城市建成区地表热通量的遥感研究——以泉州市区为例

近年来,城市热岛效应已成为一个日益突出的城市环境问题,并受到了普遍关注。本文主要利用泉州市1989年、1996年Landsat TM以及2003年Terra ASTER遥感卫星影像来反演地表温度、地表反照率和植被指数等重要参数,然后结合当地的气象数据来进一步反演泉州市区的显热和潜热通量,分析了它们这些年发生的变化。研究表明泉州市区显热通量的高值分布区与城市建成区的分布范围基本吻合,并且在这些年中的分布面积不断增加。     

地表通量对模型参数的不确定性和敏感性分析

基于2007年12月22日~2009年12月31日黑河流域阿柔冻融观测站的气象驱动数据,利用通用陆面模型(Common Land Model,CoLM)模拟的地表通量结果,研究地表通量对模型参数(叶面积指数、地表反照率和植被覆盖度)的不确定性与敏感性。结果表明,叶面积指数、地表反照率和植被覆盖度对地表感热和潜热通量不同组分的影响存在较大的差异。其中,植被层的感热和潜热通量对叶面积指数的敏感性程度较高,敏感系数均达到0.7以上;与潜热通量相比,感热通量对反照率更加敏感,土壤感热、植被感热和总感热通量对反照率的敏感系数分别达到-0.96、-0.97和-0.66,而土壤潜热和总潜热通量对地表反照率的敏感系数仅为0.1左右;植被潜热通量对植被覆盖度的敏感性程度很高,敏感系数范围为0.92~0.96,而土壤感热通量对植被覆盖度最不敏感,敏感系数只有0.18左右。     

土地利用变化下北京市热通量的时空演变

探究土地利用变化对城市热通量的影响,对城市用地规划和城市热岛缓解具有重要指导意义。利用混合像元组分排序对比和分层能量切割方法,通过Landsat系列数据反演的地表参数,结合气象再分析资料,估算了2004、2009、2014和2017年4期9月份的北京市地表瞬时热通量,依据同期的北京市土地利用图,分析了北京市热通量随土地利用变化的时空演变。结果表明：①北京市地表温度和热通量分布具有明显的空间异质性,山区和平原、平原不同土地利用类型之间差异明显;②在不同时期,土地利用类型间的地表温度和热通量的高低次序具有一致性,瞬时潜热通量,林地最高,为347.85~546.95 W/m²,其次为耕地、草地,建设用地最小,为225.23~349.03 W/m²,感热通量和地表温度则相反,建筑用地最高,分别为94.06~189.28 W/m²和25.18~32.25 ℃,耕地和草地次之,水体的最低,分别为28.15~102.55 W/m²和19.25~28.38 ℃;③土地利用类型转变引起的城市热通量变化方面,自然表面转为建设用地时,潜热通量急剧减少,感热通量增加,城区周边耕地的潜热通量受城市热辐射影响而增加,城市绿地能有效缓解城市热岛效应。     

Spatial–temporal patterns of urban anthropogenic heat discharge in Fuzhou,China, observed from sensible heat flux using Landsat TM/ETM+ data

The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment. We used a heat balance model and satellite images (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images acquired in 1989 and 2001), together with meteorological station data to assess the urban thermal environment in the city of Fuzhou, China. The objective of this study was to estimate the anthropogenic heat discharge in the form of sensible heat flux in complex urban environments. In order to increase the accuracy of the anthropogenic heat flux analysis, the sub-pixel fractional vegetation cover (FVC) was calculated by linear spectral unmixing. The results were then used to estimate latent heat flux in urban areas and to separate anthropogenic heat discharge from heat radiation due to insolation. Spatial and temporal distributions of anthropogenic heat flux were analysed as a function of land-cover type, percentage of impervious surface area, and FVC. The accuracy of heat fluxes was assessed using the ratios of sensible heat flux (H), latent heat flux (L), and ground heat flux (G) to net radiation (R _n), which were compared to the results from other studies. It is apparent that the contribution of anthropogenic heat is smaller in suburban areas and larger in high-density urban areas. However, seasonal disparities of anthropogenic heat discharge are small, and the variance of anthropogenic heat discharge is influenced by urban expansion, land-cover change, and increasing energy consumption. The results suggest that anthropogenic heat release probably plays a significant role in the UHI effect, and must be considered in urban climate change adaptation strategies. Remote sensing can play a role in mapping the spatial and temporal patterns of UHIs and can differentiate the anthropogenic heat from the solar radiative fluxes. The findings presented here have important implications for urban development planning.     

城市热岛效应地表通量空间分布研究

城市化进程的加快促使更多大中城市的产生以及城市面积的扩展,导致更加严重的城市热岛现象。为了更加深入理解城市热岛效应产生根源,以西安市城区为例采用美国陆地卫星遥感数据反演或估算地表温度、植被指数以及地表通量等变量,不仅采用传统的地表温度参数理解城市热岛现象,还着重分析城市建成区和郊区的地表通量空间分布格局及其与地表温度的关系。研究发现西安城市建成区与郊区之间热环境存在显著的差异,地表温度不仅与植被覆盖状况具有密切的关系,还与地表潜热通量和实际蒸散发变量存在显著的反相关关系。详细分析表明拥有众多工厂企业的西安市莲湖区热岛效应尤为显著,而位于市中心的新城区次之,具有较大面积郊区的灞桥区热岛效应并不明显。因此城市绿地不仅影响城市建成区的地表温度空间分布,还对地表通量以及实际蒸散发的空间格局产生重要的影响,在调节城市热岛效应方面具有重要的作用。
     

A numerical simulation of the greenhouse microclimate

A numerical one-dimensional model was designed to simulate the diurnal changes of the greenhouse environment. The model takes into consideration a soil layer, a vegetation layer, an air layer and a cover. The thermal radiative, sensible, latent and conductive heat fluxes were modeled in each layer in terms of its unknown temperature and vapor pressure. The model was applied to the coastal region of Israel during the winter and summer seasons in order to assess the heating/cooling requirements of glass and polyethylene covered greenhouses.     

Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations

Models estimating surface energy fluxes over partial canopy cover with thermal remote sensing must account for significant differences between the radiometric temperatures and turbulent exchange rates associated with the soil and canopy components of the thermal pixel scene. Recent progress in separating soil and canopy temperatures from dual angle composite radiometric temperature measurements has encouraged the development of two-source (soil and canopy) approaches to estimating surface energy fluxes given observations of component soil and canopy temperatures. A Simplified Two-Source Energy Balance (STSEB) model has been developed using a “patch” treatment of the surface flux sources, which does not allow interaction between the soil and vegetation canopy components. A simple algorithm to predict the net radiation partitioning between the soil and vegetation is introduced as part of the STSEB patch modelling scheme. The feasibility of the STSEB approach under a full range in fractional vegetation cover conditions is explored using data collected over a maize (corn) crop in Beltsville Maryland, USA during the 2004 summer growing season. Measurements of soil and canopy component temperatures as well as the effective composite temperature were collected over the course of the growing season from crop emergence to cob development. Comparison with tower flux measurements yielded root-mean-square-difference values between 15 and 50 W m^− 2 for the retrieval of the net radiation, soil, sensible and latent heat fluxes. A detailed sensitivity analysis of the STSEB approach to typical uncertainties in the required inputs was also conducted indicating greatest model sensitivity to soil and canopy temperature uncertainties with relative errors reaching ∼ 30% in latent heat flux estimates. With algorithms proposed to infer component temperatures from bi-angular satellite observations, the STSEB model has the capability of being applied operationally.     

基于Landsat 8影像和能量平衡的西安市热场格局研究!

基于2014年8月15日的Landsat 8影像,通过劈窗算法反演西安中心城区地表温度,定量测算热岛中心范围。估算多种地表能量分量,分析热环境格局与地表能量分量的关系。结果表明:(1)西安中心城区城市热岛集中分布在人口、居住、商业密集区、经济技术开发区以及植被覆盖较差的区域;(2)感热、波文比与地表温度呈正相关,人为热与温度呈不显著正相关,净辐射、潜热与地表温度呈显著负相关;(3)城市热岛的地表能量结构中感热与潜热差异是构成城市热岛差异的主要原因。     

Analysis of energy fluxes and land surface parameters in a grassland ecosystem: a remote sensing perspective

A remote sensing‐based land surface characterization and flux estimation study was conducted using Landsat data from 1997 to 2003 on two grazing land experimental sites located at the Agricultural Research Services (ARS), Mandan, North Dakota. Spatially distributed surface energy fluxes [net radiation (R _n), soil heat flux (G), sensible heat (H), latent heat (LE)] and surface parameters [emissivity (ε), albedo (α), normalized difference vegetation index (NDVI) and surface temperature (T _sur)] were estimated and mapped at a pixel level from Landsat images and weather information using the Surface Energy Balance Algorithm for Land (SEBAL) procedure as a function of grazing land management: heavily grazed (HGP) and moderately grazed pastures (MGP). Energy fluxes and land surface parameters were mapped and comparisons were made between the two sites. Over the study period, H, ε and T _sur from HGP were higher by 6.7%, 18.2% and 2.9% than in MGP, respectively. The study also showed that G, LE and NDVI were higher by 1.3%, 1.6% and 7.4% for MGP than in HGP, respectively. The results of this study are beneficial in understanding the trends of land surface parameters, energy and water fluxes as a function of land management.     

Automated calculation of surface energy fluxes with high-frequency lake buoy data

Lake Heat Flux Analyzer is a program used for calculating the surface energy fluxes in lakes according to established literature methodologies. The program was developed in MATLAB for the rapid analysis of high-frequency data from instrumented lake buoys in support of the emerging field of aquatic sensor network science. To calculate the surface energy fluxes, the program requires a number of input variables, such as air and water temperature, relative humidity, wind speed, and short-wave radiation. Available outputs for Lake Heat Flux Analyzer include the surface fluxes of momentum, sensible heat and latent heat and their corresponding transfer coefficients, incoming and outgoing long-wave radiation. Lake Heat Flux Analyzer is open source and can be used to process data from multiple lakes rapidly. It provides a means of calculating the surface fluxes using a consistent method, thereby facilitating global comparisons of high-frequency data from lake buoys.     

The influence of meteorological conditions and topographic parameters on the beech forest microclimate of Simbruini Mountains,central Italy

The relation between vegetation surface temperature and remotely sensed spectral vegetation indices has been examined by a number of authors. The observed linear decrease in surface temperature with the increase in vegetation cover density has generally been explained in terms of the increase in latent heat flux associated with greater amounts of transpirationally active vegetation. However, these investigations have initially concentrated in spatially uniform crop or pasture targets on level terrain, excluding more complex forested environments with variable Sun-sensor-surface geometry. In irregular terrains, the vegetation surface temperature may be strongly influenced by topographic parameters, such as altitude and insulation angle, so that the actual forest microclimate is often difficult to evaluate. Moreover, in the thermal regime, the emission of radiative flux within the canopy element is very tightly coupled to the environment through driving mechanisms such as meteorological conditions. In fact, the allocation of absorbed solar radiation into sensible heat flux and latent heat flux is dominated by the availability of water at the Earth's surface and thus by precipitations and air temperature conditions. In this paper, which uses remotely sensed inputs of surface temperature and vegetation fractional cover, the effects of topographic parameters and vegetation cover density on surface temperature of vegetation are investigated based on Landsat 5 satellite images obtained in the daytime of two clear summer days with different antecedent meteorological conditions. For both scenes analysed, results indicate that altitude as well as the orientation of the surface relative to the Sun were the most important factors controlling surface temperatures of beech forests of Simbruini Mountains, in central Italy.     

Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: application to Denmark

Evapotranspiration rates in Denmark were estimated using Advanced Very High Resolution Radiometer (AVHRR) satellite data and weather conditions predicted by a high-resolution weather forecast model (HIRLAM). The predictions were used both for atmospheric correction of satellite data and for remote sensing based calculation of net radiation, sensible heat fluxes and evapotranspiration rates. Climate predictions at 12 GMT were used as proxies for the atmospheric conditions at the time of the afternoon satellite passage (12.30–14.30 GMT). The air temperature at the time of the satellite passage was retrieved with a rms error of 1.9°C, and the rms error of the retrieved air humidity was 204 Pa. The evapotranspiration results were significantly influenced by the spatial distribution of weather conditions. Due to the encirclement of Denmark by sea shorelines, sea breezes extending more than 30?km inland were responsible for the intrusion of cooler air temperatures which increased the sensible heat fluxes and suppressed the evapotranspiration rates. The predictions were linearly related to eddy-covariance flux measurements representing agricultural land, beech forest and conifer forest, but the relationships were also characterized by a large degree of scattering. The results are discussed in relation to inaccuracies and future perspectives.     

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。     

黑河综合遥感联合试验涡动相关通量数据处理及产品分析黑河综合遥感联合试验涡动相关通量数据处理及产品分析

涡动相关通量数据的处理及质量控制是保证各涡动观测台站数据质量的重要步骤。“黑河综合遥感联合试验”(Watershed Allied Telemetry Experimental Research,WATER)从2007年底到现在连续观测积累了大量涡动相关通量观测资料。使用经过改进的EdiRe软件对盈科站、阿柔站和关滩站3个站点的原始涡动数据进行预处理和质量控制。通过异常值及野值点剔除、倾斜修正、超声虚温修正、时间滞后校正、频率响应修正和空气密度效应修正（WPL修正）等基本处理生成Level\|1数据产品;再通过大气状态平稳性检验、总体湍流特征检验以及湍流通量统计特征分析等初步质量控制,生成Level\|2数据产品。以盈科绿洲站2009年7月份涡动相关数据为例,着重介绍涡动相关数据处理过程中各校正方法的重要性及不同校正方法对湍流通量计算的贡献。结果表明：超声虚温修正后感热通量比修正前减少约7.7%;时间滞后校正后潜热通量和CO²通量分别增加了3.9%、2.7%;频率响应修正后感热通量、潜热通量及CO2通量分别增加了2.7%、10.5%、11.6%;WPL修正后潜热通量增加了1.7%,CO²通量减少了9.8%。最后将Campbell实时处理结果与Level\|1产品及Level\|2产品进行对比分析,并对黑河流域各涡动站做了总体数据精度评价,阿柔站数据质量最好,盈科站次之,关滩站较差。
     

On the Earth's surface energy exchange determination from ERS satellite ATSR data: Part 3. Turbulent heat flux on open sea

This is the third in a series of papers which discusses determination of the Earth's surface energy exchange from ERS satellite Along-Track Scanning Radiometer (ATSR) data. This paper focuses on estimation of turbulence heat flux exchange by using ATSR data in areas of open sea. In this paper, we present results of net longwave radiation, latent heat flux and sensible heat flux. The results of net longwave radiation are from the first paper in the series (Xue et al., 1998, International Journal of Remote Sensing, 19, 2561-2583). We use monthly average tropical-ocean-global-atmosphere (TOGA) data and ATSR sea surface temperature (SST) data to calculate two years' (1992 and 1993) heat fluxes in the TOGA area. A comparison with published results indicates good qualitative agreement. Also, we compared the results of heat flux exchange by using ATSR SST data and by using the TOGA bucket SST data. The ATSR SST dataset has been shown to be useful in helping to estimate the large space scale heat flux exchange.     

Ten years of forest cover change in the Sierra Nevada detected using Landsat satellite image analysis

The Sierra Nevada of California is a region where large wildfires have been suppressed for over a century. A detailed geographic record of recent vegetation regrowth and disturbance patterns in forests of the Sierra Nevada remains a gap that can be filled with remote-sensing data. Landsat Thematic Mapper imagery was analysed to detect 10 years of recent changes (between 2000 and 2009) in forest vegetation cover for areas burned by wildfires between years of 1995 and 1999 in the region. Results confirmed the prevalence of regrowing forest vegetation during the period 2000 and 2009 over 17% of the combined burned areas. Classification of these regrowing forest vegetation areas by the Landsat normalized burn ratio (NBR) showed that there was a marked increase in average disturbance index (ΔDI) values in the transitions from low to moderate to high burn severity classes. Within the five combined wildfire perimeters, 45% of the high burn severity area delineated by the RdNBR analysis was covered by regrowing forest detected between 2000 and 2009. In contrast, a notable fraction (12%) of the entire 5 km (unburned) buffer area outside the 1995–1999 fires perimeters showed decline in forest cover, and not nearly as many regrowing forest areas, covering only 3% of all the 1995–1999 buffer areas combined. Based on comparison of these results to ground-based survey data and high-resolution aerial images, the Landsat difference index (ΔDI) methodology can fulfil much of the need for consistent, low-cost monitoring of changes due to climate and biological factors in western forest regrowth following stand-replacing disturbances.     

Relation between vegetation canopy surface temperature and the Sun-surface geometry in a mountainous region of central Italy

Evapotranspiration is the dominant energy exchange process in dense vegetated environments with an adequate water supply. If water is available vegetation canopy temperatures do not respond immediately upon intercepting solar radiation because of the apportionment of absorbed solar radiation into sensible and latent heat. This lag in the thermal conditions of vegetation canopy following the incident solar flux can be even more complex after sunrise because the presence of dew on the foliage requires more available energy investment in evaporating water and less energy spent in warming the foliage. The aim of this Letter, which is based on remotely-sensed thermal data obtained from Landsat Thematic Mapper in the daytime of a clear summer day, is to investigate the relationship between canopy surface temperatures and the incident solar radiation for a forested montainous landscape of central Italy. Results show that, under the conditions of our experiment, a time lag of one hour considerably increases the linear relation between vegetation canopy temperature and local solar illumination angle.     

Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study

Predicted latent and sensible heat fluxes from Land Surface Models (LSMs) are important lower boundary conditions for numerical weather prediction. While assimilation of remotely sensed surface soil moisture is a proven approach for improving root zone soil moisture, and presumably latent (LE) and sensible (H) heat flux predictions from LSMs, limitations in model physics and over-parameterisation mean that physically realistic soil moisture in LSMs will not necessarily achieve optimal heat flux predictions. Moreover, the potential for improved LE and H predictions from the assimilation of LE and H observations has received little attention by the scientific community, and is tested here with synthetic twin experiments. A one-dimensional single column LSM was used in 3-month long experiments, with observations of LE, H, surface soil moisture and skin temperature (from which LE and H are typically derived) sampled from truth model run outputs generated with realistic data inputs. Typical measurement errors were prescribed and observation data sets separately assimilated into a degraded model run using an Ensemble Kalman Filter (EnKF) algorithm, over temporal scales representative of available remotely sensed data. Root Mean Squared Error (RMSE) between assimilation and truth model outputs across the experiment period were examined to evaluate LE, H, and root zone soil moisture and temperature retrieval. Compared to surface soil moisture assimilation as will be available from SMOS (every 3 days), assimilation of LE and/or H using a best case MODIS scenario (twice daily) achieved overall better predictions for LE and comparable H predictions, while achieving poorer soil moisture predictions. Twice daily skin temperature assimilation achieved comparable heat flux predictions to LE and/or H assimilation. Fortnightly (Landsat) assimilations of LE, H and skin temperature performed worse than 3-day moisture assimilation. While the different spatial resolutions of these remote sensing data have been ignored, the potential for LE and H assimilation to improve model predicted LE and H is clearly demonstrated.     

Comparison of multiple models for remote sensing of carbon exchange using MODIS data in conifer-dominated forests

Boreal forests in the northern hemisphere provide important sinks for storing carbon dioxide (CO₂). However, the size and distribution of these sinks remain uncertain. In particular, many remote-sensing models show a strong bias in the simulation of carbon fluxes for evergreen needleleaf forest. The objective of this study is to improve these predictive models for accurately quantifying temporal changes in the net ecosystem exchange (NEE) of conifer-dominated forest solely based on satellite remote sensing, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime land-surface temperature (LST), night-time LST′, enhanced vegetation index (EVI), land–surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). Considering that the component fluxes, gross primary production (GPP), and ecosystem respiration (R_e), are strongly influenced by vegetation phenology, seasonality information was extracted from time-series MODIS EVI data based on non-linear least-squares fits of asymmetric Gaussian model functions with a software package for analysing the time-series of satellite sensor data (TIMESAT). The results indicated that models directly incorporating phenological information failed to improve their performance for temperate deciduous forest. Instead, three methods to retrieve the component fluxes – GPP and R_e – including direct estimates, models incorporating the phenological information, and models developed based on the threshold value (LST 273 K), were explored respectively. All methods improved NEE estimates markedly and models developed based on the threshold value performed best, and provided a future framework for accurate remote sensing of NEE in evergreen forest.     

Derivation of turbulent surface fluxes — An iterative flux-profile method allowing arbitrary observing heights

A method for practical calculation of the turbulent surfaces fluxes of momentum, sensible heat latent heat between the atmosphere and the ocean/land surface is presented. The effect of stratification is taken into account using the Monin-Obukhov similarity theory and relevant universal functions. The iterative calculation method allows the observation heights of wind speed, temperature and humidity to be arbitrary and different from each other. Various forms of observations of moisture can be used. In the standard form, the algorithms yield reasonable estimates for fluxes above a water and ice/snow surface. For estimates above land on-site information should be available, because of a great variety of roughness conditions and a possible displacement height involved. After proper adjustments, the algorithms might be installed e.g. into the software of an automatic station.