针对TM6有效波长的计算方法及其对地温反演的影响研究

相对NOAA-AVHRR及MODIS的热波段而言,Landsat TM6有更高的空间分辨率;因而更适合于地面热力场分布特征及微环境研究的准确分析需要。有效波长的确定对反演地表温度有重要影响。本文根据Landsat5及Landsat7 TM6对波长的光谱响应函数(RSR),推导出相应的有效波长值并分析其对地温反演的影响。分析结果表明,一般情况下,就Landsat5而言,用中心波长反演地表温度不会有太大影响;但利用Landsat7反演地表温度时则必须使用有效波长,因为中心波长可能会导致较大的地表温度反演误差。     

基于Landsat8的地表温度反演算法研究——以滇池流域为例

2013年2月11日Landsat 8在加州范德堡空军基地发射升空,其携带的热红外传感器为反演地表温度提供了一种新的数据,但目前尚没有针对Landsat 8热红外波段反演地表温度的算法。针对Landsat 8第10波段特征,对现有反演地表温度的单窗算法进行了参数修正,得到了用Landsat 8第10波段反演地表温度的单窗算法系数。为了评价修正后算法的精度,用MODTRAN模拟地表温度为20、30和40℃时大气水汽含量分别为1.0、1.5、2.0和2.5g·cm-2传感器高度处的热辐射值,再将模拟数据用修正后的单窗算法反演地表温度,结果表明:地表温度越低、大气水汽含量越低,误差越小;模拟结果的平均误差为0.74℃。说明基于Landsat 8第10波段用修正后的单窗算法反演地表温度是可行的,该方法可为地表温度反演提供一种途径。最后以滇池流域为例,基于2013年4月20日的Landsat 8热红外数据反演了滇池流域的地表温度,并分析了滇池流域地表温度的分布特征。     

基于静止气象卫星数据的地表温度遥感估算

基于分裂窗算法和地表温度日周期变化模型,探讨了利用多时相热红外遥感数据反演地表温度的方法。首先,利用分裂窗算法及地表温度日周期变化形式,推导了多时相遥感数据反演地表温度的方法。其次,利用辐射传输模型(MODTRAN),以2006年夏季在禹城观测的3 d地表温度、气温及大气水汽数据做为输入参数、变化观测角及比辐射率,模拟了一日多个时刻与风云二号(F-2D)波谱响应函数一致的亮温数据,基于此,模拟数据库对所提算法进行了检验。最后,利用2010年9月30日FY-2D多时相热红外数据对新疆区域地表温度进行了反演,并与相应时刻的MODIS地表温度产品进行了比较。结果表明：利用模拟遥感数据反演地表温度,模拟值与估算值的相关系数达0.9,均方根误差在1.5 K以内;利用在轨FY-2D热红外数据反演得到的地表温度与MODIS温度产品趋势基本一致,两者的相关性达到了0.5,均方根误差为4.4 K。需要说明的是,此方法仅满足于晴朗无云的条件。     

环境一号B星热红外波段单通道算法温度反演

文中在考虑环境一号B星(HJ-1B)热红外波段(infrared scanner,IRS4)光谱响应函数和有效波长的基础上,通过MODTRAN4模型模拟,对Jimenez-Munoz和Sobrino(JM&S)单通道算法中的大气函数进行改进,重新计算得到了适合HJ-1B星IRS4地表温度(land surface temperature,LST)反演的3个大气函数公式,并反演了福州地区的地表温度.采用基于星上辐亮度法对反演的地表温度进行精度评价,并将反演的地表温度与JM&S算法、段四波等修正的JM&S算法反演的地表温度进行对比分析.结果表明:使用文中改进后的大气参数对HJ-1B星IRS4进行地表温度反演,可取得较好结果.     

一个从MODIS数据同时反演地表温度和发射率的神经网络算法

MODIS的三个热红外波段29、31、32建立了三个辐射传输方程,这三个方程包含了5个未知数(大气平均作用温度、地表温度和三个波段的发射率)。用JPL提供的大约160种地物的波谱数据对MODIS三个波段(29/31/32)发射率之间的关系和用MODTRAN4对大气透过率和大气水汽含量之间关系进行模拟分析。分析结果表明地球物理参数之间存在着大量的潜在信息。由于潜在的信息难以严格地用数学表达式来描述,因此神经网络是非常适合被用来解这种病态反演问题。利用辐射传输模型(RM)和神经网络(NN)反演分析表明神经网络能够被用来精确地同时从MODIS数据中反演地表温度和发射率。地表温度的平均反演误差在0.4°C以下;波段29/31/32发射率平均反演误差都在0.008以下。     

MODIS数据反演地表温度的参数敏感性分析

在利用MODIS卫星遥感数据进行地表温度反演过程中，有两个基本参数需要确定，即地表比辐射率和大气透过率，尽管采用了比较合理的参数估计方法，但仍会有一些不可避免的因素导致误差的产生。为了进一步研究可能的参数误差对地表温度反演精度的影响，我们对该算法的两个参数进行敏感性分析。结果表明，当31、32两个波段的参数估计都有中等误差时，可能的地表温度误差对大气透过率和地表比辐射率都不敏感，所引起的地表温度误差大约为0．6～0．8℃，算法能够得到较高精度的地表温度反演结果。     

基于Landsat TM图像的北京城市地表温度遥感反演研究

利用北京地区Landsat TM热红外波段数据，采用单通道算法反演得到北京地区地面温度分布图。从反演结果可以看出，北京城区地面温度比郊区地表温度高，郊区地表温度较低，密云水库、官厅水库等水体的温度最低，总体上北京城市热岛效应显著。地表比辐射率是通过Van经验公式反演得到，通过对比分析，表明该方法对自然地表的比辐射率反演效果较好。     

天宫二号数据地表温度反演及其在城市群热环境监测中的应用

根据天宫二号宽波段成像仪热红外谱段的特点,提出了一种适用于天宫二号数据的劈窗算法,并以苏南城市群为研究区,进行了地表温度的反演。在此基础上,通过SUHI(Surface Urban Heat Lsland)指标分析了苏南城市群热环境空间分布特征。结果表明:劈窗算法可以有效地应用于天宫二号热红外数据,地表温度反演结果的均方根误差在1K以内;研究区地表温度结果与土地利用类型有较好的一致性,建筑用地温度最高,水体最低,苏锡常城市群形成一个整体热岛效应,SUHI指标能有效监测城市群的热岛强度及其空间分布。     

基于TES算法的地表温度反演应用研究

温度比辐射率分离算法（TES）是针对高光谱分辨率的热红外测量数据,利用地物热红外光谱的一些共性特点作为先验知识或者约束条件,从一次观测中同时反演温度和光谱发射率的方法。利用这类方法反演地表温度时主要需要较多的光谱波段和较高的信噪此。本研究以长白山火山区为例,首次将温度比辐射率分离算法应用于该区的地表温度反演,通过NEM,RAT和MMD这三种TES方法的迭代逼近,基于ASTEPR多波段热红外数据反演了长白山地区的地表温度。反演结果显示：望天鹅火山区为低地表温度集中分布区;而长白山火山区却表现为高海拔低温背景下的高地表温度集中分布,经过对地表覆盖状况的实地调查发现,该区裸露的大量浮岩是引起这种地表温度异常的主要原因之一。     

改进的浓密植被法反演Landsat-8 OLI气溶胶光学厚度

针对利用传统浓密植被法难以准确确定地表反射率的不足,分析了Landsat-8 OLI影像红蓝波段的地表反射率与归一化植被指数、散射角对短波红外波段(2.1μm)地表反射率的关系。分析表明,红、蓝波段与短波红外波段(2.1μm)地表反射率之间的比例关系随归一化植被指数和散射角的改变而不同,据此提出构建用于确定红、蓝波段地表反射率的关系模型,用于实现气溶胶光学厚度的反演。选取美国中东部云覆盖较小的地区进行气溶胶反演,使用AERONET站点数据进行反演结果的验证。结果表明,使用该模型得到反演结果与AERONET站点的实测值具有很好的一致性,拟合结果较好;大约70%的数据位于误差线内,反演结果满足精度要求。     

Recovering missing pixels for Landsat ETM + SLC-off imagery using HJ-1A /1B as auxiliary data

To improve the usability of Enhanced Thematic Mapper Plus (ETM+) scan line corrector (SLC)-off data, this article proposes using HJ-1A/1B imagery as auxiliary (i.e. reference) data to recover the SLC-off ETM+ data. The least-median-of-squares (LMedS) method is newly proposed to recover missing pixels of Landsat 7 by removing the variant or abnormal digital number values. In particular, for the visible and near-infrared bands, using HJ-1A/1B for recovery has three clear advantages: the same spatial resolution, similar spectral resolution, and approximate temporal resolution. The experiments show that all of the reference-recovery methods are better than the non-reference-recovery method. The results of using of auxiliary data in reference-recovery methods, from best to worst, are Landsat 8, HJ-1A/1B, and Landsat 7. However, for recovering missing pixels, HJ-1A/1B is superior to the ETM+ auxiliary data due to the shorter time interval in Landsat 7 (a few hours). Hence, HJ-1A/1B should be considered a useful auxiliary data to recover ETM+ SLC-off imagery data.     

HJ-1A/B CCD与Landsat TM/ETM+植被指数的交互比较

利用5对同日过境的HJ-1A/B CCD和Landsat TM/ETM+影像对,研究了二者植被指数(NDVI,SAVI,EVI)之间的定量关系。选用其中的3对影像对作为实验影像,通过对均匀同质实验区对应的植被指数进行回归分析求出二者之间的转换方程,用未参与实验的2对影像对来验证所求转换方程的有效性,并对二者植被指数之间的差异进行了分析。结果表明：两种传感器对应的植被指数之间存在极显著的线性正相关关系,所求的转换方程具有较高的精度,可以利用转换方程将两种传感器的植被指数进行互为转换,有利于二者植被监测结果的互为补充,而两种传感器在光谱响应函数上的不同造成了二者植被指数间存在差异。     

An evaluation of the temporal stability of HJ-1 CCD data using a desert calibration site and Landsat 7 ETM+

Remote-sensing data from the China Huan Jing 1 charge-coupled device (HJ-1 CCD) sensors have been extensively used to capture the dynamics of the Earth’s resources and environment, and to complement Landsat observations. It is critical to obtain radiometrically continuous and consistent measurements from multiple instruments spanning various periods. This study evaluated the radiometric stability of the HJ-1 CCD sensors since launch based on the stable Dunhuang calibration site and the Landsat Enhanced Thematic Mapper Plus (ETM+). The top-of-atmosphere (TOA) reflectance trends revealed from all rigorous selected cloud-free images showed that all bands of the CCD sensors degraded with time ranging from 1% to 7% per year, with the near-infrared (NIR) bands exhibiting the greatest drift. In addition, comparisons between the simultaneous HJ-1 CCD and Landsat ETM+ data revealed that the HJ-1 CCD agreed with the ETM+ within 3% for blue, green, and red bands after adjustment for spectral differences, whereas the NIR bands exhibited larger uncertainties at the level of 4%. The results provided comprehensive analysis of the long-term radiometric performance of the HJ-1 CCD sensors, which will serve as a reference for further applications using HJ-1 CCD data, as well as for successor missions of Chinese satellites, e.g. the Gaofen (GF) series of Earth observation satellites.     

Validation of HJ-1 B charge-coupled device vegetation index products with spectral reflectance of Hyperion

To validate the HJ-1 B charge-coupled device (CCD) vegetation index (VI) products, spectral reflectance data from EO-1 Hyperion of a close date were used to simulate the band reflectance of the HJ-1 B CCD camera. Four vegetation indices (the normalized difference vegetation index (NDVI), the ratio vegetation index (RVI), the soil adjusted vegetation index (SAVI) and the enhanced vegetation index (EVI)) were computed from both simulated and actual HJ-1 B CCD band reflectance data. Comparisons between simulated and actual HJ-1 B CCD band reflectance data, as well as that between simulated and actual HJ-1 B CCD vegetation indices were implemented to validate the VI products of the HJ-1 B CCD camera. The correlation coefficients between simulated and actual HJ-1 B CCD band reflectance data were 0.836, 0.891, 0.912 and 0.923 for the blue, green, red and near infra-red bands, and the correlation coefficients between simulated and actual HJ-1 B CCD VIs were 0.943, 0.926, 0.939 and 0.933 for SAVI, RVI, NDVI and EVI. The standard deviation of differential images between actual and simulated HJ-1 B CCD VIs are 0.052, 0.527, 0.073 and 0.133. The results show that the VI products from the HJ-1 B CCD camera are consistent with the simulated VIs from Hyperion, which proves the reliability of HJ-1 B CCD VI products.     

针对HJ-1B的水表温度反演方法研究

利用HJ-1B热红外波段数据进行了水表温度反演。数据定标后,利用水体指数对HJ-1B热红外数据进行了水体识别,识别后直接对水体区域进行编程处理。在模型方面,考虑了HJ-1B热红外通道波谱响应函数的影响,利用MODTRAN4模型修正了Jimenez-Munoz和Sobrino提出的单通道算法,算法的参数"水汽含量"从MOD05水汽产品中取得。最后利用相同时间段内MODIS的海表面温度产品进行了验证,结果显示在抽取的1211个验证点中相对误差在5%以下的占78.695%,可以认为利用HJ-1B热红外数据进行水表面温度反演是可行的。     

Improved calibration of Landsat-5 TM applicable for high-latitude and dark areas

The Landsat-5 Thematic Mapper (TM) has provided data for more than 17 years, making it one of the most successful missions so far. TM sensor degradation is well documented and although efforts are made to account for this degradation when calibrating the sensor, such calibrations are often done over high reflective and bright surfaces in combination with a high solar irradiance. These conditions are not found in high-latitude and dark areas, making the calibration coefficients inappropriate to use. In this study reflectances obtained from TM bands 1–4 over a high-arctic area from 1987–1998 are compared to reflectances obtained from the Landsat-7 Enhanced Thematic Mapper Plus (ETM + ) over the same area in 1999–2000. From the reflectance comparison it was found that a correction of the calibration gain could be described by a power function using days since launch as the controlling variable. From the power function, updated and lifetime calibration coefficients for TM bands 1–4 applicable for high-latitude and dark areas were determined. Furthermore, the study showed a continuous decrease of the TM sensor response with band 1 being the most affected and band 4 the most stable. The study also showed the possibility of using ETM+ to determine updated calibration coefficients for TM by a cross-calibration even though the ETM+ and TM scenes are not coincident.     

Sensor‐independent analysis method for hyperspectral data based on the pattern decomposition method

This paper describes a modified pattern decomposition method with a supplementary pattern. The proposed approach can be regarded either as a type of spectral mixing analysis or as a kind of multivariate analysis; the later explanation is more suitable considering the presence of the additional supplementary patterns. The sensor‐independent method developed herein uses the same normalized spectral patterns for any sensor: fixed multi‐band (1260 bands) spectra serve as the universal standard spectral patterns. The resulting pattern decomposition coefficients showed sensor independence. That is, regardless of sensor, the three coefficients had nearly the same values for the same samples. The estimation errors for pattern decomposition coefficients depended on the sensor used. The estimation errors for Landsat/MSS and ALOS/AVNIR‐2 were larger than those of Landsat/TM (ETM+), Terra/MODIS and ADEOS‐II/GLI. The latter three sensors had negligibly small errors.     

Cover. ERS-1 SAR interferogram of Mount Vesuvio (Italy)

A pixel block intensity modulation (PRIM) method has been developed to add spatial detail to Landsat Thematic Mapper (TM) thermal band TM6 images in regions with sufficient topography. The method uses 30 m resolution TM reflective spectral band images (TM1-5 and 7) to modulate the relevant TM6 image on the basis of its 120m resolution thermal pixel blocks. Topographic detail in each 120m resolution pixel block of the TM6 image is thus recovered, without altering the average thermal digital number level of the block, by the spatial information recorded in the reflective spectral bands at 30m resolution. Tests confirm that the PBIM can effectively integrate topographic spatial detail from reflective spectral bands with TM6 images while retaining the fidelity of the original thermal spectral information. PBIM is also applicable, as a general method, for data fusion of multispectral and panchromatic images with different spatial resolutions. Bearing in mind that, for space-borne remote sensing, the spatial resolution of the thermal band will continue to be lower than that of VISSWIR and panchromatic bands in the multispectral sensor systems of the next generation, such as Enhanced Thematic Mapper Plus, the PBIM method will remain a useful technique for enhancing thermal imagery data for some time.