基于植被蒸散法的区域缺水遥感监测方法研究
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摘要
内蒙古半干旱地区存在着严重的缺水问题。本文结合973子课题(G2000077907)——“定量遥感在西部生态环境建设中的应用示范研究”，以不同退化程度的草地为主要研究对象，围绕着区域缺水这个主题，从MODIS卫星遥感数据预处理、地表参数的遥感定量反演、到区域缺水遥感监测方法的改进开展了一系列的研究，主要工作包括以下几方面：
从地表能量平衡原理入手，结合地面同步实测数据和气象数据，运用遥感蒸散法，建立了基于亚像元的半干旱地区区域双层蒸散模型(SRDEM)；其中，考虑到叶面积指数与植被的几何粗糙度之间有密切关系，引入叶面积指数计算动力传输粗糙度长度，从而改进显热通量算法。并通过地表缺水指数研究区域缺水状况，实现对表层土壤水分的定量反演。
地表缺水指数物理意义明确，定量精度较高，但涉及参数较多，且部分仍依赖于地面气象台站及同步观测的微气象资料，实时性相对较差。因此本次研究从分析植被冠层反射、发射光谱与植被冠层水的成因关系入手，直接运用卫星遥感参数——植被指数、植被组分温度、归一化植被水分指数，组成植被冠层水综合指数(VTWSI)，以反演植被冠层水分含量。
地表缺水指数是通过基于亚像元的双层蒸散模型提取的，它既考虑了土壤的水分蒸发，又考虑了植被的蒸腾。目前主要是通过建立地表缺水指数和实测土壤水分之间的经验关系式，提取土壤水分含量。考虑到半干旱地区植被冠层水主要由土壤水提供，但植被供水状况与植被水分实际状况之间存在着一定的“滞后”，而在监测区域缺水或旱情的实际应用中，人们更关心的是植被实际水分状况，但仅考虑植被冠层水对于半干旱地区的区域研究也是不够的。鉴于此，本文运用遗传规划算法，建立了地表缺水指数与植被冠层水分、表层土壤水分含量之间的定量关系。通过模拟值与实际值之间的对照分析及敏感性分析表明所建定量关系式是比较理想的，而且该定量关系在植被覆盖区不但考虑了土壤水，同时还加入了植被冠层水，说明能够应用到地表缺水的研究，从而改进了运用遥感蒸散法监测区域缺水的方法。
运用MODIS卫星遥感数据定量反演了地表温度、植被／土壤组分温度、地表反照率、叶面积指数、植被覆盖度等地表参数，作为模型计算的输入。为满足基于亚像元尺度
基于植被蒸散法的区域缺水遥感监测方法研究
的双层蒸散模型需求，本文选择遗传算法利用MODIS热红外多波段数据，实现了基于
亚像元尺度的植初土壤组分温度反演，为地表能量平衡、蒸散研究提供更精确的参数。
云一直是图像处理、图像分析的一大障碍，由于本研究区常见多云天气，为使从
遥感数据提取的参数更精确，在大气校正的前提下，进行云的检测分析是很有必要的。
在云检测多种研究方法中，一般采用阐值法来提取云的信息。由于闽值法带有一定主
观性，本文探讨了云检测指数和基于空间结构分析和神经网络的云自动检测算法，其
检测结果较常规闭值法精度高、效果好。
Heavy deficit of water is a key problem in semi-arid area of the northwest China, Inner Mongolia. This article mainly studies different degraded degrees grasslands. Aiming at the topic of regional water deficit, this paper has developed methods in MODIS data preprocessing, remote sensing quantitative inversion of land surface parameters, and improvement of regional water deficit monitoring using remote sensing. Main subjects are as follows.
According to surface energy balance theory, the semi-arid regional dual-layer evapotranspiration model (SRDEM) is established , using remote sensing monitoring of evapotranspiration at sub-pixel level, combined with the field measurement and meteorological data. Taking the relation between leaf area index and vegetation geometrical roughness into account, a leaf area index is introduced to compute the vegetation geometrical roughness so that the algorithm computing the sensible heat flux can be improved. The surface water deficit index (SWDI) is extracted using actual and potential evapotranspiration to study the regional water deficit condition. The regional soil water content is then assessed.
Surface water deficit index with clear definition and specific meanings has higher quantitative precision. But it refers to more parameters and some of them still rely on the meteorological observations of the surface weather station, which leads to limitation to real-time monitoring. Therefore, considering vegetation reflecting, emissive spectrum and the genetic relation of vegetation water, this research has introduced a vegetation water synthetical index (VTWSI) using parameters extracted from remote sensing data, vegetation index, normalized difference water index and vegetation component temperature, which can be used to inverse vegetation water content.
The surface water deficit index is extracted based on the dual-layer evapotranspiration model, including soil water evaporation and vegetation transpiration. As vegetation water is
mainly provided by soil water in semi-arid area, there is a time lag between the vegetation water provision condition and the vegetation water content. More attention has been paid to the vegetation water content in monitoring regional water deficit and ravages of drought, but it is not enough to only concern vegetation water content. Thus, this article establishes the remote sensing quantitative formula of regional water deficit using genetic plan based on surface water deficit index, surface soil water content and vegetation water content. The result shows that the quantitative formula is ideal in comparison between modeled and true values and through a sensitivity analysis. For vegetation area, the quantitative formula considers not only the soil water but also the vegetation water. It can be used to study surface water deficit, and to improve the method of regional water deficit monitoring using remote sensing.
Parameters, such as land surface temperature, vegetation/soil component temperatures, albedo, leaf area index and vegetation coverage etc., are inversed quantitatively using MODIS data as the input of model. To meet the need of dual-layer evapotranspiration model, this study adopts genetic algorithm to inverse component temperatures using two infrared bands of MODIS data, which can provide more accurate parameters for land surface energy balance and evapotranspiration study.
Cloud is a large obstacle to processing and analysis of remote sensing image, because it is often cloudy in this area. It is necessary to detect cloud for improving the parameters inversion precision using remote sensing data. Among numbers of cloud detection methods, threshold method is often used to extract cloud information. Considering some subjective factors of threshold methods, this study discusses the cloud detection index and an automatic cloud detection algorithm based on the texture and neural network. The detection result is more accurate and effective.
从地表能量平衡原理入手，结合地面同步实测数据和气象数据，运用遥感蒸散法，建立了基于亚像元的半干旱地区区域双层蒸散模型(SRDEM)；其中，考虑到叶面积指数与植被的几何粗糙度之间有密切关系，引入叶面积指数计算动力传输粗糙度长度，从而改进显热通量算法。并通过地表缺水指数研究区域缺水状况，实现对表层土壤水分的定量反演。
地表缺水指数物理意义明确，定量精度较高，但涉及参数较多，且部分仍依赖于地面气象台站及同步观测的微气象资料，实时性相对较差。因此本次研究从分析植被冠层反射、发射光谱与植被冠层水的成因关系入手，直接运用卫星遥感参数——植被指数、植被组分温度、归一化植被水分指数，组成植被冠层水综合指数(VTWSI)，以反演植被冠层水分含量。
地表缺水指数是通过基于亚像元的双层蒸散模型提取的，它既考虑了土壤的水分蒸发，又考虑了植被的蒸腾。目前主要是通过建立地表缺水指数和实测土壤水分之间的经验关系式，提取土壤水分含量。考虑到半干旱地区植被冠层水主要由土壤水提供，但植被供水状况与植被水分实际状况之间存在着一定的“滞后”，而在监测区域缺水或旱情的实际应用中，人们更关心的是植被实际水分状况，但仅考虑植被冠层水对于半干旱地区的区域研究也是不够的。鉴于此，本文运用遗传规划算法，建立了地表缺水指数与植被冠层水分、表层土壤水分含量之间的定量关系。通过模拟值与实际值之间的对照分析及敏感性分析表明所建定量关系式是比较理想的，而且该定量关系在植被覆盖区不但考虑了土壤水，同时还加入了植被冠层水，说明能够应用到地表缺水的研究，从而改进了运用遥感蒸散法监测区域缺水的方法。
运用MODIS卫星遥感数据定量反演了地表温度、植被／土壤组分温度、地表反照率、叶面积指数、植被覆盖度等地表参数，作为模型计算的输入。为满足基于亚像元尺度
基于植被蒸散法的区域缺水遥感监测方法研究
的双层蒸散模型需求，本文选择遗传算法利用MODIS热红外多波段数据，实现了基于
亚像元尺度的植初土壤组分温度反演，为地表能量平衡、蒸散研究提供更精确的参数。
云一直是图像处理、图像分析的一大障碍，由于本研究区常见多云天气，为使从
遥感数据提取的参数更精确，在大气校正的前提下，进行云的检测分析是很有必要的。
在云检测多种研究方法中，一般采用阐值法来提取云的信息。由于闽值法带有一定主
观性，本文探讨了云检测指数和基于空间结构分析和神经网络的云自动检测算法，其
检测结果较常规闭值法精度高、效果好。
Heavy deficit of water is a key problem in semi-arid area of the northwest China, Inner Mongolia. This article mainly studies different degraded degrees grasslands. Aiming at the topic of regional water deficit, this paper has developed methods in MODIS data preprocessing, remote sensing quantitative inversion of land surface parameters, and improvement of regional water deficit monitoring using remote sensing. Main subjects are as follows.
According to surface energy balance theory, the semi-arid regional dual-layer evapotranspiration model (SRDEM) is established , using remote sensing monitoring of evapotranspiration at sub-pixel level, combined with the field measurement and meteorological data. Taking the relation between leaf area index and vegetation geometrical roughness into account, a leaf area index is introduced to compute the vegetation geometrical roughness so that the algorithm computing the sensible heat flux can be improved. The surface water deficit index (SWDI) is extracted using actual and potential evapotranspiration to study the regional water deficit condition. The regional soil water content is then assessed.
Surface water deficit index with clear definition and specific meanings has higher quantitative precision. But it refers to more parameters and some of them still rely on the meteorological observations of the surface weather station, which leads to limitation to real-time monitoring. Therefore, considering vegetation reflecting, emissive spectrum and the genetic relation of vegetation water, this research has introduced a vegetation water synthetical index (VTWSI) using parameters extracted from remote sensing data, vegetation index, normalized difference water index and vegetation component temperature, which can be used to inverse vegetation water content.
The surface water deficit index is extracted based on the dual-layer evapotranspiration model, including soil water evaporation and vegetation transpiration. As vegetation water is
mainly provided by soil water in semi-arid area, there is a time lag between the vegetation water provision condition and the vegetation water content. More attention has been paid to the vegetation water content in monitoring regional water deficit and ravages of drought, but it is not enough to only concern vegetation water content. Thus, this article establishes the remote sensing quantitative formula of regional water deficit using genetic plan based on surface water deficit index, surface soil water content and vegetation water content. The result shows that the quantitative formula is ideal in comparison between modeled and true values and through a sensitivity analysis. For vegetation area, the quantitative formula considers not only the soil water but also the vegetation water. It can be used to study surface water deficit, and to improve the method of regional water deficit monitoring using remote sensing.
Parameters, such as land surface temperature, vegetation/soil component temperatures, albedo, leaf area index and vegetation coverage etc., are inversed quantitatively using MODIS data as the input of model. To meet the need of dual-layer evapotranspiration model, this study adopts genetic algorithm to inverse component temperatures using two infrared bands of MODIS data, which can provide more accurate parameters for land surface energy balance and evapotranspiration study.
Cloud is a large obstacle to processing and analysis of remote sensing image, because it is often cloudy in this area. It is necessary to detect cloud for improving the parameters inversion precision using remote sensing data. Among numbers of cloud detection methods, threshold method is often used to extract cloud information. Considering some subjective factors of threshold methods, this study discusses the cloud detection index and an automatic cloud detection algorithm based on the texture and neural network. The detection result is more accurate and effective.
引文
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