo region of northern, semiarid Senegal in West Africa. The spatial pattern in TVDI has been compared with simulations of soil moisture from a distributed hydrological model based on the MIKE SHE code. The spatial variation in TVDI reflects the variation in moisture on a finer scale than can be derived from the hydrological model in this case.
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Geologic applications of thermal infrared images [J].https://doi.org/10.1109/PROC.1975.9712 URL [本文引用: 1] 摘要
Thermal infrared images provide information about the near-surface physical state of geologic materials, particularly, the density, water content, and heat transfer. Nonterrestrial planetary studies, conducted at fairly coarse resolution, have been useful primarily in determining the distribution of rock fragments. Terrestrial studies, conducted from satellite and aircraft at coarse to fine resolutions, have been successful for monitoring effusive volcanism, delineating areas of steaming altered ground and hot-spring activity, detecting fractures expressed hydrologically and topographically, and distinguishing a variety of geologic materials with physical and compositional differences. Interpretation of thermal images is complicated by the various types of physical processes involved and commonly requires an assessment of many different factors. A simple theoretical model was used in this analysis to provide quantitative assessment of some of these factors, to predict optimum times to acquire thermal data, and to determine quantitative values of various properties of terrian. Two geologic applications were studied in some detail: geothermal mapping and thermal inertia mapping. Initial results indicate that both techniques have considerable potential, especially in reconnaissance studies. These data were acquired under optimal meteorological conditions and at sites where the geologic materials were well exposed. A realistic assessment of the limitations of these techniques must await future studies.
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On the analysis of thermal infrared imagery-The limited utility of apparent thermal inertia (for Heat Capacity Mapping Mission data of surface temperature) [J].https://doi.org/10.1016/0034-4257(85)90038-0 URL [本文引用: 1] 摘要
A spectral window in the thermal infrared permits observations of surface temperature by satellite radiometry. The Heat Capacity Mapping Mission (HCMM) acquired 10-12 micrometer data at times of day favorable for estimation of surface thermal properties and the surface energy budget. Two variables, surface wetness, which controls evaporation and hence mean surface temperature, and thermal inertia, which relates the diurnal excursion of surface temperature to ground heat flux, are responsible for most observed temperature variability. These variables may be estimated from the mid night (2:30 a.m.) and early afternoon (1:30 p.m.) data from the HCMM or from the afternoon NOAA satellites. However, the HCMM data product, "apparent thermal inertia," is potentially misleading in agricultural areas because surface evaporation reduces the amplitude of the soil heat flux compared to the amplitude in dry areas. Thus apparent thermal inertia should not be used in regions having variability in surface moisture.
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Abstract A one-dimensional model is adopted to describe the energy partition of sparse crops. Theoretical development of this model yields a combination equation which describes evaporation in terms of controlling resistances associated with the plants, and with the soil or water in which they are growing. the equation provides a simple but physically plausible description of the transition between bare substrate and a closed canopy. Although the aerodynamic transfer resistances for incomplete canopies have, as yet, no experimental justification, typical values, appropriate to a specimen agricultural crop and soil, are shown to have limited sensitivity in the model. Processes which require further study if the equation is to be used to calculate evaporation throughout a crop season are also discussed.
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A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing [J].https://doi.org/10.1016/S0034-4257(96)00215-5 URL [本文引用: 1] 摘要
We present an operational two-source (soil+vegetation) model for evaluating the surface energy balance given measurements of the time rate of change in radiometric surface temperature (T RAD ) during the morning hours. This model consists of a two-source surface component describing the relation between T RAD and sensible heat flux, coupled with a time-integrated component connecting surface sensible heating with planetary boundary layer development. By tying together the time-dependent behavior of surface temperature and the temperature in the boundary layer with the flux of sensible heat from the surface to the atmosphere, the need for ancillary measurements of near-surface air temperature is eliminated. This is a significant benefit when T RAD is acquired remotely. Air temperature can be strongly coupled to local biophysical surface conditions and, if the surface air and brightness temperature measurements used by a model are not collocated, energy flux estimates can be significantly corrupted. Furthermore, because this model uses only temporal changes in radiometric temperatures rather than absolute temperatures, time-independent biases in T RAD , resulting from atmospheric effects or other sources, do not affect the estimated fluxes; only the time-varying component of corrections need be computed. The algorithm also decomposes the surface radiometric temperature into its soil and vegetation contributions; thus the angular dependence of T RAD can be predicted from an observation of T RAD at a single view angle. This capability is critical to an accurate interpretation of off-nadir measurements from polar orbiting and geosynchronous satellites. The performance of this model has been evaluated in comparison with data collected during two large-scale field experiments: the first International Satellite Land Surface Climatology Project field experiment, conducted in and around the Konza Prairie in Kansas, and the Monsoon '90 experiment, conducted in the semiarid rangelands of the Walnut Gulch Watershed in southern Arizona. Both comparisons yielded uncertainties comparable to those achieved by models that do require air temperature as an input and to measurement errors typical of standard micrometeorological methods for flux estimation. A strategy for applying the two-source time-integrated model on a regional or continental scale is briefly outlined.
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| [26] |
Seasonal comparisons of meteorological and agricultural drought indices in Morocco using open short time-series data [J].https://doi.org/10.1016/j.jag.2013.05.005 URL [本文引用: 1] 摘要
Although the preliminary investigations of NDWI demonstrated its sensitivity to vegetation water content, drought indices based on NDWI short time-series are still understudied compared to those derived from NDVI and LST, such as VCI, SVI and TCI. On the basis of the open data, this paper introduces a new index derived from NDWI short time-series, and explores its performance for drought monitoring in Mediterranean semi-arid area. The new index, Standardized Water Index (SWI), was calculated and spatiotemporally compared to both meteorological drought index (TRMM-based SPI) and to agricultural drought index (NDVI-based SVI) for the hydrological years and autumn, winter and spring seasons during a period of 15 years (1998-2012). Furthermore, the response and spatial agreement of the meteorological and agricultural drought indices (SWI, SVI and SPI) were compared over two land use classes, rainfed agriculture and vegetation cover, for the studied years and seasons. The validation of SWI was based on in situ SPI and cereal productions. The analysis of the 336 cross-tables, proportions of concordance and Cohen's kappa coefficients indicate that SWI and SVI are concordant comparing to other combinations for hydrological years and for the three seasons. The study points that the spatial agreements of drought indices over rainfed agriculture and over vegetation cover are different. It is relatively more important in the rainfed agriculture than in the vegetation cover areas. Our results show that the agreement between vegetation drought indices and meteorological drought indices is moderated to low and the SPI is slightly more concordant with SWI when it is compared to SVI in autumn and winter seasons. The validation approach indicates that drought affected area, according to SWI, is highly correlated with cereal production. Likewise, a satisfactory correlation was revealed between SWI and in situ SPI.
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| [27] |
区域干旱帕默尔旱度指标的修正 [J].
帕默尔旱度模式是评估干旱严重程度的重要模型,20世纪引入中国后,安顺清等人相继进行了修正,但是多采用的是单个站点数据进行运算,在研究大尺度的区域时则不适用,文章将各研究区站点数据平均整合,并以辽宁省范围内辽河流域片的7个代表性区域进行建模,以海滦河流域、淮河流域、黄河流域、长江流域的23个代表性区域相关资料为权重因子进行修正,同时利用综合水平衡模型对原帕默尔旱度模式中的水量平衡模型进行替换,经过对修正模式的验证,结果显示符合实际情况,对于区域干旱研究具有实际意义。
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| [28] |
Keep ing track of crop moisture conditions, nationwide:The new crop moisture index [J]. |
| [29] |
Development of a Surface Water Supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas [ |
| [30] |
Canopy temperature as a crop water stress indicator [J].https://doi.org/10.1029/WR017i004p01133 URL [本文引用: 1] 摘要
Canopy temperatures, obtained by infrared thermometry, along with wet- and dry-bulb air temperatures and an estimate of net radiation were used in equations derived from energy balance considerations to calculate a crop water stress index (CWSI). Theoretical limits were developed for the canopy air temperature difference as related to the air vapor pressure deficit. The CWSI was shown to be equal to 1 - E/E, the ratio of actual to potential evapotranspiration obtained from the Penman-Monteith equation. Four experimental plots, planted to wheat, received postemergence irrigations at different times to create different degrees of water stress. Pertinent variables were measured between 1340 and 1400 each day (except some weekends). The CWSI, plotted as a function of time, closely paralleled a plot of the extractable soil water in the 0- to 1.1-m zone. The usefulness and limitations of the index are discussed.
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| [31] |
土壤水分遥感监测的研究进展 [J].https://doi.org/10.3969/j.issn.1000-288X.2007.04.028 URL [本文引用: 1] 摘要
土壤水分是土壤的重要组成部分,在地-气界面间物质、能量交换中 起着重要的作用,是农作物生长发育的基本条件和农作物产量预报的重要参数.遥感技术具有大面积同步观测,时效性、经济性强的特点,为大面积动态监测土壤水 分提供了可能.简述了到目前为止出现的几种主要的土壤水分遥感监测方法,如热惯量法、作物缺水指数法、归一化植被指数法、植被指数距平法、植被供水指数 法、植被状态指数法、温度状态指数法、温度植被干旱指数法、高光谱法、微波遥感法,并分析了各种方法的原理和特点,最后展望了土壤水分遥感监测方法的发展 趋势.
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| [32] |
农业干旱遥感监测研究进展 [J].https://doi.org/10.3969/j.issn.1000-3177.2010.01.021 URL [本文引用: 1] 摘要
农业干旱给社会经济及人民生活造成严重影响,关于农业旱情监测的研究受到了学者们的广泛关注。遥感技术的发展为准确、及时进行旱情监测提供了新的机遇。本文综述了近年来国内外采用遥感方法监测农业旱情的研究进展,包括土壤湿度、作物形态、作物生理等农业旱情指标的遥感反演,指出了在实际应用中存在的一些问题,并提出了进一步改进的思路。
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| [33] |
Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness [J].https://doi.org/10.1007/s11269-006-9076-5 URL Magsci [本文引用: 1] 摘要
Recent droughts in the United States have highlighted the nation鈥檚 current and increasing vulnerability to this natural hazard. Drought-related impacts are also becoming more complex, as illustrated by the rapidly rising impacts in sectors such as recreation and tourism, energy, and transportation. Environmental and social consequences are also of increasing importance. Conflicts between water users and disputes between political entities on transboundary water issues are a reflection of the need for improved documentation of the consequences of extended periods of water shortage. Unfortunately, no national drought impact database exists and drought impact statistics are not routinely compiled at the state, regional, or national level. Without this information, it is an arduous task to convince policy and other decision makers of the need for additional investments in drought monitoring and prediction, mitigation, and preparedness. The National Drought Mitigation Center at the University of Nebraska-Lincoln is addressing this problem by creating a web-based Drought Impact Reporter (DIR) that has the following primary functions: (1) to create a database archive of drought impacts information; (2) to provide an interactive map delivery system that is efficient and user-oriented; (3) to build links with governmental agencies, non-governmental organizations, university research groups and extension programs, and others, including the public, in order to provide timely impact reports to ensure a comprehensive collection of drought impacts across all potential sectors and scales; and (4) to foster a continual process of user feedback, evaluation, assessment, and dissemination of drought impacts. The Drought Impact Reporter was launched in July 2005 and is available on the NDMC鈥檚 web site (http://drought.unl.edu). Copyright Springer Science + Business Media B.V. 2007
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| [34] |
Adaptive data-driven models for estimating carbon fluxes in the Northern Great Plains [J].https://doi.org/10.1016/j.rse.2006.09.017 URL [本文引用: 1] 摘要
Estimates of NEE were produced for each 10-day period during each growing season from 1998 to 2001. Growing season carbon flux estimates were combined with winter flux estimates to derive and map annual estimates of NEE. The rule-based piece-wise regression model is a dynamic, adaptive model that captures the relationships of the spatial data to NEE as conditions evolve throughout the growing season. The carbon dynamics in the Northern Great Plains proved to be in near equilibrium, serving as a small carbon sink in 1999 and as a small carbon source in 1998, 2000, and 2001. Patterns of carbon sinks and sources are very complex, with the carbon dynamics tilting toward sources in the drier west and toward sinks in the east and near the mountains in the extreme west. Significant local variability exists, which initial investigations suggest are likely related to local climate variability, soil properties, and management.
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| [35] |
Drought Monitoring Using Data Mining Techniques:A Case Study for Nebraska,USA [J].https://doi.org/10.1023/B:NHAZ.0000035020.76733.0b Magsci [本文引用: 1] 摘要
<a name="Abs1"></a>Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought.
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| [36] |
Efficient rule discovery in a geo-spatial decision support system [ |
| [37] |
The Vegetation Drought Response Index (VegDRI):a new integrated approach for monitoring drought stress in vegetation [J].https://doi.org/10.2747/1548-1603.45.1.16 URL [本文引用: 1] 摘要
The development of new tools that provide timely, detailed-spatial-resolution drought information is essential for improving drought preparedness and response. This paper presents a new method for monitoring drought-induced vegetation stress called the Vegetation Drought Response Index (VegDRI). VegDRI integrates traditional climate-based drought indicators and satellite-derived vegetation index metrics with other biophysical information to produce a 1 km map of drought conditions that can be produced in near-real time. The initial VegDRI map results for a 2002 case study conducted across seven states in the north-central United States illustrates the utility of VegDRI for improved large-area drought monitoring.
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| [38] |
Drought Indicators and Triggers: A Stochastic Approach to Evaluation [J].https://doi.org/10.1111/j.1752-1688.2003.tb03704.x URL [本文引用: 1] 摘要
ABSTRACT: Drought management depends on indicators to detect drought conditions, and triggers to activate drought responses. But determining those indicators and triggers presents challenges. Indicators often lack spatial and temporal transferability, comparability among scales, and relevance to critical drought impacts. Triggers often lack statistical integrity, consistency among drought categories, and correspondence with desired management goals. This article presents an approach for developing and evaluating drought indicators and triggers, using a probabilistic framework that offers comparability, consistency, and applicability. From that, a multistate Markov model investigates the stochastic behavior of indicators and triggers, including transitioning, duration, and frequency within drought categories. This model is applied to the analysis of drought in the Apalachicola-Chattahoochee-Flint River Basin in the southeastern United States, using indicators of the Standardized Precipitation Index (for 3, 6, 9, and 12 months), the Palmer Drought Severity Index, and the Palmer Hydrologic Drought Index. The analysis revealed differences among the performance of indicators and their trigger thresholds, which can influence drought responses. Results contribute to improved understanding of drought phenomena, statistical methods for indicators and triggers, and insights for drought management.
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| [39] |
Comparison of Remote Sensed and Meteorological Data Derived Drought Indices in Mid-Eastern China [J]. |
| [40] |
Establishing and assessing the Integrated Surface Drought Index (ISDI) for agricultural drought monitoring in mid-eastern China [J].https://doi.org/10.1016/j.jag.2012.11.003 URL [本文引用: 1] 摘要
Accurately monitoring the temporal, spatial distribution and severity of agricultural drought is an effective means to reduce the farmers鈥 losses. Based on the concept of the new drought index called VegDRI, this paper established a new method, named the Integrated Surface Drought Index (ISDI). In this method, the Palmer Drought Severity Index (PDSI) was selected as the dependent variable; for the independent variables, 12 different combinations of 14 factors were examined, including the traditional climate-based drought indicators, satellite-derived vegetation indices, and other biophysical variables. The final model was established by fully describing drought properties with the smaller average error (relative error) and larger correlation coefficients. The ISDI can be used not only to monitor the main drought features, including precipitation anomalies and vegetation growth conditions but also to indicate the earth surface thermal and water content properties by incorporating temperature information. Then, the ISDI was used for drought monitoring from 2000 to 2009 in mid-eastern China. The results for 2006 (a typical dry year) demonstrate the effectiveness and capability of the ISDI for monitoring drought on both the large and the local scales. Additionally, the multiyear ISDI monitoring results were compared with the actual drought intensity using the agro-meteorological disaster data recorded at the agro-meteorological sites. The investigation results indicated that the ISDI confers advantages in the accuracy and spatial resolution for monitoring drought and has significant potential for drought identification in China.
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| [41] |
The Integrated Surface Drought Index (ISDI) as an Indicator for Agricultural Drought Monitoring: Theory, Validation, and Application in Mid-Eastern China [J].https://doi.org/10.1109/JSTARS.2013.2248077 Magsci [本文引用: 1] 摘要
Based on the concept of the new drought index called Vegetation Drought Response Index (VegDRI) using data-mining technology, an Integrated Surface Drought Index (ISDI) was established in this study. ISDI improved the original model, adding remote sensed temperature information into the input factors. This index attempt to describe drought from a more comprehensive perspective, the integrated information including: traditional meteorological data, satellite-derived earth surface water and heat environments, vegetation conditions, and inherent properties of the earth's surface. The Cross-validation results indicated that ISDI construction models for three phases of growth season have very high regression accuracy. The practical application of ISDI in mid-eastern China during the reported dry year 2009 also demonstrated that it can provide accurate and detailed drought condition both at regional and local scale. This investigation showed that ISDI has good application potential for drought monitoring across China.
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