Landsat卫星热红外数据地表温度遥感反演研究进展

作为驱动地表与大气之间能量交换的关键物理量,地表温度在众多领域中都发挥着重要作用,包括气候变化、环境监测、蒸散发估算以及地热异常勘探等。Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中。本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状,梳理了2001年—2020年20年间基于Landsat热红外数据的地表温度遥感反演与应用的相关文献发表及互引情况,系统地综述了基于Landsat热红外数据的地表温度反演算法,包括基于辐射传输方程的算法、单窗算法、普适性单通道算法、实用单通道算法和分裂窗算法等。在此基础上,进一步介绍了每种算法的参数化方案,包括地表比辐射率和大气参数的估算方法。最后针对Landsat热红外数据地表温度遥感反演提出了未来可能的发展趋势与研究方向。     

城市地表温度空间降尺度研究—以北京市为例

地表温度LST（Land Surface Temperature）是城市热环境研究的重要参数之一,城市下垫面极为复杂,LST空间差异性较高。高空间分辨率LST对精细化城市热环境监测和缓解具有重要意义。目前大部分城市遥感LST降尺度研究仍以二维角度为主,缺乏建筑三维结构的考虑。本研究同时考虑地表二维和三维指标,构建基于随机森林方法的降尺度模型,开展MODIS 1 km LST降尺度研究（100 m）,并探讨二维和三维建筑形态对LST影响的空间尺度效应。另外,为了弥补随机森林模型缺乏物理基础的不足,参考热辐射传输方程,将方程中传感器接收的辐亮度和与大气透过率相关的大气可降水量,加入降尺度模型构建中。为了更好利用真实观测的MODIS 1 km LST验证降尺度结果,故将MODIS LST和所有指标因子升尺度至5 km,开展5 km LST降尺度至1 km研究,进一步研究探讨大气顶层辐亮度和大气可降水量对LST降尺度的影响。研究结果表明：（1）随机森林模型中增加辐亮度和大气可降水量前后,通过将5 km LST降尺度后1 km LST与原始MODIS 1 km LST相比,RMSE和R²分别由3.1 K和0.5提高至0.38 K和0.94。（2）当随机森林模型中增加建筑形态指标后,模型的袋外分数OOB_score由0.46提高至0.49,模拟的100 m LST与ASTER LST产品比较,R²有所降低,可能的原因是ASTER 和MODIS LST的反演方法和传感器不同,造成两者在100 m尺度下的对比性差一些。但是当驱动因子中增加MOD02和MOD05后,RMSE和R²由2.4 K和0.29提高至1.2 K和0.68,进一步说明MOD02和MOD05在1 km LST降至100 m过程中,起到至关重要作用。（3）在1 km和100 m尺度下,增加建筑形态后,模型OOB_score均有提高,并且建筑形态指标的重要性有所不同,在100 m尺度下独立建筑形态的影响程度有所增加。综上,MODIS LST在城市地区降尺度研究中需要考虑大气顶层辐亮度、大气可降水量和建筑形态的影响,并且不同的建筑形态对LST的重要性存在空间尺度效应。     

光谱指数趋势面的城市地表温度降尺度转换

针对城市及周边区域建造区和自然地表交织分布的特点,探讨了利用归一化植被指数(NDVI)和归一化建造指数(NDBI)构造趋势面的地表温度(LST)降尺度方法,以北京市市区及周边较平坦区域为例实现了LST自960 m向120 m的降尺度转换。分析了LST空间分布特征及NDVI、NDBI对地物的指示性特征;以北京市四至六环为界分析NDVI、NDBI趋势面对地表温度的拟合程度及各自的适用区域;在120 m、240 m、480 m和960 m 4个尺度上评价了NDVI、NDBI和NDVI+NDBI趋势面对LST的拟合程度和趋势面转换函数的尺度效应;对NDVI、NDBI和NDVI NDBI等3种方法的降尺度结果分覆盖类型、分区域对比评价。实验结果表明结合两种光谱指数的NDVI NDBI方法降尺度转换精度有所改善,改善程度取决于地表覆盖类型组合。     

Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+

Landscape patterns in a region have different sizes, shapes and spatial arrangements, which contribute to the spatial heterogeneity of the landscape and are linked to the distinct behavior of thermal environments. There is a lack of research generating landscape metrics from discretized percent impervious surface area data (ISA), which can be used as an indicator of urban spatial structure and level of development, and quantitatively characterizing the spatial patterns of landscapes and land surface temperatures (LST). In this study, linear spectral mixture analysis (LSMA) is used to derive sub-pixel ISA. Continuous fractional cover thresholds are used to discretize percent ISA into different categories related to urban land cover patterns. Landscape metrics are calculated based on different ISA categories and used to quantify urban landscape patterns and LST configurations. The characteristics of LST and percent ISA are quantified by landscape metrics such as indices of patch density, aggregation, connectedness, shape and shape complexity. The urban thermal intensity is also analyzed based on percent ISA. The results indicate that landscape metrics are sensitive to the variation of pixel values of fractional ISA, and the integration of LST, LSMA. Landscape metrics provide a quantitative method for describing the spatial distribution and seasonal variation in urban thermal patterns in response to associated urban land cover patterns.     

Spatial analysis of high-resolution urban thermal patterns in Vojvodina,Serbia

Main objective of this study was to establish a relationship between land cover and land surface temperature (LST) in urban and rural areas. The research was conducted using Landsat, WorldView-2 (WV-2) and Digital Mapping Camera. Normalised difference vegetation index and normalised difference built-up index were used for establishing the relation between built-up area, vegetation cover and LST for spatial resolution of 30 m. Impervious surface and vegetation area generated from Digital Mapping Camera from Intergraph and WV-2 were used to establish the relation between built-up area, vegetation cover and LST for spatial resolutions of 0.1, 0.5 and 30 m. Linear regression models were used to determine the relationship between LST and indicators. Main contribution of this research is to establish the use of combining remote sensing sensors with different spectral and spatial resolution for two typical settlements in Vojvodina. Correlation coefficients between LST and LST indicators ranged from 0.602 to 0.768.     

The use of motor-glider in topoclimatic studies

This paper reports on the advantages and disadvantages of motor-glider use in studying topoclimates. Despite the widespread use of images taken from low-altitude flying platforms (planes, helicopters, UAVs), the use of a motor-glider for imagery collection has not been reported in environmental studies. In presented study, the low-altitude remote sensing techniques were used to increase the spatial resolution of thermal maps derived from Landsat ETM+ thermal bands. Thermal images from motor-glider were taken by a thermovision camera. At the local scale, land surface temperature (LST) is one of the factors influencing topoclimatic diversity hence, by analysing LST distribution one can determine topoclimatic variability. Topoclimate has been the subject of previous studies, however, they have not used thermal remote sensing in the research process but instead relied on ground measurement network. The presented research contributes to better understanding of the thermal environment of the Earth by employing an innovative data collection method suitable for relatively large areas under specific weather conditions. The data collection with motor glider offers good spatial resolution of less than 1 m and facilitates the compilation of good quality LST maps. The paper discusses the influence of spatial resolution on LST variability and demonstrates gain in information granularity resulting from sub-meter resolution of collected data.     

Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data

Beijing has experienced rapid urbanization and associated urban heat island effects and air pollution. In this study, a contribution index was proposed to explore the effect of urbanization on land surface temperature (LST) using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived data with high temporal resolution. The analysis indicated that different zones and landscapes make diurnally and seasonally different contributions to the regional thermal environment. The differences in contributions by the three main functional zones resulted from differences in their landscape compositions. The roles of landscapes in this process varied diurnally and seasonally. Urban land was the most important contributor to increases in regional LSTs. The contributions of cropland and forest varied distinctly between daytime and nighttime owing to differences in their thermal inertias. Vegetation had a notable cooling effect as the normalized vegetation difference index (NDVI) increased during summer. However, when the NDVI reached a certain value, the nighttime LST shifted markedly in other seasons. The results suggest that urban design based on vegetation partitions would be effective for regulating the thermal environment.     

利用Landsat TM影像进行地表温度像元分解

提出了一种基于Landsat TM的地表温度二次像元分解方法,将地表温度的空间分辨率从120 m提高到30 m。首先,利用地表类型的线性统计模型（E-DisTrad）获取初次分解子像元的地表温度,计算得到初次分解子像元的辐亮度;然后,利用面向对象的图像分割方法获取二次分解子像元的权重,实现对地表温度的二次分解;最后,采用升尺度再分解的验证方法进行精度分析,并选取了北京市TM影像进行实例分析。实验结果表明,二次像元分解模型不仅能有效地提高地表温度的空间分辨率,反映出不同地表类型地表温度的空间差异性,而且保证了像元分解前后能量值的一致性,非常适合于复杂地表覆盖地区的热红外波段遥感影像数据的降尺度处理。     

HJ-1B卫星热红外遥感影像农田地表温度反演

本文以我国自主研发的HJ-1B卫星影像为数据源,利用其热红外影像、基于JM&S普适性单通道算法反演2009年5月20日河北省涿州市和高碑店市的农田地表温度。最后将HJ-1B IRS影像的地表温度反演结果与同时相Landsat TM5影像的反演结果进行比较分析,分析结果表明:本文所提出的基于HJ-1B卫星热红外影像反演农田地表温度精度可靠,该方法是可行性的。     

利用时空信息的单波段地表温度遥感反演

地表温度被认为是影响生态系统的关键因子之一,它与许多地表过程有关。目前,热红外卫星遥感技术是获取有关区域和全球尺度地表温度信息的一个有效、可行的手段。针对不同卫星上搭载的热红外传感器,许多学者开展了大量的研究,其中针对单波段热红外的特点(如Landsat TM/ETM+,CBERS和HJ-1B)提出了单通道(或单窗)算法。该类算法需要准确的地表比辐射率和大气参数(如大气水分含量)。这些参数在现实中又很难轻易获得,从而在一定程度上限制了现有算法的应用。针对HJ-1B高回访频率的特点,本文提出了利用多时相影像的时空信息来直接反演地表温度的Multi-Temporal and Spatial Information-Based Single Channel(MTSC),以解决现有算法对地表比辐射率和大气参数的过度依赖性。实例分析结果显示,基于MTSC法由HJ-1B反演得到的地表温度结果与MODIS地表(陆表和海表)温度产品具有很好的空间一致性;HJ-1B的陆表温度结果总体上被高估了约1 K,而海表温度结果总体上被高估了0.5 K;同时,MTSC法得到的HJ-1B地表温度结果具有更好的细节和空间完整性。最后,通过分析和讨论指出了一些可能的完善途径,如相似像元的确定、修改优化求解中的目标函数、参数的自适应初始化等,以便提高MTSC法的反演精度和实用性。     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

陆地卫星TM6波段范围内地表比辐射率的估计

地表比辐射率是用热红外波段遥感数据反演地表温度的关键参数。目前,应用陆地卫星TM6波段数据反演地表温度共有3种算法,即大气校正法、单窗算法和单通道算法。这3种算法都需要TM6波段范围内的地表比辐射率作为地表温度反演参数。本文首先简介这3种反演算法;然后着重探讨TM6波段地表比辐射率估计方法;最后,利用这一方法对山东省陵县附近农田地区进行地表比辐射率估计和地表温度反演。结果表明,该方法能获得较合理的地表温度反演结果。     

基于地球静止气象卫星的地表参数遥感研究进展

地表参数定量遥感反演是遥感科学研究的重要环节.21世纪以来,地球静止气象卫星数据在地表参数遥感反演中受到越来越多的重视.本文对利用地球静止气象卫星进行地表参数遥感反演研究的进展进行了综述.文章首先简单介绍了当前正在运行的欧盟Meteosat、美国GOES-R、日本葵花和中国风云静止卫星系统,随后详细总结了不同卫星系统估...     

An analysis of urban expansion and its associated thermal characteristics using Landsat imagery

There has been an increasing interest in mapping and monitoring urban land use/land cover using remote sensing techniques. However, there still exist quite a number of challenges in deriving urban extent and its expansion density from remote sensing data quantitatively. This study utilized Landsat TM/ETM+ remote sensing data to assess urban expansion and its thermal characteristics with a case study in the city of Changsha, China. We proposed a new approach for quantitatively determining built-up area, its expansion density and their respective relationship with land surface temperature (LST) patterns. An urban expansion metric was also developed using a moving window mechanism to identify urban built-up area and its expansion density based on selected threshold values. The study suggested that urban extent and its expansion density, as well as surface thermal characteristics and patterns could be identified through quantitatively derived remotely sensed indices and LST, which offer meaningful characteristics in quantifying urban expansion density and urban thermal pattern. Results from the case study demonstrated that: (1) the built-up area and urban expansion density have significantly increased in the city of Changsha from 1990 to 2001; and (2) the differences of urban expansion densities correspond to thermal effects, where a high percentage of imperviousness is usually associated with the area covered by high surface temperature.     

城市地表温度影像时空融合方法研究

地表温度（land surface temperature,LST）是反映地表能量和水平衡物理过程的一个重要参数,受限于载荷量的限制以及传感器的技术瓶颈,当前的卫星平台均难以获取同时具有较高空间和时间分辨率的遥感地表温度影像,客观上影响了遥感地表温度影像的应用。针对地表异质性较高的城市区域,选取覆盖武汉城区的中分辨率成像光谱仪（Moderate-Resolution Imaging Spectroradiometer,MODIS）和增强型专题绘图仪（Enhanced Thematic Mapper Plus,ETM+）数据,结合时空反射率融合模型（enhanced spatial and temporal adaptive reflectance fusion model,ESTARFM）和非线性辐射温度分解算法（non-linear disaggregation procedure for radiometric surface temperature,NL-DisTrad）对地表温度影像进行时空融合研究,最终生成60 m空间分辨率的逐日地表温度融合影像。将融合影像与2002-07-09和2002-10-13的ETM+实际地表温度影像进行融合精度验证分析,其决定系数R2分别为0.80和0.86,均方根误差（root mean square error,RMSE）分别为2.65 K和1.78 K。实验结果表明,所提出的地表温度时空融合模型在城市区域的地表温度时空融合应用中具有潜在的应用前景。     

热红外遥感地表温度与发射率地面验证进展

地表温度与发射率是地表—大气系统长波辐射和潜热通量交换的直接驱动力,是描述区域和全球尺度上地表能量平衡与水平衡的重要参数,其时空变化信息在气象预测、气候变化、水循环、地质勘探、农林监测和城市热环境等诸多领域具有广泛的应用。热红外遥感作为当前获取区域或全球尺度上地表温度和发射率的最有效手段之一,相较于传统的地面点位测量方法,具有空间覆盖范围大和重复观测等优势。对热红外遥感定量反演的地表温度与发射率产品进行地表真实性验证,有利于发现遥感数据自身或其反演算法的缺陷,确定产品的精度与不确定度,便于遥感产品的应用与推广。本文首先回顾了地表温度和发射率的定义,阐述了热红外遥感可反演、地面可测量的地表温度和发射率的科学内涵,并对利用热红外遥感数据反演地表温度和发射率的理论和方法作了概述;对地表温度和发射率地面验证的框架体系、验证指标进行总结,建立了基于精度、精确度、不确定度、完整性和稳定性的验证评价指标体系;总结了地表温度和地表发射率的地面验证方法、地面测量方法、辅助数据的获取方法、地表温度地面测量的采样方法,以及在验证异质非同温地表时从点到像元尺度的地表温度尺度转换方法等,分析了地面验证过程的主要误差来源;归纳了目前地表温度和地表发射率主要验证站点、观测网络及其空间分布特征;最后,本文讨论了地表温度与发射率地面验证存在的若干问题,并对地表温度与发射率验证工作的发展前景和趋势进行了相关展望。     

Bi-temporal characterization of land surface temperature in relation to impervious surface area,NDVI and NDBI,using a sub-pixel image analysis

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.     

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.     

Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes

Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLST) derived from the Landsat ETM + thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use land cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m, 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The results indicate that wavelengths centered around 457, 671, 1488 and 2013–2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity.