Estimation of the Land Surface Emissivity in the Hinterland of Taklimakan Desert

An accurate accounting of land surface emissivity(ε) is important both for the retrieval of surface temperatures and the calculation of the longwave surface energy budgets.Since ε is one of the important parameterizations in land surface models(LSMs),accurate accounting also improves the accuracy of surface temperatures and sensible heat fluxes simulated by LSMs.In order to obtain an accurate emissivity,this paper focuses on estimating ε from data collected in the hinterland of Taklimakan Desert by two different methods.In the first method,ε was derived from the surface broadband emissivity in the 8–14 μm thermal infrared atmospheric window,which was determined from spectral radiances observed by field measurements using a portable Fourier transform infrared spectrometer,the mean ε being 0.9051.The second method compared the observed and calculated heat fluxes under nearneutral atmospheric stability and estimated ε indirectly by minimizing the root-mean-square difference between them.The result of the second method found a mean value of 0.9042,which is consistent with the result by the first method.Although the two methods recover ε from different field experiments and data,the difference of meanvalues is 0.0009.The first method is superior to the indirect method,and is also more convenient.

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

利用Landsat/TM热红外数据进行陆面温度反演的比较

对于只有一个热红外波段的遥感数据(Landsat/TM),目前有三种算法可以进行地表温度反演,分别是:大气校正法、Jimnez-Mun%oz等人的单通道算法和覃志豪等人的单窗算法。这三种算法都要在地表发射率已知的情况下进行。在没有电探空数据和发射率未知的情况下,大气校正法比较困难实施。所以本文在发射率未知的情况下只比较后两种方法,并采用MODIS LST/LSE产品对结果进行验证。通过植被覆盖度法估计地表比辐射率,估计结果与MODIS陆面发射率产品相比发现两者之间的平均误差在0.004,说明了此方法的可行性。试验中选取了2×2像元大小的MODIS LST均质检验区。对两算法反演的Landsat/TM LST结果进行了比较,并与MODIS LST建立了线性纠正方程。所建立的纠正方程表明发射率未知的情况下Jimnéez-Munoz算法优于覃志豪等人的单窗算法。

基于Landsat TM/ETM+遥感数据的福州热环境研究

以福建省福州市为研究区,使用1994年5月12日Landsat TM、2002年3月7日LandsatETM+和2009年3月10日Landsat TM数据,采用普适性单通道算法对研究区进行地表温度反演算,反演得出福州地面温度的分布情况,并根据研究结果对福州市热环境空间分布格局及动态演变过程进行监测分析.研究结果表明:福州市存在明显的城市热岛现象,不同时相遥感数据反演得到的福州市地表温度的空间分布趋势基本一致;城区地表温度明显高于郊区,水体温度最低.1994年福州市热岛中心分布范围较小;1994—2002年间福州市高温区域明显增加,主要分布在城区建筑物比较密集的区域和裸地区域;2002—2009年间高温区增加得比较缓慢,同1994年和2002年相比,2009年城区范围内大面积高温区减少且分布比较破碎.

基于Landsat 5 TM数据反演地表温度

利用Landsat 5第6波段数据,根据单窗算法反演合肥市地表温度。针对在夏季大气中水汽含量比较大的问题,利用MODTRAN中纬度夏季模式模拟出在大气水汽含量位于0.4～5.4 g/cm^2区间时,大气透过率与大气水汽含量之间的关系。利用模拟出的结果及MODIS水汽产品对TM图像进行大气校正并反演地表温度,选择均匀区的反演温度值与MODIS温度产品比较,比较结果显示植被均匀区的温差在1K以内,城镇建筑均匀区的温差在1.1 K以内,故可用此方法对TM图像进行大气校正进而反演地表温度。

基于Landsat ETM+影像的城市热岛研究

采用Landsat ETM+影像数据,分别利用MODTRAN4和FLAASH对热红外波段和可见光、近红外波段进行大气校正,采用Valor的植被指数混合模型估算地表比辐射率,以普朗克公式推算地表温度,并以长沙市为研究区,分析了不同地表覆盖类型以及NDVI与地表温度之间的关系。结果表明,不同地表覆盖类型的地表温度有着显著差异,城市地表温度与NDVI之间具有明显的负相关关系,增加城市的绿地面积有助于缓解城市热岛效应。

Landsat8热红外波段地表比辐射率估算方法改进

地表比辐射率是地表温度反演的关键输入参数,为了解决现有Landsat8热红外波段地表比辐射率估算方法较复杂的问题,基于ASTER光谱库数据和植被指数混合模型,提出一个适用于Landsat8热红外波段地表比辐射率的估算方法。采用MODIS地表比辐射率产品、地表温度产品与地面气象站点气温数据验证表明:估算得到的地表比辐射率同MODIS地表比辐射率产品平均相对误差小于0.005,相关系数0.8以上。运用估算的地表比辐射率数据反演得到的地表温度同气温数据的绝对误差小于1℃。由此得出结论,该方法可以较好的估算出Landsat8热红外波段地表比辐射率,可以支持生产高精度地表温度产品。

The study of estimation method of broadband emissivity from EOS/MODIS data

The broadband emissivity is an important parameter for estimating the energy balance of the Earth.This study focuses on estimating the window(8-12 |xm) emissivity from the MODIS(moderate-resolution imaging spectroradiometer) data,and two methods are built.The regression method obtains the broadband emissivity from MOD11B1 5KM product,whose coefficient is developed by using 128 spectra,and the standard deviation of error is about 0.0118 and the mean error is about0.0084.Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29,31 and 32 obtained from MOD11B1 5KM product,the standard deviations of errors of single emissivity in bands 29,31,32 are about 0.009 for MOD11B1_5KM product,so the total error is about 0.02 and resolution is about 5km×5km.A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emissivity from MODIS 1B data.The standard deviation of error is about 0.016,the mean error is about0.01,and the resolution is about 1km ×1km.The validation and application analysis indicates that the regression is simpler and more practical,and estimation accuracy of the dynamic learning neural network method is higher.Considering the needs for accuracy and practicalities in application,one of them can be chosen to estimate the broadband emissivity from MODIS data.

黄河三角洲Sentinel-3高精度时空连续海陆表面温度反演

海陆表面温度是理解全球变化与人类活动的关键参数。针对已有温度反演产品海陆边界时空不连续的问题,本文采用Sentinel-3海陆表面温度辐射计(SLSTR)光学与热红外影像,通过植被指数阈值法逐像元计算陆表与海表发射率,基于分裂窗算法反演得到黄河三角洲陆海表面时空连续温度产品。结合地面温度观测和欧洲中期天气预报中心提供的全球中等分辨率数值大气再分析产品,验证了本文陆海温度产品(均方根误差优于1.1K)反演精度高于现有公开的全球产品,可为全球时空连续温度产品反演提供参考。

Synchronous retrieval of land surface temperature and emissivity

This is an old topic for more than ten years to retrieve land surface temperature (LST) from satellite data, but it has not been solved yet. At first, people tried to transplant traditional split window method of sea surface temperature (SST) to the retrieval of LST, but it was found that the emissivities of land surface ( ε i) must be involved in atmospheric correction. Then many different formulas appeared with assumption of emissivities known. In fact, emissivities of land surface with pixel size cannot be known beforehand because of various reasons, so in recent years the focus of attention has been transferred to retrieving emissivities ( ε i) and LST at the same time. Therefore, we have to solve missing equations problem. For this some people try to introduce middle infrared information, but new problems will be brought in which means that it is very difficult to describe middle infrared BRDF of targets with high accuracy and the scattering of atmospheric aerosol cannot be ignored. Therefore a different way is offered to solve this problem only using two thermo infrared bands data based on three assumptions, constant emissivities in two measurements, and the same atmospheric parameters for neighbouring pixels and the difference of emissivity (Δ ε ) of two channels can be known beforehand. Results of digital simulations show that it is possible to retrieve LST with its root mean square (RMS) of errors less than 1 K and RMS of relative error of ground radiance at 7‰ if the error of atmospheric temperature at ±2℃ and the relative error of atmospheric water vapor at ±10% can be satisfied. Results have been confirmed by initial field test.

全球微波陆表发射率产品质量评估及优化

全球微波陆表发射率(MLSE)地图集是目前卫星资料同化观测算子中的重要初猜值或模拟值,但由于缺乏“真实”的观测MLSE,已有的基于多种微波传感器制作的全球MLSE产品质量可靠性未知,而且还缺少1套质量较好频率覆盖范围较宽的全球MLSE数据集。选择大气快速辐射传输模型(RTTOV)中使用的3套MLSE地图集(SSMI/S、AMSU-A/B、ATMS)、TELSEM2工具背景数据集、2套AMSR-E数据集(AMSR-E1和AMSR-E2)和1套FY-3D数据集,基于统计分析技术开展7套MLSE产品的全球时、空一致性评估,并选择6种典型土地覆盖类型开展各产品MLSE随土地覆盖类型和频率变化一致性评估,并在MLSE产品优选的基础上对TELSEM2发射率产品进行优化,新建1套频率为6.925—150.0 GHz的全球MLSE数据集(CoTELSEM2)。研究结果显示:AMSR-E2几乎不可用,AMSR-E1、TELSEM2、SSMI/S、AMSU-A/B、ATMS、FY-3D月MLSE之间具有较好的时、空一致性,平均空间相关系数为0.887—0.928,其中TELSEM2最高(0.928),FY-3D略低(0.914);平均绝对偏差为0.031—0.041,其中TELSEM2最低(0.031),FY-3D最高(0.041);传感器相同扫描方式较不同扫描方式的空间一致性更好,圆锥和跨轨扫描方式分别以TELSEM2和AMSU-A表现更好;ATMS的51.7 GHz MLSE存在系统性高估,AMSR-E1和FY-3D的23.8、89.0 GHz MLSE在高植被覆盖地区也存在系统性高估,而FY-3D MLSE则存在一些明显偏高或偏低问题。总体上,TELSEM2和AMSU-A/B的质量可靠性较高,FY-3D质量可靠性较差。新的CoTELSEM2发射率产品具有较好的时、空一致性和频率依赖一致性,且全球MLSE的季节变化不确定性存在明显的土地覆盖类型依赖特征。

A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data

On the basis of the concept of the two-channel Temperature-Independent Spectral Indices (TISI), a physically based method is developed to extract the directional emissivities in mid-infrared and thermal infrared channels from day-night space measurements. A phenomenol-ogical model with three parameters is also proposed in this paper to describe the angular variations of the reflectivity (or emissivity). Having applied the proposed method to AVHRR data on an area covering the Iberian Peninsula (rather vegetated) and on a region centered on Tunisia (arid area), one can see from the results that the terrestrial surfaces do not behave as Lambertian reflector and angular variations of bidirectional reflectivity for bare soils appear to be azimuth-independent whereas those for vegetation present a pronounced backscattering effect. As for directional emissivities, values of vegetated areas are found to be higher and remain rather constant whatever the view angle is. On the contrary, on arid areas, values are

光学载荷定标场比辐射率方向性模型构建

地表比辐射率方向性研究是国际热红外定量遥感研究领域的热点与难点,现有的热红外沙地比辐射率方向性模型存在先验参数较多、精度较低、适用性差等问题。论文基于5个伪不变定标场,即Algeria3_1km、Algeria5_1km、Libya1_1km、Mauritania1_1km及Mauritania2_1km,综合利用长时序极轨卫星载荷AQUA/MODIS与静止轨道卫星载荷MSG/SEVIRI多角度观测数据,经过载荷相互校准、大气纠正、影像数据时空匹配等处理,基于热红外辐射传输方程,获取0~65°观测天顶角范围内各研究区方向性比辐射率,建立公里级像元尺度比辐射率方向性模型,并对其进行了不确定度评估。结果表明:地表比辐射率随观测天顶角增大而降低,其方向性效应随波段中心波长的增加而减小,Algeria5_1km区域的方向性效应最小,Mauritania1_1km区域的方向性效应最强;各研究区比辐射率方向性模型不确定度均随观测天顶角的增大而增大,模型不确定度均优于3%。

基于夜间灯光数据的江西省人为热排放与夜间地表温度的相关性分析

基于自编码器处理的江西省2005—2020年的夜间灯光数据,实现了人为热排放在较长时间序列的空间化模拟,并分析了江西省人为热排放的时空变化特征,最后对空间化的人为热排放数据与夜间地表温度进行相关性分析,结果表明:人为热排放呈现显著的增加趋势,人为热排放的面积在不断扩张,16年间人为热排放重心不断向江西省东南方向移动;有26.27%的区域的夜间地表温度处于升温趋势;冬季的夜间地表温度增长最明显,升温区域的面积最大,增长趋势最高;人为热排放强度和夜间地表温度有着显著的正相关,在年内表现为热排放量高的区域夜间地表温度明显较高;年际变化上表现为,在热排放区域夜间地表温度的变化趋势和热排放的变化趋势主要为显著正相关。

Simultaneous retrieval of land surface temperature and emissivity from the FengYun-4A advanced geosynchronous radiation imager

This paper extends a new temperature and emissivity separation(TES)algorithm for retrieving land surface temperature and emissivity(LST and LSE)to the Advanced Geosynchronous Radiation Imager(AGRI)onboard Fengyun-4A,China’s newest geostationary meteorological satellite.The extended TES algorithm was named the AGRI TES algorithm.The AGRI TES algorithm employs a modified water vapor scaling(WVS)method and a recalibrated empirical function over vegetated surfaces.In situ validation and cross-validation are utilized to investigate the accuracy of the retrieved LST and LSE.LST validation using the collected field measurements showed that the mean bias and RMSE of AGRI TES LST are 0.58 and 2.93 K in the daytime and−0.30 K and 2.18 K at nighttime,respectively;the AGRI official LST is systematically underestimated.Compared with the MODIS LST and LSE products(MYD21),the average bias and RMSE of AGRI TES LST are−0.26 K and 1.65 K,respectively.The AGRI TES LSE outperforms the AGRI official LSE in terms of accuracy and spatial integrity.This study demonstrates the good performance of the AGRI TES algorithm for the retrieval of high-quality LST and LSE,and the potential of the AGRI TES algorithm in producing operational LST and LSE products.

Land surface emissivity change in China from 2001 to 2010

陆地表面放射率是在从热红外线的遥感的温度倒置的重要参数之一。用地的复数 L3 水平产品的 MOD11C3，时间空间的数据在中国陆地表面放射率设定因为从 2001 ～ 2010 的 10 年被获得。结果证明在西北沙漠区域的陆地表面放射率在中国是最低的，与小季节的变化。相反，在陆地表面放射率在有重要季节的变化东北中国和北 Xinjiang， Qinghai 西藏高原，长江山谷和东方、南部的中国。在冬季，在东北中国和北 Xinjiang 的陆地表面放射率相对高。Qinghai 西藏高原区域的陆地表面放射率从 11 月在低价值被维持到三月，当它在另外的月内变得更高时。长江山谷，东方、南部的中国，和四川盆的陆地表面放射率从 7 月变化到 10 月，并且在 8 月达到顶点。陆地表面放射率价值能被划分成五个层次：低放射率(0.61630.9638 ) ，中等低的放射率(0.96390.9709 ) ，中等放射率(0.97100.9724 ) ，中等高度的放射率(0.97250.9738 ) ，和高放射率(0.97390.9999 ) 。分别地，有低放射率，中等低的放射率和中等放射率的区域的百分比是大约 20% ， 10% 和 20% 。中等高度的放射率区域完成瓷器陆地表面区域的 40%50% 。中等高度的放射率区域的内部年度的变化也是很清楚的，与二座山峰(在春天和秋天) 并且二马槽(在夏天和冬季) 。高放射率区域的内部年度的变化是很重要的，与在冬季(10%) 的一座山峰，当时仅仅 1% 或 2% 在另外的季节。在瓷器陆地表面放射率和温度的时间空间的分发之间有一个清楚的协会：越高放射率，越多降低温度，并且反过来也如此。放射率是任何对象的一个固有的性质，但是它的放射率的精确的值非常取决于它包围环境因素。

Physics-based simultaneous retrieval of atmospheric temperature-humidity profiles and land surface temperature-emissivity by integrating Terra/Aqua MODIS measurements

Atmospheric temperature-humidity profiles and land or sea surface temperature are coupled actions in the earth system process. Based on the numerical perturbation form of the atmospheric radiative transfer equation, a physics-based algorithm is pre- sented to integrate four pairs of MODIS measurements from the Terra and Aqua satellites to retrieve simultaneously atmospheric temperature-humidity profile, land-surface temperature and emissivity. Three pairs of MODIS data at two field sites in China, Luancheng and Poyang Lake areas, have been chosen to test and validate the model. Two pairs of atmospheric tem- perature and humidity profiles, land surface temperature (LST), and land surface emissivity (LSE) have been retrieved simul- taneously for every pair of MODIS measurements respectively by the proposed physical algorithm for the study area. The synchronous field measurements at two field sites were conducted to validate the retrieval LST, the differences between the retrieved LST and the field measurements are in the range of -0.15 K and 1.11 K. The emissivity errors of MODIS bands 31 and 32, compared with the EOS MODIS LST/LSE data products (MOD11_L2/MYD11_L2 V5) by the physics-based day/night algorithm, are from 0.0018 to 0.44 and from 0.0058 to 1.24, respectively. Meanwhile, the retrieved atmospheric profiles fully agree with the standard atmospheric temperature-water vapor profiles and with the results from single MODIS data onboard Terra or Aqua satellite by the former two-step physical algorithm. Therefore, the proposed algorithm is robust enough to improve the retrieval accuracy of the atmospheric profiles and land surface parameters. And it will have four pairs of the retrieval results for one area each day by integrating these MODIS measurements from Terra and Aqua satellites.

New hybrid algorithm for land surface temperature retrieval from multiple-band thermal infrared image without atmospheric and emissivity data inputs

Land surface temperature(LST)retrieval from thermal infrared(TIR)remote sensing image requires atmospheric and land surface emissivity(LSE)data that are sometimes unattainable.To overcome this problem,a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input,by combining the split-window(SW)and temperature–emissivity separation(TES)algorithms.The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands,and such radiance is applied to the TES algorithm to retrieve LST and LSE.The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER).Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020,respectively.Moreover,the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm,involving 1%–2%uncertainty in atmospheric correction.The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products,indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement.Finally,the hybrid algorithm is applied to at two places.

Estimating global land surface broadband thermal-infrared emissivityusing advanced very high resolution radiometer optical data

An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.

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。