Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Aims There are numerous grassland ecosystem types on the Tibetan Plateau. These include the alpine meadow and steppe and degraded alpine meadow and steppe. This study aimed at developing a method to estimate aboveground biomass (AGB) for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition (VIUPD) from the spectra to estimate AGB. First, we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type. Next, we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables. At last, we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately, all eight vegetation indices provided good estimates of AGB, with the best predictor of AGB varying among different ecosystems. When AGB of all the five ecosystems was estimated together using a simple linear model, VIUPD showed the lowest prediction error among the eight vegetation indices. The regression models containing dummy variables predicted AGB with higher accuracy than the simple models, which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index (VI). These results suggest that VIUPD is the best predictor of AGB among simple regression models. Moreover, both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models. Therefore, ground-based hyperspectral measurements are useful for estimating AGB, which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.     

一种估测小麦冠层氮含量的新高光谱指数

提出了一种估测小麦冠层氮含量的新高光谱指数--微分归一化氮指数(FD-NDNI)。以FieldSpec Pro FR地物光谱仪采集拔节后至孕穗前小麦的冠层光谱190份,随机抽取142份作为训练集,其余48份作为预测集。将光谱以小波阈值去噪法去噪后,利用其525、570 与730 nm处的一阶导数值,采用差值、比值以及归一化的方法构建了12种光谱指数以实现小麦冠层氮含量的估测,并与mNDVI705、mSR以及NDVI705等22种常用指数进行了比较分析。发现指数FD-NDNI对小麦冠层氮含量的估测结果最佳,其估测模型(指数形式)校正集决定系数(C-R²)与预测集决定系数(P-R²)分别达0.818与0.811,优于mNDVI705等常用指数。进一步分析表明,在各指数中,FD-NDNI对叶面积系数最不敏感,可最有效地避免冠层郁闭度等因素对氮含量估测的影响。为优化结果,采用最小二乘支持向量回归算法(LS-SVR)对模型进行了改进,当模型惩罚系数C与RBF核函数参数g取得最优解6.4与1.6时,其C-R²与P-R²分别提高至0.846与0.838,具有比指数模型更高的精度。结果表明:FD-NDNI是小麦冠层氮含量估测的优选指数,LS-SVR为建模的优选算法。     

冠层水平互花米草叶片光合色素含量的高光谱遥感估算模型

以闽江河口鳝鱼滩湿地互花米草(Spartina alterniflora)的实测冠层高光谱反射率和叶片光合色素含量(LPPC)为数据源,在分析LPPC与原始光谱反射率、一阶导数光谱反射率、22种已报道光谱指数和14种新构建的植被指数相关性的基础上,利用直线回归、指数回归、对数回归以及乘幂回归方法,系统地比较了36种植被指数在估算互花米草LPPC中的表现。研究表明:(1)一阶导数光谱反射率组合的植被指数用于估算互花米草的LPPC优于原始光谱反射率;(2)红边区域一阶导数光谱是估测互花米草LPPC的最佳波段;(3)对于单一色素含量的估算,叶绿素a(Chla)的最佳估算指数为FDNDVI[723,703];叶绿素b(Chlb)的最佳估算指数为FDRVI[723,525];类胡萝卜素(Cars)的最佳估算指数为FDNDVI[723,703];(4)对于使用统一参量同时估算Chla、Chlb、Cars,由FDRVI[723,703]建立的对数估算模型效果最佳。研究成果可为湿地植物生化参量反演提供参考,也可为闽江河口湿地入侵种互花米草的动态监测和生态评估管理提供有力的科学依据。     

Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity

Leaf area index (LAI) is one of the key biophysical parameters for understanding land surface photosynthesis, transpiration, and energy balance processes. Estimation of LAI from remote sensing data has been a premier method for a large scale in recent years. Recent studies have revealed that the within-canopy vertical variations in LAI and biochemical properties greatly affect canopy reflectance and significantly complicate the retrieval of LAI inversely from reflectance based vegetation indices, which has yet been explicitly addressed. In this study, we have used both simulated datasets (dataset I with constant vertical profiles of LAI and biochemical properties, dataset II with varied vertical profile of LAI but constant vertical biochemical properties, and dataset III with both varied vertical profiles) generated from the multiple-layer canopy radiative transfer model (MRTM) and a ground-measured dataset to identify robust spectral indices that are insensitive to such within canopy vertical variations for LAI prediction. The results clearly indicated that published indices such as normalized difference vegetation index (NDVI) had obvious discrepancies when applied to canopies with different vertical variations, while the new indices identified in this study performed much better. The best index for estimating canopy LAI under various conditions was D(920,1080), with overall RMSEs of 0.62–0.96 m²/m² and biases of 0.42–0.55 m²/m² for all three simulated datasets and an RMSE of 1.22 m²/m² with the field-measured dataset, although it was not the most conservative one among all new indices identified. This index responded mostly to the quantity of LAI but was insensitive to within-canopy variations, allowing it to aid the retrieval LAI from remote sensing data without prior information of within-canopy vertical variations of LAI and biochemical properties.     

内蒙古镶黄旗草地蝗虫潜在发生可能性评价

以内蒙古草地蝗虫产卵期、越冬期和孵化期的关键气象影响因子作为草地蝗虫气象适宜度指数构建的主要因子,对内蒙古镶黄旗2010年草地蝗虫潜在发生的气象适宜性进行评价;根据当年7月上中旬在镶黄旗的实地调查资料,选取海拔、坡向、土壤类型、土壤含砂量、植被类型、植被盖度、土地覆被类型7个相对稳定的生境因子,用模糊评判方法和3S（GIS、RS、GPS）技术对该旗草地蝗虫潜在发生的生境适宜性进行评价;最后通过构建气象-生境适宜性综合评价模型,得到该旗2010年草地蝗虫潜在发生可能性（POG）等级,并用2010年实测数据和2001-2010年历史数据对模型模拟的蝗虫发生位置和蝗灾发生面积进行验证.结果表明：用本文所建气象-生境适宜性综合评价模型对镶黄旗POG等级的评价结果是可靠的.该旗草地蝗虫潜在发生的气象适宜性等级非常一致,绝大多数为适宜等级;蝗虫潜在发生源地的空间异质性主要与生境因子有关,在海拔1300～1400 m的平地/东坡/南坡、植被盖度30%～50%的温带丛生禾草草原、土壤含砂量60%～80%的典型栗钙土的生境条件下,草地蝗虫潜在发生的可能性最高.     

基于遥感影像的植被指数研究方法述评

随着遥感技术的发展,植被指数作为用来表征地表植被覆盖和生长状况的度量参数,已经在环境、生态、农业等领域有了广泛的应用。本文在分析植被指数形成机制及影响因子的基础上,对其具有一定技术突破的典型植被指数进行了归纳分类与比较分析,并评价了各自的优势和局限性。植被指数按遥感数据采集的平台可以分为航空植被指数和航天植被指数两大类,其中航天植被指数又可以分为基于波段简单线性组合的植被指数、消除影响因子的植被指数和针对高光谱遥感及热红外遥感的植被指数三类。最后就植被指数应用中存在的问题以及发展前景谈了一些看法:植被指数数目繁琐重复,急待规范条理化;植被指数应用领域不同,使用者时要慎重;植被指数影响因子很多,具体使用时应适时修正;植被指数公式繁琐阻碍其应用,应开发植被指数产品;遥感技术日新月异,积极研发新的植被指数。     

基于遥感的光合有效辐射吸收比率(FPAR) 估算方法综述

光合有效辐射吸收比率(FPAR)是反映植被生长过程的重要生理参数,是陆地生态系统模型的关键参数,是反映全球气候变化的重要因子。基于遥感的FPAR估算方法是获取区域乃至全球尺度FPAR的有效方法。目前,主要形成了植被指数法和机理法两类方法,植被指数法是建立FPAR与植被指数的经验统计模型,简单、计算效率高;机理法则从物理模型上进行FPAR的求解与反演,机理明晰、可行性强。然而,由于FPAR本身的复杂性以及环境因素、遥感数据质量的影响,导致了估算方法面临诸多不确定性问题。为了解决这些不确定性问题以及满足生态过程深入研究的需求,将进一步注重FPAR的机理研究、先验知识的获取与积累,构建长时间序列FPAR以及高时空的FPAR算法研究。     

Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

The need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

     

基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型

冬小麦冠层叶片氮含量是反映其产量与品质的重要指标,构建高普适性、高精准性冬小麦冠层叶片氮含量高光谱反演模型对提高其监测效率具有重要意义。以不同地点、品种、年份、施氮水平、生育期的大田试验数据为基础,基于两波段光谱植被指数NDRE和550 nm光谱反射率组合构建一个三波段植被指数NEW-NDRE,并与11个传统冬小麦冠层叶片氮素光谱指数进行比较。结果表明: NEW-NDRE及传统植被指数中NDRE、NDDA、RI-1dB与冬小麦冠层叶片氮含量的相关性较好;其中,灌浆初期NEW-NDRE与冬小麦冠层叶片氮含量相关性最好,决定系数R²为0.9,均方根误差(RMSE)为0.4;经独立数据检验,以NEW-NDRE为变量建立的冬小麦冠层叶片氮含量反演模型的平均相对误差(RE)为9.3%,明显低于以NDRE、NDDA、RI-1dB为变量的模型RE。总体上,新构建的NEW-NDRE对冬小麦冠层叶片氮含量的模拟能力显著优于传统指数,减弱了试验条件的限制性,可为精准施肥提供新的技术支撑。     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

森林生态系统立地指数的遥感分析

森林生态系统立地指数分析和立地质量的评估是森林生态系统经营管理和造林营林的重要理论基础与规划方法,也是研究森林生态系统生产力的重要内容.由于技术的限制,迄今为止,还没有实现森林生态系统立地空间分布格局的分析和开展立地条件随时间动态变化的研究.卫星遥感为大面积研究森林生态系统的生产力及其空间分布格局和动态提供了一条重要的途径.以云杉为对象,利用卫星遥感为研究手段,在岷江上游的四川西北部松潘镇江关流域研究森林生态系统立地指数的空间分布特点,探索有关遥感反演模型的建立,并通过有关精度的评估分析这种高技术应用的价值和潜力.研究结果表明,遥感植被指数NDVI和TNDVI与野外实测云杉立地指数（SI）基本为线性相关.通过对模型模拟结果和实际测定结果的比较研究,发现在1：1比例的分析图中,NDVI和TNDVI的遥感反演模型都有很好的拟合效果与较高的精度,说明通过遥感植被指数的方法测定森林立地指数具有较高的实用价值.     

Hyperspectral remote sensing of plant pigments

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.     

叶冠尺度野鸭湖湿地植物群落含水量的高光谱估算模型

利用高光谱遥感技术定量估测野鸭湖湿地植被含水量,对于监测和诊断野鸭湖湿地植被的生理状况及生长趋势具有重要意义,也能够为高光谱遥感影像在野鸭湖湿地植被含水量诊断中的实际应用提供理论依据和技术支持.采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植被冠层和叶片的光谱,并测定了对应的含水量.以上述实测数据为基础,首先以芦苇为例初步探明了不同含水量水平下典型湿地植被冠层和叶片光谱反射率的响应模式,然后采用相关性及单变量线性与非线性拟合分析技术,从冠层和叶片两种层次,对不同尺度下的含水量与“三边”参数及高光谱植被指数进行了分析拟合,并采用交叉检验中的3K-CV方法对估算模型进行了测试和检验,确立了不同尺度下野鸭湖湿地植被含水量的定量监测模型.结果表明:(1)随着含水量水平的增加,芦苇冠层与叶片光谱在可见光波段(350-760 nm)和红外波段(760-2500 nm)的反射率均呈逐渐降低趋势.(2)不同尺度含水量与选取的光谱特征参数整体上相关性较强,与“三边”参数基本上都呈极显著相关,相关系数最大达到0.906;与高光谱指数全部呈极显著相关,相关系数最小为0.455,最大达到0.919,并通过选取不同尺度上相关性最佳的光谱特征参数,分别基于“三边”参数和高光谱植被指数构建了不同尺度下的含水量估算模型.其中,冠层尺度下,黄边面积(SDy)与SRWI( Simple Ratio Water Index)的估算效果最好,估算模型分别为y=-9.462x2 -2.671x+0.608和y=0.219e1.010x;叶片尺度下,红边面积(SDr)与WI( Water Index)的估算效果最好,估算模型分别为y=0.562x+0.376和y=2.028x2 -0.476x-1.009.通过3K-CV的交叉验证,不同尺度下的含水量估算模型均取得了较为理想的预测精度,预测精度的最小值为94.92％,最大值为97.06％,表明估测模型具有较高的可靠性与普适性.(3)高光谱植被指数与含水量拟合方程的拟合度相对高于“三边”参数与之建立方程的拟合度,说明多波段组合的光谱特征参数更适合含水量的判别.     

Developing and applying novel spectral feature parameters for classifying soil salt types in arid land

Soil salinization is a major desertification process that threatens especially the stability of arid ecosystems. There is an urgent need for intensive monitoring and quick assessment of salinization through remote sensing as a tool for combating desertification in such ecosystems. Recent researches have revealed that in order to retrieve soil salt contents accurately from hyperspectral reflectance, a pre-knowledge of salt types is required, which greatly outlines the spectral features of saline soil reflectance. In this study, a set of feature parameters have been developed after a thorough investigation of spectral responses to different soil salt types and salt contents for quick and accurate classification of soil salt types. The application has been validated using three independent datasets composed from: laboratory experiments (dataset I), in-situ field measurements (dataset II), and satellite-borne Hyperion image (dataset III). For comparison, four other common classification algorithms have been validated using the same datasets. The results showed that the new approach proposed in this study performed well with not only single-type but also multiple-type salts for which the four common algorithms performed rather fairly. Furthermore, validating using datasets II and III showed that the newly proposed approach had a stable performance while the other four failed, indicating the advantage of the new approach. The feature parameters developed in this study hence provide a novel and efficient approach for salt type classification from reflectance spectra, and we foresee its potential applications on large-scale soil salt type mapping towards better understanding soil salinity characterization from remote sensing data.     

Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data

There has been a great deal of Interests in the estimation of grassland biophysical parameters such as percentage of vegetation cover （PVC）, aboveground biomass, and leaf-area index with remote sensing data at the canopy scale. In this paper, the percentage of vegetation cover was estimated from vegetation indices using Moderate Resolution Imaging Spectroradiometer （MODIS） data and red-edge parameters through the first derivative spectrum from in situ hypserspectral reflectance data. Hyperspectral reflectance measurements were made on grasslands in Inner Mongolia, China, using an Analytical Spectral Devices spectroradiometer. Vegetation indices such as the difference, simple ratio, normalized difference, renormalized difference, soil-adjusted and modified soil-adjusted vegetation indices （DVI, RVI, NDVI, RDVI, SAVI L=0.5 end MSAVI2） were calculated from the hyperspectral reflectance of various vegetation covers. The percentage of vegetation cover was estimated using an unsupervised spectral-contextual classifier automatically. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that MSAVI2 and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression, respectively.     

Spectral response of the seagrass Zostera noltii with different sediment backgrounds

The efficiency of vegetation indices (VIs) to estimate the above-ground biomass of the seagrass species Zostera noltii Hornem. from remote sensing was tested experimentally on different substrata, since terrestrial vegetation studies have shown that VIs can be adversely influenced by the spectral properties of soils and background surfaces. Leaves placed on medium sand, fine sand and autoclaved fine sand were incrementally removed, and the spectral reflectance was measured in the 400–900 nm wavelength range. Several VIs were evaluated: ratios using visible and near infrared wavelengths, narrow-band indices, indices based on derivative analysis and continuum removal. Background spectral reflectance was clearly visible in the leaf reflectance spectra, showing marked brightness and spectral contrast variations for the same amount of vegetation. Paradoxically, indices used to minimize soil effects, such as the Soil-Adjusted Vegetation Index (SAVI) and the Modified second Soil-Adjusted Vegetation Index (MSAVI₂) showed a high sensitivity to background effects. Similar results were found for the widely used Normalized Difference Vegetation Index (NDVI) and for Pigment Specific Simple Ratios (PSSRs). In fact, background effects were most reduced for VIs integrating a blue band correction, namely the modified specific ratio (mSR₍₇₀₅₎), the modified Normalized Difference (mND₍₇₀₅₎), and two modified NDVIs proposed in this study. However, these indices showed a faster saturation for high seagrass biomass. The background effects were also substantially reduced using Modified Gaussian Model indices at 620 and 675 nm. The blue band corrected VIs should now be tested for air-borne or satellite remote sensing applications, but some require sensors with a hyperspectral resolution. Nevertheless, this type of index can be applied to analyse broad band multispectral satellite images with a blue band.     

于星载通道光谱指数与小麦冠层叶片氮素营养指标的定量关系

利用空间遥感信息大面积监测小麦冠层氮素营养状况和生产力指标具有重要意义和应用前景.本研究基于不同施氮水平下小麦冠层反射光谱信息,利用响应函数模拟基于不同卫星通道构建的光谱指数(包括单波段、比值光谱指数和归一化光谱指数),分析基于星载通道的光谱指数与小麦冠层叶片氮素营养指标的定量关系,确定监测小麦冠层叶片氮素营养的较好卫星传感器和光谱波段,建立小麦冠层氮素营养指标监测方程.结果表明：利用NDVI(MSS₇, MSS₅)、NDVI(RBV₃, RBV₂)、TM₄、CH₂、MODIS₁和MODIS₂遥感数据可以预估小麦叶片氮含量(LNC),其决定系数（R₂）在0.60以上;应用NDVI(PB₄, PB₂)、NDVI(CH₂, CH₁)、NDVI(MSS₇, MSS₅)、RVI(MSS₇, MSS₅)、MODIS₁和MODIS₂可以预测小麦叶片氮积累量(LNA),其R₂大于0.86.比较而言,NDVI(MSS₇, MSS₅)和NDVI(PB₄, PB₂)分别为预测小麦LNC和LNA的适宜星载通道光谱参数.     

辐射传输模型多尺度反演植被理化参数研究进展

植被是生态系统最重要的组成成分之一,许多与植被有关的物质能量交换过程都与植被的理化参数密切相关,因此定量估算植被的理化参数含量对监测植被生长状况、森林火灾预警以及研究全球碳氮循环过程等都具有重要意义.在众多定量反演植被理化参数的方法中,基于数学、物理学以及生物学的基本理论建立起来的辐射传输模型受到越来越多的关注.辐射传输模型描述了植被与入射辐射之间的相互作用过程和特征,相对于传统的经验/半经验方法,辐射传输模型物理意义明确,具有稳定性和可移植性强的特点.在分析国内外最新相关研究的基础上,首先从植被叶片、冠层和像元3个不同的尺度阐述反演植被理化参数的辐射传输模型.叶片尺度上主要介绍PROSPECT模型和LIBERTY模型;冠层尺度上主要介绍SAIL冠层辐射传输模型以及PROSPECT与SAIL耦合的PROSAIL叶片-冠层辐射传输模型;像元尺度的植被理化参数反演目前主要采用冠层尺度的辐射传输模型.其次,分析尺度变化下植被理化参数遥感反演所面临的主要问题,如不同尺度下模型参数敏感性的变化、辐射传输模型的选取以及混合像元的影响等.最后,总结展望植被理化参数反演多模型与多种数据源相互结合的研究趋势,以及将来具有高空间分辨率的高光谱遥感卫星升空后所带来的发展前景.     

Research progress on monitoring vegetation water content by using hyperspectral remote sensing

植被含水量是陆地植被重要的生物物理特征, 其定量遥感反演有助于植被干旱胁迫的实时监测与诊断评估。该文系统综述了国内外利用高光谱遥感评估植被水分状况的4个常见植被水分指标——冠层含水量、叶片等量水厚度、活体可燃物湿度和相对含水量的概念及其遥感估算方法研究进展, 评述了植被含水量高光谱遥感估算各类方法的优缺点, 探讨了植被含水量高光谱遥感估算目前存在的问题, 并提出进一步的研究任务, 即服务于植被干旱胁迫的高光谱遥感监测、预警与评估。     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力(GPP)是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：(1)基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高(R²分别为0.52和0.80),但在模拟草地、农田生态系统生产力时存在较明显的低估(RE>20%);基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高(R²分别为0.91和0.81),同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。(2)水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估...