陆地资源卫星数据地表温度反演

针对目前陆地资源卫星(Landsat-8)地表温度反演过程中,地表比辐射率估计和敏感度分析中存在的不足,对这两方面进行改进,提出了一种基于Landsat-8数据的地表温度反演算法。该文主要从劈窗算法的推导、参数的估计、敏感度分析等方面进行研究。对于大气透过率的计算,首先用与其有相邻过境时间的MODIS数据反演大气水汽含量,然后通过中分辨率的大气传输模型(Moderate Resolution Atmospheric Transmission,MODTRAN)模拟大气水汽含量与透过率的关系,最后得到大气透过率。对于发射率的计算,通过分类和ASTER提供的光谱库获得。将大气辐射传输方程模拟的地表温度与此劈窗算法反演的地表温度做比较,结果表明平均精度达到0.82K。最后研究了大气水汽含量对地表温度的影响。结果显示,当大气水汽含量误差为0.1g/cm2,其对温度反演精度的影响最大不超过0.3K;当大气水汽含量的反演误差较大的时候,其对温度反演精度的影响较大。     

一个从ASTER数据中反演地表温度的劈窗算法

根据EOS/Terra多传感器的特点,提出了一个适合于ASTER数据的劈窗算法,该算法包括两个必要的参数大气透过率和比辐射率。大气透过率是通过利用MODIS的3个近红外波段反演大气水汽含量并根据大气水汽含量与热红外波段的统计关系计算得到。由于MODIS和ASTER是在同一颗星上,这种大气透过率估计方法保证了地表温度反演过程中所需大气参数的同步获取。对于比辐射率则是通过分类和JPL提高的光谱库获得。最后用大气模拟校正法对算法进行了验证,在比辐射率已知的情况下,当使用大气模型模拟得到的大气透过率时,对Planck函数优化简化后的平均精度为0.56℃;当大气透过率是从大气水汽含量计算得到时,优化平均精度为0.58℃,表明该算法可行。     

基于分组分裂窗算法的MTSAT-1R地表温度反演

以黑河流域上游和中游为研究区,针对MTSAT-1R卫星数据,运用MODTRAN 4.0及晴空状态下的TIGR大气廓线数据,发展了根据地表比辐射率、大气水汽含量、传感器观测角度分组模拟的分裂窗算法,进行地表温度反演。分析了传感器噪声、地表比辐射率和大气水汽含量3个参数对该算法的影响,并结合模拟数据、地面观测数据及MODIS地表温度产品,对反演结果进行分析评价。结果表明:当传感器垂直观测或大气水汽含量小于2.5g/cm2时,反演精度在1K以内;反演结果与地面观测数据对比差异较小,在阿柔站RMSE为3.7 K(日)/1.4 K(夜),在盈科站RMSE为2.4K(日)/2.0K(夜);与MODIS地表温度产品比较,空间分布呈现出一致性。总之,分组分裂窗算法能较好地用于MTSAT-1R卫星数据进行地表温度反演。     

基于MODIS 影像数据的劈窗算法研究及其参数确定

劈窗算法是目前由热红外遥感数据获取陆面温度的主要方法。在介绍劈窗算法的一般表现形式的基础上, 我们推导出适合于MOD IS 影像数据的劈窗算法。大气透过率和地表比辐射率是求解地表温度的两个关键参数。由于MOD IS 图像分辨率较低,MOD IS 像元主要由水面、植被和裸土3种地物类型构成, 故可依据这3 种地物的构成比例确定地表比辐射率。从遥感影像上反演大气的水汽含量, 再根据大气水汽含量与大气透过率的关系计算出大气透过率。最后将文中推导的劈窗算法用于江苏省地表温度的反演。反演出来的地表温度图显示出明显的地表温度空间差异、城市热岛效应和不同的地物类型。     

Landsat-8数据地表温度反演单通道修正算法

Jiménez-Mu1oz提出的陆地表面温度(land surface temperature,LST)反演单通道(single-channel,SC)算法因其需要的大气参数少而被广泛应用,但大气水汽含量较高时,SC算法精度会受到影响。该文针对不同的大气模式建立适应于Landsat-8数据且在大气水汽含量为0~4.5g/cm~3时也有较高精度的单通道修正(singlechannel correction,SCC)算法。在1976美国标准大气、热带大气、中纬度冬季和中纬度夏季4种大气模式条件下,SCC算法平均误差/平均绝对误差分别为0.04/0.55K、0.07/0.25K、0.02/0.51K、-0.08/0.24K。通过参数敏感性分析发现,LST反演误差与地表比辐射率、大气水汽含量呈线性相关。地表比辐射率每改变0.01,SCC算法反演得到的LST在不同大气模式下大约改变0.46K、0.49K、0.46K、0.47K。大气水汽含量每改变0.1g/cm~3,SCC算法反演得到的LST在不同大气模式下大约改变0.41K、0.10K、0.06K、0.06K、0.13K。     

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。     

重庆地表温度的遥感反演及其空间分异特征

地表温度是地表能量平衡研究的重要参数之一,为了提高重庆主城区夏季高湿热条件下地表温度的反演精度,结合MODTRAN模型与MERRA大气廓线数据,修正了大气透过率估算方程,基于2013年夏季Landsat 8TIRS第10波段数据和单窗算法,分别利用修正前后的大气透过率反演了地表温度,并将结果与0cm土壤表层温度观测数据进行了对比,最后,分析了地表温度随地形和土地覆被的空间分异特征。结果表明:(1)修正后的大气透过率较显著提高了地表温度的反演精度,平均绝对误差从4.89K减少至1.73K;(2)地表温度具有显著的地形分异特征,和海拔之间的相关系数为-0.542 6(极显著相关),垂直递减率约为1.17K/100m,随坡度增加而降低,且随不同坡向也存在较明显差异,平缓坡阳坡半阳坡半阴坡阴坡,平缓坡和阴坡之间相差约2.40K;此外,和地形遮蔽之间的相关系数为0.217 2(极显著相关),随着地形遮蔽的减弱而升高;(3)不同土地覆盖类型的地表温度之间差异显著,城镇的平均地表温度最高,湿地的最低,其他类型之间则相差较小。     

单窗算法在福建毗邻海域海表温度反演的适用性分析

基于2013~2014年春夏秋冬4个时相的HJ-1B热红外遥感影像,采用覃志豪单窗算法反演海表温度。利用实测海表温度数据作为反演结果的对比验证,结果表明:平均绝对误差为0.86℃,相关系数R~2为0.971 5。并对水汽含量和大气温度的不确定性,对反演结果产生不同程度的影响进行分析,结果表明水汽含量误差范围在-2~0g/cm~2之间,气温变化量在-2℃~2℃之间,所产生的海表温度误差在5%以内,依然可以达到较高的反演精度;冬季水汽含量的敏感性比夏季高,而大气温度在夏季的敏感性却比冬季高。因此,单窗算法在福建毗邻海域海表温度反演具有较好的适用性,对福建毗邻海域的海洋环境监测具有重要意义。     

MODIS数据地表温度反演劈窗算法比较

针对MODIS数据,分析比较了QIN和Wan-Dozier两种劈窗算法地表温度(LST)反演精度和误差分布。首先利用辐射传输模型MODTRAN4.0,结合TIGR大气廓线数据,评价两种算法绝对精度,然后基于误差传递理论分析评价二者的总精度,最后对两种算法的LST反演结果进行比较。研究表明针对所有廓线数据,两种算法绝对精度相差不大,但Wan-Dozier算法绝对精度受地表温度和水汽含量变化的影响程度要大于QIN算法;两种算法总精度相差不大,且主要误差源均为算法绝对精度和地表比辐射率精度,QIN算法反演结果对地表比辐射率的敏感性要略高于Wan-Dozier算法;两种算法得到研究区LST分布情况基本一致,均可表现空间LST分布差异,其中水体和裸土的LST反演结果差异较大,城镇和植被平均温度差异在0.5 K以内。     

基于Landsat 8 TIRS数据的大气水汽含量反演劈窗算法

Landsat 8是2013年最新发射的Landsat卫星,携带了OLI和TIRS两个传感器,其中TIRS传感器获取了两个临近的热红外通道信息。劈窗协方差—方差比算法(SWCVR)是一种最通用的基于热红外的大气水汽含量反演方法,利用两个热红外通道(其中一个在大气窗口,另一个在大气吸收谱段)的吸收差异来反演大气水汽含量,该方法已经在MODIS等中低分辨率(1km)的热红外数据上得到很好的应用。将SWCVR算法移植到较高分辨率的Landsat 8TIRS数据上,并对水汽含量反演结果进行精度验证。气象数据验证结果表明,水汽含量的反演精度可以达到0.43g/cm~2。用MODIS水汽产品(MOD05)做交叉验证,反演的水汽含量和MOD05水汽含量的均方根误差(RMSE)为0.44g/cm~2,平均绝对误差(MAE)为0.34g/cm~2。总的来说,SWCVR算法应用于Landsat 8数据的水汽含量反演也能得到一个较高的精度。     

A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data

This paper presents a practical split‐window algorithm utilized to retrieve land‐surface temperature (LST) from Moderate‐resolution Imaging Spectroradiometer (MODIS) data, which involves two essential parameters (transmittance and emissivity), and a new method to simplify Planck function has been proposed. The method for linearization of Planck function, how to obtain atmosphere transmittance from MODIS near‐infrared (NIR) bands and the method for estimating of emissivity of ground are discussed with details. Sensitivity analysis of the algorithm has been performed for the evaluation of probable LST estimation error due to the possible errors in water content and emissivity. Analysis indicates that the algorithm is not sensitive to these two parameters. Especially, the average LST error is changed between 0.19–1.1°C when the water content error in the simulation standard atmosphere changes between ?80 and 130%. We confirm the conclusion by retrieving LST from MODIS image data through changing retrieval water content error. Two methods have been used to validate the proposed algorithm. Results from validation and comparison using the standard atmospheric simulation and the comparison with the MODIS LST product demonstrate the applicability of the algorithm. Validation with standard atmospheric simulation indicates that this algorithm can achieve the average accuracy of this algorithm is about 0.32°C in LST retrieval for the case without error in both transmittance and emissivity estimations. The accuracy of this algorithm is about 0.37°C and 0.49°C respectively when the transmittance is computed from the simulation water content by exponent fit and linear fit respectively.     

Near-surface air temperature retrieval from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) data

Spatially distributed near-surface air temperature data are a very important input parameter for several land-surface models. Such data are often lacking because there are few traditional meteorological stations. It is of great significance in both theoretical research and practical applications to retrieve air temperature data from remote-sensing observations. Based on the radiative transfer theory, this article addresses the estimate of near-surface air temperature from data from the first Chinese operational geostationary meteorological satellite, FengYun-2C (FY-2C), in two thermal infrared channels (IR1, 10.3–11.3 μm and IR2, 11.5–12.5 μm) and the MODIS atmospheric profile (MOD07) product, which provide profiles of water vapour and air temperature in different atmospheric layers. The algorithm involves only two essential parameters (transmittance and emissivity). Sensitivity analysis of the algorithm has been performed for evaluation of probable near-surface air temperature estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of near-surface air temperature from FY-2C data. Results from the sensitivity analysis indicate that the average air temperature estimation error is less than 1.2 K for a possible transmittance error of 0.05 in both channels under an emissivity range 0.95–0.98. Assuming an error of 0.005 in ground emissivity for the two thermal channels, the average near-surface air temperature error is 0.6 K. Measured air temperature datasets have been used to validate the algorithm. All the validated data indicate that the estimate error is less than 3 K in more than 80% of the samples. The high accuracy for this dataset confirms the applicability of the proposed algorithm as an alternative method for accurate near-surface air temperature retrieval from FY-2C data.     

Retrieval of land surface temperature and emissivity from ASTER1B data using a dynamic learning neural network

The accuracy of a radiance transfer model neural network (RM-NN) for separating land surface temperature (LST) and emissivity from AST09 (the Advanced Spaceborne and Thermal Emission and Reflection Radiometer (ASTER) Standard Data Product, surface leaving radiance) is very high, but it is limited by the accuracy of the atmospheric correction. This article uses a neural network and radiance transfer model (MODTRAN4) to directly retrieve the LST and emissivity from ASTER1B data, which overcomes the difficulty of atmospheric correction in previous methods. The retrieval average accuracy of LST is about 1.1 K, and the average accuracy of emissivity in bands 11–14 is under 0.016 for simulated data when the input nodes are a combination of brightness temperature in bands 11–14. The average accuracy of LST is under 0.8 K when the input nodes are a combination of water vapour content and brightness temperature in bands 11–14. Finally, the comparison of retrieval results with ground measurement data indicates that the RM-NN can be used to accurately retrieve LST and emissivity from ASTER1B data.     

地表温度遥感中大气平均作用温度估算模型研究

大气平均作用温度Ta是地表温度遥感单窗算法中一个关键的参数,利用2008~2011年全国123个探空站点资料,针对大气水汽量的垂直分布特征,分析了利用近地层气温T0估算大气有效平均温度的可行性;进一步分析了T0和Ta之间的相关性,建立了适合我国地区大气平均温度估算的最佳模型Ta=44.97098+0.80512 T0,模型的决定系数R²为0.859,均方根误差为4.198 K。通过对44幅HJ\|1B/IRS热红外图像地温反演的敏感性分析,结果表明:模型估算的Ta用于地表温度反演时的误差为1.734 K;当大气透射率τ很小时,模型估算的Ta误差对地温反演很敏感,较小的估算误差会给地温反演带来很大的误差;随着大气透射率τ的增加,Ta的估算误差对地温反演的敏感性逐渐降低。
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Infrared sensing of sea surface temperature from space

Error sources in infrared remote sensing of sea surface temperature are discussed, e.g., imperfect transmittance models, uncertain or unknown atmospheric pressure-temperature-humidity vertical profiles, temperature discontinuities at the air-sea interface, temperature differences between surface and bulk water, and neglect of surface emissivity and reflectance. Some of these are analyzed using a simplified version of the transmittance function of Prabhakara et al. (1974). The rms error in conventional sea surface temperature retrievals, in which computers are used to integrate the equation of radiative transfer over many atmospheric layers, has thus far been reduced to about ±1 K (Maul, 1980). This error is for optimum conditions, and seems irreducible. Unless the accuracy can be improved it seems impractical to spend so much effort on lengthy computer retrievals. Prabhakara et al. (1974) have devised a much simpler retrieval method using three infrared bands, which yields an rms error of ±1.1 K. A very simple method yielding ±1.0 K with two infrared bands is described here.     

基于Hyperion影像的高光谱水汽含量反演的算法比较研究

基于高光谱遥感图像数据的大气参数反演和一体化辐射校正具有重要研究意义和应用价值。首先,通过6S模型辐射传输计算分析了EO-1/Hyperion遥感影像在940和1 130nm附近水汽吸收区域的光谱吸收特点。其次,采用两通道比值法和三通道比值法,比较了不同波段组合的大气含水量高光谱遥感反演精度并进行了敏感性分析,模拟实验结果表明采用三波段比值算法的相关系数和均方根误差均优于对应的两波段算法。最后,利用张掖地区2008年3景EO-1Hyperion高光谱遥感影像,反演了大气含水量,并与地基CE-318太阳分光光度计测量数据进行对比验证,结果表明:1 124nm水汽吸收通道反演精度优于940nm,两通道和三通道比值法的均方根误差分别为0.369和0.128g/cm2,三通道比值方法优于两通道比值方法,与地面观测结果一致。     

Mono-window Algorithm Applicability in Fujian Sea and Its Surrounding Areas Sea Surface Temperature Retrieval

On the basis of four quarters HJ\|1b thermal infrared remote sensing images during 2013~2014,each of the spring,summer,autumn and winter,mono\|window algorithm was adopted to retrieve Sea Surface Temperature(SST).To verify the feasibility and accuracy of this algorithm,the derived results were compared with the measured SST data,show that the average absolute error is 0.86 ℃ and the correlation coefficient R² is 0.971 5.The different levels influence on derived results caused by the uncertainty of water vapor and atmospheric temperature is analyzed,indicate that if the water vapor error ranges between -2~0 g/cm²and the temperature variation is between -2 ℃~2 ℃,the sea surface temperature error will be within 5%,the high retrieving accuracy can still be achieved;The sensitivity of the water vapor in winter shows higher than in summer,while the sensitivity of atmospheric temperature demonstrates lower than in summer.Therefore,mono\|window algorithm is good applicable in the SST retrieval in Fujian sea and its surrounding areas,which is of great significance to Fujian environmental monitoring.     

Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals

ASTER reflectance spectra from Cuprite, Nevada, and Mountain Pass, California, were compared to spectra of field samples and to ASTER-resampled AVIRIS reflectance data to determine spectral accuracy and spectroscopic mapping potential of two new ASTER SWIR reflectance datasets: RefL1b and AST_07XT. RefL1b is a new reflectance dataset produced for this study using ASTER Level 1B data, crosstalk correction, radiance correction factors, and concurrently acquired level 2 MODIS water vapor data. The AST_07XT data product, available from EDC and ERSDAC, incorporates crosstalk correction and non-concurrently acquired MODIS water vapor data for atmospheric correction. Spectral accuracy was determined using difference values which were compiled from ASTER band 5/6 and 9/8 ratios of AST_07XT or RefL1b data subtracted from similar ratios calculated for field sample and AVIRIS reflectance data. In addition, Spectral Analyst, a statistical program that utilizes a Spectral Feature Fitting algorithm, was used to quantitatively assess spectral accuracy of AST_07XT and RefL1b data.Spectral Analyst matched more minerals correctly and had higher scores for the RefL1b data than for AST_07XT data. The radiance correction factors used in the RefL1b data corrected a low band 5 reflectance anomaly observed in the AST_07XT and AST_07 data but also produced anomalously high band 5 reflectance in RefL1b spectra with strong band 5 absorption for minerals, such as alunite. Thus, the band 5 anomaly seen in the RefL1b data cannot be corrected using additional gain adjustments. In addition, the use of concurrent MODIS water vapor data in the atmospheric correction of the RefL1b data produced datasets that had lower band 9 reflectance anomalies than the AST_07XT data. Although assessment of spectral data suggests that RefL1b data are more consistent and spectrally more correct than AST_07XT data, the Spectral Analyst results indicate that spectral discrimination between some minerals, such as alunite and kaolinite, are still not possible unless additional spectral calibration using site specific spectral data are performed.     

Near‐surface air temperature estimation from ASTER data based on neural network algorithm

An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near‐surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0 K. The mean and the standard deviation of estimation error are about 2.0 K and 2.3 K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM‐NN can be used to estimate near surface air temperature from ASTER data.