Impact of the spatial resolution on the energy balance components on an open-canopy olive orchard

The recent technical improvements in the sensors used to acquire images from land surfaces has made possible to assess the performance of the energy balance models using unprecedented spatial resolutions. Thus, the objective of this work is to evaluate the response of the different energy balance components obtained from METRIC model as a function of the input pixel size. Very high spatial resolution airborne images (≈50 cm) on three dates over olive orchards were used to aggregate different spatial resolutions, ranging from 5 m to 1 km. This study represents the first time that METRIC model has been run with such high spatial resolution imagery in heterogeneous agricultural systems, evaluating the effects caused by its aggregation into coarser pixel sizes. Net radiation and soil heat flux showed a near insensitive behavior to spatial resolution changes, reflecting that the emissivity and albedo respond linearly to pixel aggregation. However, greater discrepancies were obtained for sensible (up to 17%) and latent (up to 23%) heat fluxes at spatial resolutions coarser than 30 × 30 m due to the aggregation of non-linear components, and to the inclusion of non-agricultural areas in such aggregation. Results obtained confirm the good performance of METRIC model when used with high spatial resolution imagery, whereas they warn of some major errors in crop evapotranspiration estimation when medium or large scales are used.     

数据驱动的蒸散发遥感反演方法及产品研究进展

蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。     

土壤蒸发和植被蒸腾遥感估算与验证

地表蒸散发是土壤—植被—大气系统中能量和水循环的重要环节,它包括土壤、水体和植被表面的蒸发,以及植被蒸腾。随着地表参数多源遥感产品的快速发展,利用不同地表参数遥感产品估算地表蒸散发以及其组分土壤蒸发和植被蒸腾成为日常监测越来越便利,监测尺度已从单站扩展到田块、区域乃至全球。目前地表蒸散发双层遥感估算模型按照建模机理的不同可分为:系列模型、平行模型、基于特征空间的模型、结合传统方法的模型以及数据同化方法。本文从模型构建物理机制、模型驱动数据以及模型输出结果验证等方面总结了上述模型的发展历史和现状,并指出在模型结构与参数化方案的优化、高分辨率模型驱动数据的发展、土壤蒸发和植被蒸腾像元尺度"地面真值"的获取等方面都仍需进一步完善。     

Separating near surface thermal inertia signals from a thermal time series by standardized principal component analysis

Principal component analysis has been applied to remote sensing data to identify spatiotemporal patterns in a time series of images. Thermal inertia is a surface property that relates well to shallow surface thermal and physical properties. Mapping thermal inertia requires quantifying surface energy balance components and soil heat flux, both of which are difficult to measure remotely. This article describes a method to map soil thermal inertia using principal component analysis applied to a time series of thermal infrared images and it also assesses how sensitive this method is to the time intervals between images. Standardized principal component analysis (SPCA) was applied to thermal infrared images captured at half-hour intervals during a complete diurnal cycle. Shallow surface thermal properties accounted for 45%, 82% and 66% of the spatiotemporal variation in surface temperature observed during the heating phase, cooling phase and over the total diurnal cycle respectively. The remaining 55%, 18% and 34% of the variation was attributed to transient effects such as shadows, surface roughness and background noise. Signals related to thermal inertia explained 18% of total variation observed in a complete diurnal cycle and 7% of variation in the cooling series. The SPCA method was found useful to separate critical information such as timing and amplitude of maximum surface temperature variation from delays related to differential heating induced by micro-topography. For the field conditions experienced in this study, decreased temporal resolution when sampling intervals were greater than an hour significantly reduced the quality of results.     

Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate

In the last two decades, a number of single-source surface energy balance (SEB) models have been proposed for mapping evapotranspiration (ET); however, there is no clear guidance on which models are preferable under different conditions. In this paper, we tested five models-Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), Surface Energy Balance System (SEBS), and operational Simplified Surface Energy Balance (SSEBop)—to identify the single-source SEB models most appropriate for use in the humid southeastern United States. ET predictions from these models were compared with measured ET at four sites (marsh, grass, and citrus surfaces) for 149 cloud-free Landsat image acquisition days between 2000 and 2010. The overall model evaluation statistics showed that SEBS generally outperformed the other models in terms of estimating daily ET from different land covers (e.g., the root mean squared error (RMSE) was 0.74 mm day⁻¹). SSEBop was consistently the worst performing model and overestimated ET at all sites (RMSE = 1.67 mm day⁻¹), while the other models typically fell in between SSEBop and SEBS. However, for short grass conditions, SEBAL, METRIC, and S-SEBI appear to work much better than SEBS. Overall, our study suggests that SEBS may be the best SEB model in humid regions, although it may require modifications to work better over short vegetation.     

Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces

Surface moisture is important to link land surface temperature (LST) to people’s thermal comfort. In urban areas, the surface roughness from buildings and urban trees impacts wind speed, and consequently surface moisture. To find the role of surface roughness in surface moisture estimation, we developed methods to estimate daily and hourly evapotranspiration (ET) and soil moisture, based on a case study of Indianapolis, Indiana, USA. In order to capture the spatial and temporal variations of LST, hourly and daily LST was produced by downscaling techniques. Given the heterogeneity in urban areas, fractions of vegetation, soil, and impervious surfaces were calculated. To describe the urban morphology, surface roughness parameters were calculated from digital elevation model (DEM), digital surface model (DSM), and Terrestrial Light Detection and Ranging (LiDAR). Two source energy balance (TSEB) model was employed to generate ET, and the temperature vegetation index (TVX) method was used to calculate soil moisture. Stable hourly soil moisture fluctuated from 15% to 20%, and daily soil moisture increased due to precipitation and decreased due to seasonal temperature change. ET over soil, vegetation, and impervious surface in the urban areas yielded different patterns in response to precipitation. The surface roughness from high-rise has bigger influence on ET in central urban areas.     

基于分形的城市地面亮温与植被研究

利用主成分像元分解分析法提取植被信息，计算了城市地面亮温、NDVI、主成分像元分解分析3幅图像的样带分形维数，分析了3幅图像及其样带分形雏数的特征，探讨了地面亮温与植被的关系。实例结果表明，当地表覆盖多为同质时，地面亮温分形雏数较低，主成分像元分解分析法比NDVI能更有效地提取植被信息，更适合用于地面亮温的研究。     

非均匀地表蒸散遥感研究综述

本文评述了目前常用的遥感估算地表蒸散方法,包括地表能量平衡模型、Penman-Monteith类模型、温度—植被指数特征空间方法、Priestley-Taylor类模型和其他方法。然而使用这些方法估算地表蒸散时会面临严重的尺度效应,而产生尺度效应的根本原因之一是地表异质性,在分析了非均匀下垫面对水热通量遥感反演造成的影响后,介绍了面积加权、校正因子补偿与温度降尺度3种尺度误差纠正方法;并从地面观测实验的角度简述了非均匀下垫面水热通量真实性检验的研究;最后探讨了将来建立更具时空代表性的非均匀下垫面地表蒸散遥感估算模型可能会面临的一些挑战。     

苏州土地利用及热岛效应的时空变化

选取苏州作为研究区,分别利用1986年和2004年2个时相的Landsat5 TM影像数据对研究区土地利用/覆盖类型进行分类,并采用热红外波段进行地表温度反演,在此基础上分析了下垫面类型与热岛效应的时空变化,深入研究了下垫面类型改变对热岛效应的影响。结果表明,近20a苏州市城区大范围扩张,土地利用变化剧烈,整体表现为水体、植被向不透水下垫面的高强度转化;城市热岛范围由老城区向四周成倍扩大,热岛强度由3.12°增至4.76°,热岛效应变化与土地利用时空变化之间具有明显的对应关系。     

An approach to estimate the evapotranspiration using NOAA/AVHRR DATA

Evapotranspiration (ET) is continued process wherein moisture from soil and vegetated surface is transferred to the atmosphere. Changes in evapotranspiration are likely to have large impacts on terrestrial vegetation. Evapotranspiration is a seasonally varying property at a given place; changes in it reflect the status of soil moisture and terrestrial vegetation. Through water balance, ET can include major shifts in vegetative patterns and or its condition leading to climate change. Therefore, in this paper, it is attempted to estimate the evapotranspiration over various land cover using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 km. For this purpose, a semi-empirical model has been proposed to estimate the ET. Regression analysis has been carried out to develop an empirical relation between individual land cover surface temperature and ET, which will be helpful to know the effect of each land cover surface temperature on ET. In which, it is observed that surface temperature over grassland is more effective on ET in comparison to other land cover in March 1999 on the Mupfure, Zimbabwe catchment area. This type of estimation will be helpful for climate modeler, climatologists, ecosystem modeler and regional planner.     

Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data

Data from the first operational Chinese geostationary satellite Fengyun-2C (FY-2C) satellite are applied in combination with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products for the assessment of regional evapotranspiration over the North China Plain. The approach is based on the improved triangle method, where the temperature–vegetation index space includes thermal inertia. Two thermal infrared channels from FY-2C are used to estimate surface temperature (Ts) based on a split window algorithm originally proposed for the MSG-SEVIRI sensor. Subsequently the high temporal resolution of FY-2C data is exploited to give the morning rise in Ts. Combined with the 16 days composite MODIS vegetation indices product (MOD13) at a spatial resolution of 5 km, evaporative fraction (EF) is estimated by interpolation in the ΔTs–NDVI triangular-shaped scatter space. Finally, regional actual evapotranspiration (ET) is derived from the evaporative fraction and available energy estimated from MODIS surface albedo products MCD43. Spatial variations of estimated surface variables (Ts, EF and ET) corresponded well to land cover patterns and farmland management practices. Estimated ET and EF also compared well to lysimeter data collected for the period June 2005–September 2007. The improved triangle method was also applied to MODIS products for comparison. Estimates based on FY-2C products proved to provide slightly better results than those based on MODIS products. The consistency of the estimated spatial variation with other spatial data supports the use of FY-2C data for ET estimation using the improved triangle method. Of particular value is the high temporal frequency of image acquisitions from FY-2C which improves the likelihood of obtaining cloud free image acquisitions as compared to polar orbiting sensors like MODIS.     

非匀一冠层热辐射方向性3维模型构建——以玉米冠层为例

建立适用于多类型植被场景的热辐射方向性模型是进行地表热辐射方向性研究的一种手段。利用真实植株几何及生理参数的统计平均值来刻画理念植株,并给定其空间分布特征,进行不同生长期植被冠层的描述。基于冠层双向孔隙率思想构建了冠层热辐射方向性3维模型,模型继承了孔隙率模型在计算冠层热辐射方向性上的简洁优势同时以几何光学的思想考虑了冠层空间异质性对冠层热辐射方向性的影响。以玉米冠层为例,进行了不同生长期玉米冠层热辐射方向亮温的模拟,通过与实地测量数据的比对表明,本文发展的模型能够较准确地模拟不同生长期玉米植被场景的方向亮温变化规律,模拟误差主要来自理念株的刻画误差、玉米叶片形状的近似以及忽略了多次散射贡献等3个方面。模型的构建方法对稀疏植被场景、浓密植被场景、多类型植被的混合场景均可适用,不同观测几何下的植被场景4组份面积比计算结果有望应用于复杂地表条件下地表返照率的研究。     

基于Landsat-8 OLI影像的植被信息提取方法研究

植被是地理环境的重要组成部分,在城市生态环境系统中扮演着非常重要的角色。本文以池州市2014年OLI遥感影像为基础,结合30 m空间分辨率的DEM数据,在ENVI 5.1和ArcGIS 10.2软件的支撑下,对该区植被信息进行提取。通过对比原始波段组合、主成分分量组合和衍生波段组合的分类精度,确定植被信息提取的最佳波段组合,并对植被提取结果进行精度验证。结果表明:考虑NDVI、绿度指数和第一主成分的衍生波段组合植被提取精度最高,与该区已知的土地利用类型中的植被覆盖度进行比较,精度达到89.16%。这说明该波段组合方案对于Landsat-8影像提取植被信息效果较好,可以为其他地区植被信息的提取提供参考。     

北京市热环境时空分异与区划

城市热环境空间区划是采用分区管理的思路来缓解城市社会经济发展与热环境之间矛盾的技术基础。本文构建城市热环境区划模型的思路为:(1)将不同时相的MODIS地表温度数据产品进行正规化、分级,分析2008年北京城市热环境时空分布特征。(2)构建城市热环境影响因素评价体系,并通过空间主成分分析计算得到热环境影响主成分因子。(3)通过自组织映射神经网络,利用热环境影响主因子,进一步对热环境进行空间区划。结果表明,北京夜间较白天城市热岛分布层次感明显,夏季白天较其他季节高温区聚合程度高。区域下垫面组成要素直接影响热环境,北京城市热环境的主成分因子依次为植被覆盖、地形地貌、城市下垫面建设规模和人为热排放,并依此将北京划为7个热环境区域,根据各个分区热环境成因机制差异分别提出热环境改善和调控措施。     

遥感反演蒸散发的日尺度扩展方法研究进展

遥感技术能够提供卫星过境时刻地表参量的瞬时值,进而通过模型构建可反演得到瞬时蒸散发。相对于瞬时蒸散发,日尺度蒸散发在实际生产生活中具有更重要的应用价值。本文系统地总结分析了遥感反演瞬时蒸散发的代表性日尺度扩展方法,包括蒸发比不变法、解耦因子不变法、辐射能量比不变法、参考蒸发比不变法、地表阻抗不变法和数据同化法,并对各方法的基本原理、估算精度、适用性等进行了对比分析。在此基础上,进一步综述了日尺度扩展方法存在的不确定性和主要问题,包括扩展方法本身误差、云覆盖、气象数据获取、夜间蒸散发估算、遥感反演同扩展误差累积及真实性检验等,并指出今后应从加强有云天及夜间蒸散发扩展机理和方法等方面的研究来提升瞬时蒸散发日尺度扩展精度。     

Water Balance Study for a Basin Integrating Remote Sensing Data and GIS

Water balance of a basin involves estimation of input precipitation, runoff, infiltration and evapotranspiration (ET). Although ET may have large variations over a big basin, it is commonly estimated using a few point measurements and this makes the estimation error prone. Satellite based remote sensing data provides few parameters for estimation of energy fluxes, at the land surface and atmosphere interaction in a distributed manner using the meteorological parameters. These parameters through surface energy balance equation have been used for the estimation of ET in this study. Various spatially distributed variables required for ET estimation; viz. NDVI, surface albedo, surface temperature etc. have been derived using remote sensing and ancillary data for Tapi basin located in western India. Beside this field data such as rainfall, air temperature, relative humidity, sunshine hours etc. have been used. For computation of runoff, Soil Conservation Services (SCS) approach has been considered. Tapi basin up to Ukai dam has been selected as the study area. Satellite data from National Oceanic and Atmospheric Administration (NOAA), Polar Orbiting Environmental Satellite, which carries the Advanced Very High Resolution Radiometer (AVHRR), have been used for preparation of various maps required for runoff and ET analysis. The results of runoff and ET have been compared with observed data for 2 years, 2002–2003 and the results have been found in good agreement with observed data.     

MODIS数据估算区域蒸散量的空间尺度误差纠正方法研究

探讨了使用中高分辨率卫星数据提供的地表分类以及植被指数信息与中低分辨率卫星数据相结合,在混合像元内部进行亚像元处理,以纠正混合像元造成的通量估算误差的方法。其意义在于利用中低分辨率卫星数据进行长期大面积蒸散监测时,只需要少量的中高分辨率数据支持,就可以在一定程度上改善监测结果,具有很好的可操作性。     

Characterization of ASTER GDEM elevation data over vegetated area compared with lidar data

Current researches based on areal or spaceborne stereo images with very high resolutions (<1 m) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is the state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 m) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that the accurate coregistration between ASTER GDEM and national elevation dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.     

Remotely sensed canopy temperature based evapotranspiration models for wheat

Field studies were conducted on differentially irrigated wheat (Cv. ‘Sonalika’), on a sandy loam soil at Indian Agricultural Research Institute, New Delhi to evaluate the surface energy balance evapotranspiration (ET) models which use remotely sensed canopy temperature as an input. Four energy balance ET models were used in this study viz. Jackson et. al. (1977) approach; Bartholic et. al. (1970) and stability corrected and uncorrected aerodynamic resistance forms of models. To test the model, ET was estimated using remotely sensed canopy temperatrue, with the remaining to ET measured by water depletion method. Aerodynamic resistance forms of ET models performed well as compared to other models. Results indicate that remotely sensed canopy temperature as an input to surface energy balance models, offers a potential method of estimating ET from a cropped surface.     

Basin scale rainfall-evapotranspiration dynamics in a tropical semiarid environment during dry and wet years

In semiarid regions the occurrence of alternating long drought and heavy rainfall periods directly impacts water availability, affecting human water supply, agriculture development and the provision of ecosystem services. Because of that, research on the water input and output fluxes at the basin scale is of paramount importance. In this sense, rainfall-evapotranspiration (ET) dynamics play a critical role in water, soil and vegetation interactions, in hydrometeorological modelling and in the energy fluxes dynamics of semiarid regions. Therefore, the objective of this study was to quantify daily ET during a wet year and a dry year in a watershed located in the Brazilian Semiarid, by using remote sensing data and formulations based on the Simplified Surface Energy Balance Index (S-SEBI) and the Simplified Surface Energy Balance (SSEB) algorithms. Land surface temperature, albedo and NDVI data from MODIS sensor and solar radiation data from weather stations located in the basin were used. Rainfall analysis indicated 2009 and 2012 as being representatives of anomalously wet and dry years respectively, which were selected for the quantification of ET. The proposed algorithm was adjusted and verified with data from a flux tower equipped with eddy covariance system. Daily remote sensing ET estimates showed good agreement with observed values (RMSE = 0.79 mm.d⁻¹) and the annual ET relative error was of 7.7% (35.4 mm.year⁻¹). Results showed that the native vegetation can delay its dormant state for five months during wet years. During the wet year, ET differences between land cover classes were less noticeable due to soil saturation and the urgency of vegetated surfaces to meet their physiological needs. In dry year, however, differences were more evident, with bare soil presenting lower ET rates and vegetation classes showing higher ET values.