SEBAL模型及其在区域蒸散研究中的应用

蒸散是地表能量平衡与水量平衡的重要参数,遥感技术的发展促进了区域蒸散的研究。基于地表能量平衡方程,SEBAL模型利用遥感影像的可见光、近红外与热红外波段及少量气象数据可计算出区域的日蒸散量,是一个物理概念较为清楚的模型。采用Landsat7 ETM+数据利用SEBAL模型对河北省栾城县进行了遥感蒸散研究,计算获得相关地面特征参数与日蒸散量,模拟结果较为合理。     

基于SEBAL模型的区域蒸发蒸腾遥感估算

蒸发蒸腾是区域水资源循环中重要组成部分,对于区域水文学研究具有重要的意义.传统的蒸发蒸腾计算模型多以"点"上的观测为基础,对潜在蒸发量进行评价,难以在大面积区域上推广应用,在一定程度上限制了其在实际中的应用.遥感技术的发展,以及其能够方便快捷地获取地面信息的特性,为区域蒸发蒸腾量的遥感反演提供了可能.陆面能量平衡方法(SEBAL)模型是一种基于遥感影像的区域地表通量的估算模型,能够对区域蒸发蒸腾进行精确估算.本文在SEBAL模型的基础上,以河北平原为例,采取中分辨率成像光谱辐射仪(MODIS)影像产品,根据研究区下垫面的实际情况进行了参数估算,进行了区域实际蒸发蒸腾量计算及模型精确度评价,在此基础上,结合研究区的地面覆盖,对河北平原区域蒸发蒸腾分布进行了分析.     

三维辐射传输模型LESS原理及其应用

三维辐射传输模型能够准确地刻画太阳辐射与异质地表之间的相互作用,近年来已成为定量遥感建模与反演研究中的重要工具。LESS模型是基于光线追踪的三维真实冠层辐射传输模型,充分利用了光线追踪的前向追踪模式模拟能量平衡问题以及后向追踪模式模拟大尺度(公里级)遥感影像,从而实现在同一模型中多种遥感数据的模拟。目前,LESS模型可以模拟多角度反射率、多/高光谱影像、鱼眼相机、复杂地形区上下行短波辐射、冠层分层FPAR等数据,可以为验证物理模型、发展参数化模型以及训练神经网络模型等提供更为可靠的模拟数据集。本文主要介绍了LESS模型的基本原理和典型的应用。LESS模型可以从www.lessrt.org网站下载。     

三维辐射传输模型LESS原理及其应用

三维辐射传输模型能够准确地刻画太阳辐射与异质地表之间的相互作用,近年来已成为定量遥感建模与反演研究中的重要工具。LESS模型是基于光线追踪的三维真实冠层辐射传输模型,充分利用了光线追踪的前向追踪模式模拟能量平衡问题以及后向追踪模式模拟大尺度(公里级)遥感影像,从而实现在同一模型中多种遥感数据的模拟。目前,LESS模型可以模拟多角度反射率、多/高光谱影像、鱼眼相机、复杂地形区上下行短波辐射、冠层分层FPAR等数据,可以为验证物理模型、发展参数化模型以及训练神经网络模型等提供更为可靠的模拟数据集。本文主要介绍了LESS模型的基本原理和典型的应用。LESS模型可以从www.lessrt.org网站下载。     

三维辐射传输模型LESS原理及其应用

三维辐射传输模型能够准确地刻画太阳辐射与异质地表之间的相互作用，近年来已成为定量遥感建模与反演研究中的重要工具。LESS模型是基于光线追踪的三维真实冠层辐射传输模型，充分利用了光线追踪的前向追踪模式模拟能量平衡问题以及后向追踪模式模拟大尺度(公里级)遥感影像，从而实现在同一模型中多种遥感数据的模拟。目前，LESS模型可以模拟多角度反射率、多/高光谱影像、鱼眼相机、复杂地形区上下行短波辐射、冠层分层FPAR等数据，可以为验证物理模型、发展参数化模型以及训练神经网络模型等提供更为可靠的模拟数据集。本文主要介绍了LESS模型的基本原理和典型的应用。LESS模型可以从www.lessrt.org网站下载。     

基于TSEB模型的黄河三角洲蒸散量估算

蒸散发作为湿地生态系统中地-气间水热交换的主要方式,很大程度上影响着湿地的水热平衡,合理准确地估算蒸散发量,对湿地生态系统的水分循环、能量平衡以及科学管理具有重要意义。黄河三角洲湿地作为世界上暖温带最广阔、最完整和最年轻的河口湿地生态系统,既是气候变化的敏感区,也是生态环境的脆弱区。针对其地理位置特殊、水资源供需矛盾尖锐等特点,利用SEBAL(Surface Energy Balance Algorithm for Land)和TSEB(Two-Source Energy Balance)模型,对黄河三角洲湿地蒸散发量进行估算:首先利用SEBAL模型计算地表的特征参数和各地表通量,然后利用TSEB模型分离土壤和植被,分别计算黄河三角洲湿地瞬时的土壤蒸发、植被蒸腾和土壤植被总蒸散发量,利用积分关系法进行时间尺度转换,得到日蒸散量。利用气象站实测蒸发值和FAO Penman\|Monteith公式计算的作物系数,对遥感估算结果进行直接和间接精度评价。结果表明反演的蒸散发结果合理,精高较高。分析蒸散的空间分布及不同地表类型的蒸散特性,对比分析芦苇沼泽和芦苇草甸的不同蒸散特点,结果表明基于两模型耦合的方法可用于黄河三角洲湿地蒸散量估算。     

基于EFAST方法的SEBS模型参数全局敏感性分析

应用高效、稳健的EFAST方法,以黑河流域盈科绿洲站为例,从3个方面对SEBS模型的参数敏感性进行了分析:分别以感热通量(H)、潜热通量(λE)、蒸发比(fr)作为SEBS模型的输出结果,分析其对12个输入参数的敏感性;利用气象数据驱动模型,分析H、λE和fr对6个地表特征参数的敏感性;分析了参数取值范围对敏感性分析结果的影响。研究结果表明:H、λE与fr都对参考高度处的气温和风速、地表温度以及植被特征参数的敏感性较高。参数间相互作用对H、λE的间接影响很小,而对fr的影响较大。当气象输入参数确定时,6个地表参数中地表温度对模型输出的直接贡献最大,其主敏感度指数接近0.6。参数采样范围不同时,模型输入参数的敏感性表现不同。     

库布齐沙漠典型沙地人工林蒸散对比分析

人工造林使库布齐沙漠的生态快速逆转,深入理解沙地人工林的蒸散特征,对改善现有人工林的经营管理和开展人工林建设具有重要意义。利用Landsat 8、MODIS产品、气象观测资料等数据,通过基于能量平衡的SEBAL模型和MODIS MOD16蒸散产品获取库布齐沙漠典型林场2014年7月14日、7月30日、8月15日、9月7日的地表蒸散量,并采用波文比系统相关数据对估算的结果进行验证。得到以下结果：与波文比观测系统的蒸散相比,SEBAL模型反演的蒸散整体偏大,日蒸散分别多1.06、1.71、1.19、2.65 mm,两者的决定系数达0.827;MODIS MOD16产品的蒸散整体偏小,日蒸散分别少0.13、0.32、0.18、0.95 mm,两者的决定系数达0.823;在沙漠人工林斑块区域且植被类型较单一的情况下,MODIS MOD16的蒸散结果要好于SEBAL模型反演的蒸散,两者在空间分布上基本保持一致;林场蒸散较大的区域主要分布在中部和南部,而北部区域蒸散相对较小。研究结果可为其他沙地斑块人工林获取蒸散提供参考。     

流域尺度ET的遥感反演

利用多光谱卫星遥感可以反演水文气象模式所需的一些基本地面参数,进而得到整个流域尺度上的蒸发蒸腾量(ET)的分布图像,对流域水资源的管理提供重要依据。首先简述了当前地面观测计算ET采用的一些较新的技术和方法,然后论述了有关遥感反演ET方法的发展,并着重介绍了近年来国际上应用较好的‘陆面能量平衡方法(SEBAL)’。     

TSEB模型在复杂下垫面下模拟结果比较研究

地表蒸散发对干旱半干旱地区水文过程模拟以及水文平衡有重要影响,复杂地表更是对地表蒸散发模拟提出了新的挑战。利用TSEB(Two-Source Energy Balance)模型,分别以Landsat、MODIS卫星数据为驱动数据,得到黑河下游绿洲地表蒸散发时空分布格局,并利用大孔径闪烁仪和涡动相关仪的观测数据对模拟结果分别在公里级和百米级尺度进行验证,研究结果表明：在大孔径闪烁仪观测的公里级尺度下,基于Landsat数据和MODIS数据驱动的TSEB估算感热通量与大孔径闪烁仪观测数据比较的均方根误差分别为48.47 W/m²、58.57 W/m²。基于涡动相关仪观测百米级尺度,Landsat数据驱动的TSEB估算感热通量与涡动相关仪观测数据的均方根误差为89.37 W/m²。因此,得出以中高分辨率卫星遥感数据作为驱动数据模型模拟效果更好,LAS观测公里级尺度数据能部分解决遥感地表蒸散发验证空间不匹配问题。     

应用Landsat数据和SEBAL模型反演区域蒸散发及其参数估算

随着遥感技术的进步,人们发展了多种通过遥感计算区域蒸散发的方法。SEBAL模型是通过遥感反演地面蒸散的典型方法,它以陆面能量平衡为基础,物理意义明确,只利用遥感影像和少量的气象数据（风速、气温）就能反演蒸散量。Landsat数据的波谱信息丰富、空间分辨率高,数据源稳定,是通过遥感技术反演蒸散发的理想数据源。如果能合理地估算SEBAL模型中的基本参数,将会获得较高精度的反演结果,能够满足在水文、生态、林业等研究或应用中的需要,对区域水资源的管理与利用具有重要意义。从SEBAL模型的基本原理出发,分析了利用SEBAL模型采用Landsat的TM/ETM+数据反演区域蒸散发的基本过程,针对TM/ETM+数据特点对模型所需要的基本参数进行估算求解,为SEBAL模型在蒸散量反演中的广泛应用提供了一定的指导。     

A comparison of experimental energy balance components data and SEBAL model results in Dourados,Brazil

The understanding of surface energy flux is important as regards weather and climate models. Therefore, analysis of remotely sensed data, estimation of surface energy balance components (fluxes), validating experiments and results are discussed in this work. Data extracted from NOAA–AVHRR (National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer) satellite images were used to estimate the fluxes based on the SEBAL (Surface Energy Balance for Land) algorithm, as suggested by Bastiaanssen [Bastiaanssen, W.G.M., 1995, Regionalization of surface flux densites and moisture indicators in composite terrain. PhD thesis, Wageningen Agricultural University, The Netherlands.] To validate the results, one wide-ranging field experiment was organized near Dourados, municipality in the Brazilian state of Mato Grosso do Sul, during the summer of 1999. The experiment involved simultaneous taking of satellite images and in-situ measurements. SEBAL results are shown and discussed. The obtained average errors are less than 4%, 6% and 7% for net surface radiation, surface heat and latent flux estimations, respectively, as compared to the in-situ measurements.     

Evapotranspiration monitoring with Meteosat Second Generation satellites: improvement opportunities from moderate spatial resolution satellites for vegetation

The Satellite Application Facility on Land Surface Analysis proposes a land evapotranspiration (ET) product, generated in near-real time. It is produced by an energy balance model forced by radiation components derived from data of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation geostationary satellites, at a spatial resolution of approximately 3 km at the equator and covering Europe, Africa, and South America. In this article, we assess the improvement opportunities from moderate spatial resolution satellites for ET monitoring at the Meteosat Second Generation satellite scale. Four variables, namely the land cover, the leaf area index (LAI), the surface albedo, and the open water fraction, derived from moderate-resolution satellites for vegetation monitoring are considered at two spatial resolutions, 1 km and 330 m, corresponding to the imagery provided by Satellite Pour l’Observation de la Terre (SPOT)-VEGETATION and future Project for On-Board Autonomy – Vegetation (PROBA-V) space-borne sensors. The variables are incorporated into the ET model, replacing or complementing input derived from the sensor aboard the geostationary satellite, and their relative effect on the model output is analysed. The investigated processes at small scales unresolved by the geostationary satellite are better taken into account in the final ET estimates, especially over heterogeneous and transition zones. Variables derived from sensors at 250–300 m are shown to have a noticeable effect on the ET estimates compared to the 1 km resolution, demonstrating the interest of PROBA-V 330 m-derived variables for the monitoring of ET at Meteosat Second Generation resolution.     

A satellite-based energy balance algorithm with reference dry and wet limits

Based on the assumption of a linear relationship between near-surface temperature difference and radiometric surface temperature such as the surface energy balance algorithm for land (SEBAL), a satellite-based energy balance algorithm with reference dry and wet limits (REDRAW) is proposed to estimate evapo-transpiration (ET) for the regional scale. REDRAW supposes that extreme hydrological conditions can be represented by the reference dry and wet limits, which consist of four reference limits: reference bare soil dry limit (RBD), reference vegetated soil dry limit (RVD), reference bare soil wet limit (RBW), and reference vegetated soil wet limit (RVW). These reference limits should be derived geographically and used to estimate actual ET under common hydrological conditions. A comparison is made between REDRAW and a commonly used model, SEBAL, at two sites: the Tongyu in China and the Cabauw in The Netherlands. The performances in both cases show that REDRAW can provide more reliable ET estimation in relatively arid and humid areas. Meanwhile, error analysis shows that estimation of sensible heat flux is sensitive to meteorological data, and further study is needed to make REDRAW more robust to environmental conditions.     

Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea

Estimating the evapotranspiration (ET) is a requirement for water resource management and agricultural productions to understand the interaction between the land surface and the atmosphere. Most remote-sensing-based ET is estimated from polar orbiting satellites having low frequencies of observation. However, observing the continuous spatio-temporal variation of ET from a geostationary satellite to determine water management usage is essential. In this study, we utilized the revised remote-sensing-based Penman–Monteith (revised RS-PM) model to estimate ET in three different timescales (instantaneous, daily, and monthly). The data from a polar orbiting satellite, the Moderate Resolution Imaging Spectroradiometer (MODIS), and a geostationary satellite, the Communication, Ocean, and Meteorological Satellite (COMS), were collected from April to December 2011 to force the revised RS-PM model. The estimated ET from COMS and MODIS was compared with measured ET obtained from two different flux tower sites having different land surface characteristics in Korea, i.e. Sulma (SMC) with mixed forest and Cheongmi (CFC) with rice paddy as dominant vegetation. Compared with flux tower measurements, the estimated ET on instantaneous and daily timescales from both satellites was highly overestimated at SMC when compared with the flux tower ET (Bias of 41.19–145.10 W m^?2 and RMSE of 69.61–188.78 W m^?2), while estimated ET results were slightly better at the CFC site (Bias of –27.28–13.24 W m^?2 and RMSE of 45.19–71.82 W m^?2, respectively). These errors in results were primarily caused due to the overestimated leaf area index that was obtained from satellite products. Nevertheless, the satellite-based ET indicated reasonable agreement with flux tower ET. Monthly average ET from both satellites showed nearly similar patterns during the entire study periods, except for the summer season. The difference between COMS and MODIS estimations during the summer season was mainly propagated due to the difference in the number of acquired satellite images. This study showed that the higher frequency of COMS than MODIS observations makes it more ideal to continuously monitor ET as a geostationary satellite with high spatio-temporal coverage of a geostationary satellite.     

Validating remotely sensed land surface fluxes in heterogeneous terrain with large aperture scintillometry

The Large Aperture Scintillometer (LAS) has emerged as one of the best tools for quantifying areal averaged fluxes over heterogeneous land surfaces. This is particularly useful as a validation of surface energy fluxes derived from satellite sources. We examine how changes in surface source area contributing to the scintillometer and eddy covariance measurements relate to satellite derived estimates of sensible heat flux. Field data were collected on the Konza Prairie in Northeastern Kansas, included data from two eddy covariance towers: one located on an upland, relatively flat homogeneous area, and the second located in a lowland area with generally higher biomass and moisture conditions. The large aperture scintillometer spanned both the upland and lowland areas and operated with a path length of approximately 1 km specifically to compare to Moderate Resolution Imaging Spectroradiometer (MODIS) derived estimates of surface fluxes. The upland station compares well with the LAS (correlation of 0.96), with the lowland station being slightly worse (correlation of 0.84). Data from the MODIS sensor was used to compute surface fluxes using the ‘triangle’ method which combines the remotely sensed data with a soil-vegetation-atmosphere-transfer scheme and a fully developed atmospheric boundary layer model. The relative contribution to the surface observations is estimated using a simple footprint model. As wind direction varies, the relative contribution of upland and lowland sources contributing to the LAS measurements varies while the MODIS pixel contribution remains relatively constant. With the footprint model, we were able to evaluate the relationship between the LAS observations and the remotely sensed estimates of the surface energy balance. The MODIS derived sensible heat flux values correspond better to the LAS measurements (percentage error: 0.04) when there was a larger footprint compared to a time with a smaller footprint (percentage error:??0.13). Results indicate that the larger the footprint, the better the agreement between satellite and surface observations.     

Evaluation of dynamic linkages between evapotranspiration and land-use/land-cover changes with Landsat TM and ETM+ data

Complexity embedded in coastal management leads to numerous questions as to how inherent spatial and temporal linkages among evapotranspiration (ET), depth to groundwater and land-use/land-cover change (LUCC) could affect the dynamics among these seemingly unrelated events. This article aims to address such unique dynamics in the nexus of physical geography and ecohydrology. To understand such dynamic linkages, a case study was carried out in a fast growing coastal region – the southern Laizhou Bay in Shandong Province, China – by identifying the coastal LUCC at the decadal scale in association with the variations of ET with the aid of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. In such a coastal landscape evolutionary assessment, findings show that the major patterns of land use and land cover (LULC) in the study area are farmland, saline-alkali land, developed land, salt land and beach land. Over a 20-year time frame, declining groundwater trends were observed, while ET increased gradually with changing LULC. By using the surface energy balance algorithm for land (SEBAL) with Landsat TM/ETM+ images and additional environmental data, the concomitant response of ET variations due to LUCC becomes lucid among three significantly correlated pairs including fractional vegetation cover (FVC), land surface temperature (LST) and soil heat flux. The dynamic linkages between ET and LULC were finally confirmed with such a pair-wise analysis.     

Effect of remote sensing spatial resolution on interpreting tower-based flux observations

Validation comparisons between satellite-based surface energy balance models and tower-based flux measurements over heterogeneous landscapes can be strongly influenced by the spatial resolution of the remote sensing inputs. In this paper, a two-source energy balance model developed to use thermal and visible /near-infrared remotely sensed data is applied to Landsat imagery collected during the 2004 Soil Moisture Experiment (SMEX04) conducted in southern Arizona. Using a two dimensional flux-footprint algorithm, modeled surface fluxes are compared to tower measurements at three locations in the SMEX04 study area: two upland sites, and one riparian site. The effect of pixel resolution on evaluating the performance of the land surface model and interpreting spatial variations of land surface fluxes over these heterogeneous areas is evaluated. Three Landsat scenes were examined, one representing the dry season and the other two representing the relatively wet monsoon season. The model was run at three resolution scales: namely the Landsat visible/near-infrared band resolution (30 m), the Landsat 5 thermal band resolution (120 m), and 960 m, which is nominally the MODIS thermal resolution at near-nadir. Comparisons between modeled and measured fluxes at the three tower sites showed good agreement at the 30 m and 120 m resolutions — pixel scales at which the source area influencing the tower measurement (∼ 100 m) is reasonably resolved. At 960 m, the agreement is relatively poor, especially for the latent heat flux, due to sub-pixel heterogeneity in land surface conditions at scales exceeding the tower footprint. Therefore in this particular landscape, thermal data at 1-km resolution are not useful in assessing the intrinsic accuracy of the land-surface model in comparison with tower fluxes. Furthermore, important spatial patterns in the landscape are lost at this resolution. Currently, there are no definite plans supporting high resolution thermal data with regular global coverage below ∼ 700 m after Landsat 5 and ASTER fail. This will be a serious problem for the application and validation of thermal-based land-surface models over heterogeneous landscapes.     

Forward and inverse modeling of land surface energy balance using surface temperature measurements

Land surface temperature measurements have been widely used to estimate surface energy balance. However, because land surface temperature and energy balance both depend on a complex suite of factors, precise estimation of surface energy exchanges using thermal remote sensing is difficult. In recent years, a variety of methods have been developed that overcome previous limitations and show substantial promise for robust estimation of surface fluxes from remote sensing. This paper reviews recent progress in this domain and describes a two-layer energy balance model designed for use with thermal remote sensing. An important aspect of the model is that it is specifically designed to account for the complex micrometeorology and thermal properties of land surfaces possessing a range of density in vegetation. Further, the physics underlying this model are complementary to the physics of land surface thermal remote sensing. Comparisons between field measurements and modeled fluxes show good agreement, which suggests that the model describes land surface energy balance processes with good realism. More importantly, these results reinforce the conclusions of other recent studies that have demonstrated the compatibility of two-layer energy balance models with remote sensing observations and, by extension, the viability of using thermal remote sensing to model surface energy balance.