Landsat 9数据的地表温度反演算法优化

地表温度作为地表与大气之间能量交换的关键参数被广泛地用于众多领域。本文针对Landsat 9数据,优化了单窗算法模型和劈窗算法模型,实现了地表温度反演,并结合SURFRAD站点实测数据和地表温度产品进行了精度验证分析。结果表明：两种算法模型的确定系数均大于0.96,其中劈窗算法模型精度较高,误差(RMSE)值为1.45 K左右,单窗算法模型精度较低,误差(RMSE)值为1.61 K左右;劈窗算法模型相较于单窗算法模型对参数的敏感性较低,在高水汽含量范围内,劈窗算法模型的结果要优于单窗算法模型的结果;本文提出的地表温度反演方法结果与官方地表温度产品的误差(RMSE)值均在2.5 K以内,可满足热红外遥感数据生产地表温度产品的应用需求。     

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。     

单窗算法的大气参数估计方法

根据地表热辐射传导方程,提出了一个简单可行且精度较高的专门用于从TM6数据中演算地表温度的方法——单窗算法.这一算法把大气和地表状态对地表热传导的影响直接包括在演算公式中.该方法需要两个大气参数进行地表温度的演算,即大气平均作用温度和大气透射率.本文论述这两个大气参数的估计方法:根据大气水分含量或地表附近空气湿度来估计大气透射率;通过分析标准大气剖面资料,尤其是大气水分和气温随高程的变化规律,根据地表温度推算大气平均作用温度.     

地表温度反演的算法综述

地表温度是研究地球表面的重要参数,被称为地表的皮肤温度。反演地表温度对自然灾害监测、城市热岛等有着重要意义,利用热红外遥感技术是反演地表温度的主要方式。本文阐述了目前主要用于反演地表温度的各算法特性、优缺点和适用情况,对各种算法进行对比分析,并指出反演地表温度存在的问题及解决问题的方向,并对反演地表温度发展趋势进行了展望。     

用MODIS数据和分裂窗算法反演内蒙古地区的地表温度

地表温度是气象、水文、生态等研究中一个重要参数。大气透过率和比辐射率是分裂窗算法的两个重要输入参数,本文利用MODIS数据的可见光波段(band1)和近红外波段(band2、19)计算该两个参数;再利用MODIS数据的两个热红外波段(band31、32)和分裂窗算法对内蒙古地区地表温度进行了反演;结果表明,遥感反演出来的地表温度的空间分布与内蒙古气候区地空间分布具有高度的一致性,能直观地反应内蒙古地区地表温度的空间分布特征。     

Landsat 8劈窗算法精度评价及适用性分析

针对Landsat 8数据的两种地表温度反演算法:Jiménez-Mu(n)oz(JM)和Offer Rozenstein(OR)劈窗算法,利用辐射传输模型模拟分析算法的绝对精度以及平均绝对精度随参数的变化关系.基于误差传递理论,分析对比两种算法的总精度,在此基础上评价两种算法的适用条件.结果表明:(1)JM算法高估地...     

用NOAA-AVHRR热通道数据演算地表温度的劈窗算法

气象卫星NOAA-AVHRR有两个热通道用来监测地球表面温度。劈窗算法是用这两个热通道数据演算地表温度的最常用方法。近10年来,国际遥感界已经提出了10多种劈窗算法。本文主要介绍这些劈窗算法,并比较它们的演算精度,重点放在这些算法的具体计算方面,以便有关同行在需要计算地表温度时有选择地应用.     

R3视域算法与参考面算法的对比研究

在地形可视性分析中,R3视域算法和参考面算法是两种重要的算法。在对两种算法的计算结果进行对比后,可以发现参考面算法的计算结果总要比R3算法的结果偏高。本文在此对比的基础上继续深入分析造成这种结果差异的本质原因,进一步解释了这种差异多表现在山体和沟壑等地形起伏较大的区域原因,并得出结论:这种结果差异不能作为两种算法精度相互评价的依据。     

单通道算法地表温度反演的若干问题讨论——以Landsat系列数据为例

目的 在地表温度反演中,Jiménez Muoz和Sobrino开发的单通道(SC)算法因其需要的实时大气参数少而被广泛应用。由于SC算法在2003年提出时只提供了针对LandsatTM的大气参数,导致许多后续基于ETM+数据的地表温度计算也都采用TM的大气参数。即使SC算法于2009年提出了改进版,但这一混用现象仍未改观。因此,基于实测地表温度,以Landsat5/7/8号卫星的热红外数据为例比较了2003和2009版的算法,探讨了Planck函数、λ取值和大气参数等常见问题。结果表明,2009版的算法明显提高了ETM+地表温度反演的精度,且以采用Planck函数和USGS提供的λ值时所获得的平均精度最高。在当前,如果要用SC算法来反演Landsat8的地表温度,可在大气水汽含量较低时选用TIRS10波段来单独进行。     

MODIS数据地表温度反演分裂窗算法的IDL实现

地表温度是气象、水文、生态等研究领域中的一个重要参数。本文针对MOD IS数据的分裂窗算法进行了简要的介绍,并对参数的获取进行了分析。该算法已经被推荐并已经应用于中国地表温度产品生产。为了进行义务化流程生产地表温度产品,我们在IDL6.0环境下,编程实现了该算法。该程序运算速度快,操作简便,不需人为干预就可快速反演地表温度,非常适合批量计算MOIDS地表温度。     

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。     

Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响

Landsat系列卫星上的TIRS热红外传感器数据已被大量应用,针对TIRS数据的地表温度反演也相继开发出一些算法,并有一些研究对TIRS数据的定标及其地表温度反演算法的精度进行了对比。本文主要就TIRS热红外传感器定标参数的变化,结合这些定标参数变化的时间点对有关地表温度反演算法的适用性和有效性进行分析,特别是对劈窗算法是否适合当前的TIRS数据进行了讨论,以使用户能够对Landsat 8 TIRS热红外数据的正确使用有进一步的认识。总的看来,由于视域外杂散光的影响,TIRS数据的定标精度仍达不到设计目标,TIRS第11波段的不确定性仍成倍大于TIRS 10波段。因此,在Landsat团队未彻底解决这一问题之前,同时用TIRS第10、第11这两个差距较大的波段构成的劈窗算法来反演地表温度,其精度存在较大的不确定性,US6-S团队仍在致力于改进第11波段的精度,改进后的波段可以用劈窗策法。目前应以TIRS第10单波段的方式来反演地表温度为宜。     

利用单窗算法反演Landsat 8 TIRS数据地表温度

以Landsat 8为数据源,并结合地表发射率、大气透过率等参数遥感估算方法,提出了针对TIRS 10数据的单窗算法TIRS10_SC,并开展了研究区的地表温度反演3种单窗算法的对比研究。结果表明,TIRS10_SC算法紧密结合Landsat8 TIRS传感器的特性,通过遥感估算城区下垫面的地表发射率、大气透过率等特征,可以较为准确地估算出地表不同覆被类型的温度;裸土与水泥下垫面等相对均质的下垫面的温度反演效果稍好,TIRS10_SC算法和Q_SC算法其平均误差为0.60℃,JM_SC算法其平均误差为1.01℃;对于植被下垫面,TIRS10_SC算法和Q_SC算法其平均误差为1.48℃,JM_SC算法其平均误差为1.26℃,为了提升城区植被下垫面温度反演精度,应该进一步准确地量化其发射率特性。     

北京城市地表温度的遥感时空分析

运用Landsat TM/ETM+和Terra ASTER数据,对北京市1990~2007年夏季的地表温度进行了反演,并对地表温度的空间分布、时间变化作出了分析。对Landsat TM/ETM+数据的温度反演采用了普适性单波段算法,ASTER数据的温度反演采用了劈窗算法。通过对地表温度数据的直方图均衡处理以及综合对比分析,总结出北京地区历年来夏季地表温度的空间分布格局及该格局随北京城市发展的变化规律,分析了研究成果的不足,提出了下一步要努力的方向。     

Downscaling of Land Surface Temperature Maps in the Texas High Plains with the TsHARP Method

High-resolution evapotranspiration (ET) maps can assist demand-based irrigation management. Development of high-resolution daily ET maps requires high-resolution land surface temperature (LST) images. Earth-observing satellite sensors such as the Landsat 5 Thematic Mapper (TM) and MODerate resolution Imaging Spectroradiometer (MODIS) provide thermal images that are coarser than simultaneously acquired visible and near-infrared images. In this study, we evaluated the TsHARP downscaling technique for its capability to downscale coarser LST images using finer resolution normalized difference vegetation index (NDVI) data. The TsHARP technique was implemented to downscale seven coarser scale (240, 360, 480, 600, 720, 840, and 960 m) synthetic images to a 120 m LST image. The TsHARP was also evaluated for downscaling a coarser 960 m LST image to 240 m to mimic MODIS datasets. Comparison between observed 120 m LST images and 120 m LST images downscaled from coarser 240, 360, 480, 600, 720, 840, and 960 m images yielded root mean square errors of 1.0, 1.3, 1.5, 1.6, 1.7, 1.8, and 1.9°C, respectively. This indicates that the TsHARP method can be used for downscaling coarser (960 m) MODIS-based LST images using finer Landsat (120 m) or MODIS (240 m)-derived NDVI images. However, the TsSHARP method should be evaluated further with real datasets before using it for an operational ET remote sensing program for irrigation scheduling purposes.     

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。     

Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.     

Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.     

Enhancing group resolution of TM6 based on multi-variate regression model and semi-variogram function

It is well known that Landsat TM images are the most widely used remote sensing data in various fields. Usually, it has 7 different electromagnetic spectrum bands, among which the sixth one has much lower ground resolution compared with the other six bands. Nevertheless, it is useful in the study of rock spectrum reflection, geo-thermal resources exploration, etc. To improve the ground resolution of TM6 to the level as that of the other six bands is a problem. This paper presents an algorithm based on the combination of multi-variate regression model with semi-variogram function which can improve the ground resolution of TM6 by “fusing” the data of other six bands. It includes the following main steps: (1) testing the correlation between TM6 and one of TM1-5, 7. If the correlation coefficient between TM6 and another one is greater than a give threshold value, then select the band to the regression analysis as an argument. (2) calculating the size of the template window within which some parameters needed by the regression model will be calculated; (3) replacing the original pixel values of TM6 by those obtained by regression analysis; (4) using image entropy as a measurement to evaluate the quality of the fused image of TM6. The basic mechanism of the algorithm is discussed and the V C++ program for implemeting this algorithm is also presented. A simple application example is given in the last part of this paper, showing the effectiveness of the algorithm.