利用双波长方法对非均匀地形人工林蒸散的测量

千米级地表蒸散通常利用遥感影像并结合经验或半经验模型进行估算,且估算结果还需结合地表实测数据进行验证,但实际中很难获得与遥感像元尺度匹配的实测数据.本研究在黄河小浪底森林生态系统定位研究站人工林的生长期(2016年7月1日-10月19日),基于近红外与微波联合观测技术进行千米级地表蒸散的观测分析.结果表明:近红外与微波联合技术在不同天气条件下均能表现出良好的日变化特征,且整体能量闭合率可达0.87.该技术对空气湿度的变化敏感,土壤湿度的变化对该项技术的结果影响较小.不同波文比条件和大气稳定度均会使该技术的观测产生不同程度的误差,与涡动相关方法相比,误差在合理范围.采用双波长方法研究得出,该区域的最大日蒸散量为3.5 mm,总蒸散量为162.4 mm.     

基于数字相机、ASTER和MODIS影像综合测量植被盖度

选择我国北方温带典型草原作为研究对象,基于Bottom-up方法,采用地表实测和多尺度遥感综合测量的方法,建立基于地表实测与多尺度遥感数据综合测量的两阶段植被盖度经验模型。此外,还将该模型与常用的亚像元分解模型相比较,结果表明:1)两阶段经验模型可以较好地实现将地面数据扩展到中尺度空间范围,从而完成数据空间尺度的转换,提高大区域草地植被盖度的测量精度;2)MODIS遥感影像数据,结合地面数据和ASTER遥感影像数据可以较好地在区域范围内对北方典型草原的植被盖度进行估测;3)目前常用的亚像元分解模型,应用于中空间分辨率的MODIS影像,估测北方温度典型草原植被盖度的精度不够理想。     

基于NOAA PAL数据集的地表蒸散遥感估算方法

基于NOAA AVHRR气象卫星长时间序列10 d合成的PAL数据集（分辨率8 km×8 km）以及地表能量平衡原理和“VI-T_s”方法,建立了地表蒸散的遥感估算方法,该方法不需要地面气象观测数据的支持,所需参数可直接从遥感数据反演或推算,并选择国际上著名的遥感蒸散模型——SEBS模型对新建模型进行了验证比较.结果表明：新建模型和SEBS模型模拟的地表蒸散值及其季节性变化趋势非常一致,说明新构建模型的模拟结果比较可靠,能够反映地表蒸散的实际情况.新建地表蒸散遥感估算模型可操作性强,为利用长时间序列的卫星遥感数据研究我国乃至全球地表蒸散的时空变化规律提供了一个新的途径.     

分布式模型在流域蒸散模拟中的应用与验证

根据四川杂谷脑河流域上游地区1989～2000年气象站常规观测数据,应用分布式模型方法,考虑流域的空间异质性及时空变异性,选择离散单元格尺度为500 m,时间步长为1 d,采用Penman-Monteith公式的改进形式,估算流域多年平均潜在蒸散量的时空分布;结合流域下垫面特点,估算逐日实际蒸散量的时空分布;并将模型模拟的多年平均值与研究区同期水量平衡法计算结果相比,相对误差为+3.47%且时空分布合理.为流域分布式降雨-径流模型提供了可靠的实际蒸散量模拟方法.     

分布式模型在流域蒸散模拟中的应用与验证

根据四川杂谷脑河流域上游地区1989～2000年气象站常规观测数据,应用分布式模型方法,考虑流域的空间异质性及时空变异性,选择离散单元格尺度为500 m,时间步长为1 d,采用Penman-Monteith公式的改进形式,估算流域多年平均潜在蒸散量的时空分布;结合流域下垫面特点,估算逐日实际蒸散量的时空分布;并将模型模拟的多年平均值与研究区同期水量平衡法计算结果相比,相对误差为+3.47%且时空分布合理.为流域分布式降雨-径流模型提供了可靠的实际蒸散量模拟方法.     

遥感GPP模型在中国地区多站点的应用与比较

在区域和全球尺度上估算植被总初级生产力(GPP)对理解陆地生态系统的碳循环具有重要意义。由于地表异质性的存在,局限在站点尺度上的观测数据无法直接扩展到更大空间尺度的区域上。通过与地面观测数据相结合,遥感成为实现植被GPP空间扩展的主要工具。但是现有模型对气象数据依赖较多,且在不同气象数据集的驱动下,模拟结果间会有差异,进而产生不确定性。建立以遥感数据为主的GPP模型(简称遥感GPP模型),使其易于在区域和全球尺度上应用,是解决上述问题的一个可行方案。该研究使用TG(temperature and greenness model)和VI(vegetation index model)两个遥感GPP模型,结合中国通量观测研究联盟(China FLUX)的台站数据,对中国典型植被类型的GPP进行了模拟、比较与评估,旨在进一步提高遥感GPP模型在中国区域的适用性。结果表明:(1)TG和VI模型选用的遥感参数均与GPP观测值有较高的相关性,都可以得到可信的光合转换系数m和a。基于与夜间地表温度平均值的相关关系,m和a在空间尺度上得到了扩展,这使得TG和VI都可以应用到区域尺度上。(2)TG和VI模型的模拟值与实测值间的相关性大多较高,决定系数(R~2)多在0.67以上。但不同台站间的误差变动较大,TG模型的均方根误差为0.29–6.40 g·m~(–2)·d~(–1),VI模型的均方根误差为0.31–7.09 g·m~(–2)·d~(–1)。(3)总体而言,TG模型的表现优于VI,尤其在海拔或纬度较高、以温度限制为主的台站,TG模型的模拟效果较好。上述结果初步揭示遥感GPP模型具备了在区域尺度上应用的潜力。     

地表反照率不同计算方法对干旱区流域蒸散反演结果的影响——以新疆三工河流域为例

地表蒸散是维持地球表面水量平衡和热量平衡的重要环节,SEBAL模型作为一种快速且有效的反演地表蒸散的遥感物理模型方法,在地表蒸散研究中得到广泛应用。地表反照率作为影响地表能量平衡的重要因素,同时也是SEBAL模型的重要输入参数,因此不同的地表反照率计算方法对SEBAL模型的反演结果有重要影响。以新疆三工河流域为研究区,利用Landsat8 OLI/TIRS数据,以应用最为广泛的Smith地表反照率计算法和Liang地表反照率计算法两种方法计算地表反照率,并输入SEBAL模型中反演日蒸散量,比较分析两种地表反照率计算方法对蒸散反演结果的影响,得出以下结论:(1)两种地表反照率计算方法下经SEBAL模型得到的日蒸散量与实测值拟合程度均较高,不同年份下线性拟合决定系数大于0.75,但是使用Smith方法计算出的地表反照率结合SEBAL模型得到的日蒸散量与实测值拟合程度更高;(2)通过RMSE等精度指标比较两种地表反照率计算方法下基于SEBAL模型反演的日蒸散量,结果显示,Smith地表反照率计算方法下反演的日蒸散量精度略高;(3)Smith地表反照率计算方法下最终得到的区域日均蒸散量高于使用Liang地表反照率计算方法最终得到的区域日均蒸散量,夏季差异最大,差异为0.64 mm/d,其他季节差异较小,差异约为0.2 mm/d。(4)进一步比较研究日内两种地表反照率计算方法得到的地表反照率,结果显示,Smith地表反照率计算法得到的地表反照率均值均小于同时期Liang地表反照率计算法得到的地表反照率均值。     

南京地区蒸散发降尺度研究--基于增强型时空自适应反射融合模型

蒸散发是水文循环的重要组成部分,获取高时空分辨率的数据能够更加精细化蒸散发的时空变化规律,对于水资源管理、生态水文过程量化具有重要意义。由于单一传感器反演的蒸散发无法同时具有高空间和高时间分辨率,以南京地区为例,首先结合Landsat-8遥感影像数据和气象数据,采用基于能量平衡原理的SEBS模型估算日蒸散量。在此基础上,选取典型区域采用基于增强型时空自适应反射融合模型(ESTARFM)将估算的蒸散发结果与低空间分辨率的MOD16A2蒸散发产品数据进行时空融合降尺度研究,并评价模型的融合精度。结果表明:(1)SEBS模型估算的蒸散发结果与蒸发皿折算后的数据、MOD16A2产品数据的平均相对误差分别为0.14 mm/d和0.22 mm/d。(2)南京地区蒸散量季节差异明显,表现为夏季＞秋季＞冬季;各区在夏季的日平均蒸散量差异也较大,六合区蒸散量最大,秦淮区最小;另外,蒸散量分布受土地利用类型的影响,总体上表现为水域＞林地＞耕地＞草地＞其他,且植被覆盖度较高的区域蒸散量较大。(3)基于ESTARFM模型融合的蒸散发结果与基于Landsat-8遥感影像反演的蒸散发数据在空间分布上具有相似性,二者相关系数为0.74。在全球气候变化的背景下,本研究可为蒸散发数据集时空分辨率的提高提供参考,同时也能够为南京地区水循环过程和水资源管理研究提供数据支撑。     

遥感数据支持下不同地表覆盖的区域蒸散

将地表能量平衡系统(SEBS)扩展成遥感日蒸散估算模型,利用MODIS遥感数据估算了黄淮海地区的区域蒸散,并在地理信息系统的支持下分析了不同地表覆盖下的区域蒸散统计分布特征.在缺乏各地表覆盖类型相应蒸散量实测值进行对比的情况下,以2001年4月17日估算的日蒸散量为例,通过各地表覆盖类型日蒸散量间的相互对比分析表明,SEBS估算的区域日蒸散量具有一定的合理性.分析结果表明：在黄淮海地区,荒地具有最低的蒸散量;森林、灌木、草地等地表覆盖类型具有中等的蒸散量;而水体、湿地以及耕地具有较高的蒸散量.可能由于包含绿地和水面,城镇用地的蒸散量也较高.土壤含水量的空间差异性导致森林、灌木、草地和耕地等地表覆盖类型的蒸散量具有明显的空间差异性.耕地蒸散量的空间差异性可以为制定合理、高效的农田灌溉计划提供指示作用.SEBS遥感日蒸散模型的局限性在于有可能低估水体和湿地等地表覆盖类型的蒸散量.     

低丘红壤区农田水热通量变化特征及气候学足迹

农田水热通量的变化特征以及气候学足迹分析对加强区域气候资源管理和提高红壤地区水热资源利用率有重要意义.利用低丘红壤区的大孔径闪烁仪和自动气象站数据,在保证数据质量的基础上,详细分析了低丘红壤区农田在非雨季、作物生长旺期不同时间尺度水热通量的变化特征和观测通量的源区分布特征.结果表明: 水热通量的日变化呈单峰型,但与晴天相比较,多云天的日变化曲线波动更为复杂;无论是旬尺度还是月尺度, 8月的水热通量整体上都大于9月,且净辐射通量都更多地用于潜热交换,但9月潜热通量所占净辐射比例相较于8月有所减少,感热通量则相反;受气象条件(尤其是风)、稳定度和下垫面状况影响,不同时间尺度的观测通量源区特征不同;结合下垫面作物来看,不同时间尺度源区的通量贡献来源也不同.     

Estimating the regional evapotranspiration in Zhalong wetland with the Two-Source Energy Balance (TSEB) model and Landsat7/ETM+ images

This paper aims to integrate a numerical model, vegetation index, and regional evapotranspiration estimation to assess the water cycle in Zhalong wetland, China with the aid of remote sensing technologies. An interdisciplinary analysis was performed to study the eco-hydrological characteristics of the wetland ecosystem. In particular, a new solution for solving the Two-Source Energy Balance (TSEB) was developed and applied. This new solution method is based on a definition of the aerodynamic temperature in a two-layer ground surface structure. Following this new solution for TSEB, the outputs of the Surface Energy Balance Algorithm (SEBAL) may be used as the inputs for TSEB to avoid the complex calculations of the bulk boundary layer and soil surface resistance. The new solution method makes the implementation of TSEB much easier when bi-directional thermal infrared remote sensing images are not available. Daily evapotranspiration and vegetation index of the wetland were calculated from six scenes of Landsat7/ETM+ remote sensing images acquired during 2001 and 2002 in our case study. Spatial distribution of daily evapotranspiration was also provided to help realize the wetland condition. The seasonal changes of vegetation index and evapotranspiration for some typical wetland ground surfaces were analyzed and presented as well to explore the multitemporal variations of plant species holistically. Ultimately, such integrative analysis aids in understanding the general biological patterns associated with these wetland vegetation cover, while specific situations of the wetland ecosystem can be recognized.     

Remote sensing of riverine landscapes

1. The fashion for examining riverine landscapes is changing as our technical instruments, from microprobes to satellites, expands to be able to examine the spatial and temporal relationships among biota, hydrology and geomorphology across scales from microhabitats to channel units to valleys to catchments. 2. The range of successful applications from remote sensing analyses of riverine landscapes has especially increased with the launch of many new instruments that record data across the electromagnetic spectrum. Engineering of the instruments has also improved such that knowledge of the radiometric properties of the digital data is more complete as a result of better instrumentation and installation of on‐board calibrations systems for many instruments. 3. With the development of faster processing on cheaper computers, it is now common for comprehensive data sets to be processed through algorithms that previously could only be applied to relatively small (<1 Mbyte) rasters of data. This technical advance is especially important for the statistical algorithms such as principal components and spectral mixture analysis that can decompose gradients in the spectral data. The combined effect is the production of regional views of riverine landscapes separated into components of water, vegetation and soil. 4. The landscape properties of riverine landscapes that have been most successfully measured with remote sensing data include community and habitat level classification and connectivity of waterbodies with optical and radar data. Laser and radar altimetric data measured from aircraft provide land elevations at resolutions as fine as decimetres. A remaining challenge is to achieve an exact match between the categories of landscape classification from the remote sensing analysis and data from field surveys or model outputs. 5. In contrast to many landscape properties, several water properties are now routinely measured as absolute values (water surface elevation, temperature, surface sediment concentration and algal concentration) with remote sensing. New analyses of both passive and active radar data in addition have led to measurements of inundation and wetness that are providing valuable insight into the dynamics of flooding and its effect on riverine landscapes. 6. Finally, an effective examination of the variability in landscape cover includes additional analyses of remote sensing products using pattern metrics that measure the scale of patchiness and distribution of the landscape properties. These types of variability measures at the regional scale contribute to an increased understanding of the way in which spatial heterogeneity of riverine landscapes varies across scales and how landscape filters (sensu Poff, 1997) influence the evolution of these diversity patterns.     

陆地植被净初级生产力计算模型研究进展

植被净初级生产力(NPP)研究是全球变化与陆地生态系统的核心内容之一。在回顾NPP模型研究的基础上,综合分析了气候模型、生态生理过程模型、光能利用率模型各自的优缺点,并对NPP模型研究做出展望。生态生理过程模型是当前陆地NPP估算研究的主要手段,而区域尺度转换则是它所面临的关键问题。近年来光能利用率模型已成为NPP估算的一种全新手段,它利用遥感所获得的全覆盖数据,使区域及全球尺度的NPP估算成为可能,但其生态学机理还有待于进一步研究。已有研究表明,“生态一遥感耦合模型”将是陆地NPP估算的主要发展方向,它融合了生态生理过程模型和光能利用率模型的优点,增强了NPP模型估算的可靠性和可操作性。     

Application and comparison of remote sensing GPP models with multi-site data in China

Aims Estimation of gross primary productivity (GPP) of vegetation at the global and regional scales is important for understanding the carbon cycle of terrestrial ecosystems. Due to the heterogeneous nature of land surface, measurements at the site level cannot be directly up-scaled to the regional scale. Remote sensing has been widely used as a tool for up-saling GPP by integrating the land surface observations with spatial vegetation patterns. Although there have been many models based on light use efficiency and remote sensing data for simulating terrestrial ecosystem GPP, those models depend much on meteorological data; use of different sources of meteorological datasets often results in divergent outputs, leading to uncertainties in the simulation results. In this study, we examines the feasibility of using two GPP models driven by remote sensing data for estimating regional GPP across different vegetation types. Methods Two GPP models were tested in this study, including the Temperature and Greenness Model (TG) and the Vegetation Index Model (VI), based on remote sensing data and flux data from the China flux network (ChinaFLUX) for different vegatation types for the period 2003-2005. The study sites consist of eight ecological stations located in Xilingol (grassland), Changbaishan (mixed broadleaf-conifer forest), Haibei (shrubland), Yucheng (cropland), Damxung (alpine meadow), Qianyanzhou (evergreen needle-leaved forest), Dinghushan (evergreen broad-leaved forest), and Xishuangbanna (evergreen broad-leaved forest), respectively. Important findings All the remote sensing parameters employed by the TG and VI models had good relationships with the observed GPP, with the values of coefficient of determination, R², exceeding 0.67 for majority of the study sites. However, the root mean square errors (RMSEs) varied greatly among the study sites: the RMSE of TG ranged from 0.29 to 6.40 g·m^-2·d^-1, and that of VI ranged from 0.31 to 7.09 g·m^-2·d^-1, respectively. The photosynthetic conversion coefficients m and a can be up-scaled to a regional scale based on their relationships with the annual average nighttime land surface temperature (LST), with 79% variations in m and 58% of variations in a being explainable in the up-scaling. The correlations between the simulated outputs of both TG and VI and the measured values were mostly high, with the values of correlation coefficient, r, ranging from 0.06 in the TG model and 0.13 in the VI model at the Xishuangbanna site, to 0.94 in the TG model and 0.89 in the VI model at the Haibei site. In general, the TG model performed better than the VI model, especially at sites with high elevation and that are mainly limited by temperature. Both models had potential to be applied at a regional scale in China.     

基于贝叶斯模型平均的蒸散遥感产品集成——以三江源国家公园为例

蒸散发(ET)是陆表水热过程的一个基础通量,不同模型基于的概念、假设、应用尺度等诸多差异给ET的准确模拟带来了多种不确定性。本研究以三江源国家公园为例,应用贝叶斯模型平均(BMA)方法,通过通量塔观测值对模型进行训练,并综合PT-JPL、ARTS-GIMMS3g、ARTS-MODIS、MOD16和SSEBo 5个模型结果,以提高ET的估测精度。结果表明: 5个模型结果可以捕捉海北高寒草地通量塔观测ET的季节变化,可解释观测ET季节变异的64%～86%,均方根误差(RMSD)的范围为0.47～0.76 mm·(8 d)^-1;基于BMA得到的ET的解释能力提高至89%,RMSD降低至0.43 mm·(8 d)^-1。2003—2015年,三江源国家公园地表ET总体呈不显著增加的趋势,在全区尺度上,温度和降水对蒸散的影响不显著;但在长江源园区,降水和气温对其影响达到显著水平。气温和降水对蒸散发有积极的影响,但不同园区之间的地理差异导致蒸散发也出现不同的变化趋势。本研究为其他多源数据的集成分析提供了方法参考,所集成的蒸散数据可以有效降低原各自模型的不确定性,为区域水热变化研究提供了更为精确的数据基础。这对于更好地认识气候变化背景下的水循环过程具有重要意义。     

Energy and Mass Exchange Over Incomplete Vegetation Cover

Competition for fresh water between agriculture and domestic and industrial uses is increasing worldwide. This is forcing subsistence and commercial agriculture to produce more with less water. Consequently, it is crucial to properly and efficiently manage water resources. This requires accurate determination of crop water loss into the atmosphere, which is greatly influenced by the exchange of energy and mass between the surface and the atmosphere. Measurement of these exchange processes can best be accomplished by micrometeorological methods. However, most micrometeorological methods are very expensive, difficult to set up, require extensive post-data collection corrections and/or involve a high degree of empiricism. This review discusses estimation of evapotranspiration using relatively inexpensive micrometeorological methods in temperature-variance (TV), surface renewal (SR) and mathematical models. The TV and SR methods use high frequency air temperature measurements above a surface to estimate sensible heat flux (H). The latent heat flux (λE), and hence evapotranspiration, is calculated as a residual of the shortened surface energy balance using measured or estimated net radiation and soil heat flux, assuming surface energy balance closure is met. For crops with incomplete cover, the disadvantage of these methods is that they do not allow separation of evapotranspiration into soil evaporation and plant transpiration. The mathematical models (single- and dual-source) involve a combination of radiation and resistance equations to determine evapotranspiration from inputs of automatic weather station observations. Single-source models (Penman-Monteith type equations) are used to determine evapotranspiration over homogeneous surfaces. The dual-source models, basically an extension of single-source models, determine soil evaporation and plant transpiration separately over heterogeneous or sparse vegetation. These mathematical models have also been modified to accommodate inputs of remotely-sensed radiometric surface temperatures that enable estimation of evapotranspiration on a regional and global scale.     

The land–atmosphere water flux in the tropics

Tropical vegetation is a major source of global land surface evapotranspiration, and can thus play a major role in global hydrological cycles and global atmospheric circulation. Accurate prediction of tropical evapotranspiration is critical to our understanding of these processes under changing climate. We examined the controls on evapotranspiration in tropical vegetation at 21 pan-tropical eddy covariance sites, conducted a comprehensive and systematic evaluation of 13 evapotranspiration models at these sites, and assessed the ability to scale up model estimates of evapotranspiration for the test region of Amazonia. Net radiation was the strongest determinant of evapotranspiration (mean evaporative fraction was 0.72) and explained 87% of the variance in monthly evapotranspiration across the sites. Vapor pressure deficit was the strongest residual predictor (14%), followed by normalized difference vegetation index (9%), precipitation (6%) and wind speed (4%). The radiation-based evapotranspiration models performed best overall for three reasons: (1) the vegetation was largely decoupled from atmospheric turbulent transfer (calculated from Ω decoupling factor), especially at the wetter sites; (2) the resistance-based models were hindered by difficulty in consistently characterizing canopy (and stomatal) resistance in the highly diverse vegetation; (3) the temperature-based models inadequately captured the variability in tropical evapotranspiration. We evaluated the potential to predict regional evapotranspiration for one test region: Amazonia. We estimated an Amazonia-wide evapotranspiration of 1370 mm yr⁻¹, but this value is dependent on assumptions about energy balance closure for the tropical eddy covariance sites; a lower value (1096 mm yr⁻¹) is considered in discussion on the use of flux data to validate and interpolate models.     

森林水源涵养功能的多尺度内涵、过程及计量方法

近年来国内森林生态系统服务功能研究较多,但应用价值不高,其科学性受到诸多质疑。从森林水源涵养功能的多尺度内涵、过程以及其计量方法出发,对国内外研究动态和发展趋势进行了总结分析,重新审视森林水源涵养功能的研究意义,探讨森林水源涵养功能的多尺度特征。从森林水源涵养功能作用的空间尺度上看,其拦蓄洪水削减洪峰的功能仅在较小尺度上有效,而调节径流的功能只有当森林土壤的入渗量超过蒸散量时,才可能有更多地下水补给河道径流,进而增加旱季河道流量。同时森林水源涵养功能也具有明显的时间尺度特征,具体表现为在次降水事件中,由于蒸散发量较小,森林水源涵养功能的物质量等于森林不同层次的截留量,在功能上表现为拦蓄降水;在长时间尺度上,由于林地蒸散要耗去大量水分,森林水源涵养功能的物质量等于森林不同层次的截留量减去林地蒸散发量,在功能上表现为净化水质和调节径流。大多数研究仅对单一林分的个别层次蓄水功能进行研究,缺乏流域尺度或者更大空间尺度的森林水源涵养功能研究。建议从区域降尺度到流域或将坡面尺度上推到流域,集中在流域尺度解决森林水源涵养空间异质性将是解决森林水源涵养功能尺度外推的有效办法。就目前国内流行的森林水源涵养功能计量方法而言,其与尺度及研究目的有较大相关性,在研究中应根据研究目的、研究尺度和可获取的数据情况选择合适的计量方法。研究突出了不同尺度作用下森林水文过程的复杂性及重要性,并结合森林与水关系的争论问题,分析目前国内对森林水源涵养功能研究的一些误区,提出森林水源涵养功能研究的一些关键科学问题及未来可能的发展方向,主要包括:1)明确界定森林水源涵养功能的边界,探索森林水源涵养功能计量的新方法;2)加强不同时空尺度关联的森林水源涵养功能研究,包括正确评价森林水源涵养功能的时空变异规律,森林生态系统水源涵养功能的尺度效应,森林水源涵养与下游水生态安全,森林水源涵养研究范式转变等核心问题。     

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.     

土壤水分遥感监测研究进展

土壤水分是陆地表面参数化的一个关键变量。土壤水分含量随时空的转换而变化,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。总结了近年来国内外遥感监测土壤水分理论、方法的发展和应用,介绍了目前几种比较成熟和广泛应用的土壤水分遥感监测方法与模型,对比分析了各种监测方法的优缺点,指出了土壤水分遥感监测方法存在的不足,指明了今后发展的方向,展望了土壤水分遥感监测方法的发展趋势。