基于Landsat-5 TM 估算地表温度

基于Landsat25 TM 的热红外波段(6 波段) 数据, 运用覃志豪的单窗算法估算了张掖绿洲地区的地表温度, 结果表明沙漠和戈壁的地表温度最高, 水体和绿洲的温度最低, 估算的地表温度的结果符合地表水热关系。因此, 这种方法能较好地反演出张掖绿洲地表温度的分布状况。     

基于TM6数据的成都市地表温度反演

本利用Landsat5的TM6热红外数据，利用实时的地表温度对不同温度演算算法进行了回归检验，根据统计的原理对各种方法做出评价，并优先了一种方法进行成都市地面温度场的分布分析。本也就同一大气条件下的地温和气温之间的关系作了分析，认为用卫星影像的DN值代替气温研究热岛效应是不合适的。     

基于Landsat-5 TM数据的河南省白沙灌区地表温度反演研究

地表温度是土壤水分和植被水分状态的指示计,在干旱遥感监测中有重要作用。应用Landsat-5 TM遥感数据和气象资料,利用归一化植被指数(NDVI)区分地表覆盖类型,采用Van de Griend的经验公式法结合典型地表赋值法计算出地表比辐射率。用单窗算法和单通道算法分别对河南省白沙灌区地表温度进行反演,结果表明:两种方法均能较好地将白沙灌区地表温度分布趋势反映出来,单窗算法的反演精度较高,绝对误差为1.1 ℃,更适宜白沙灌区的地表温度反演,进而可以提高灌区旱情遥感监测精度。     

A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6

A C++ language-based software tool for retrieving land surface temperature (LST) from the data of Landsat TM/ETM+ band6 is developed. It has two main functional modules: (1) Three methods to compute the ground emissivity based on land use/cover classification image, NDVI image and the ratio values of vegetation and bare ground and (2) Converting digital numbers (DNs) from TM/ETM+ band6 to LST. In the software tool, Qin et al.'s mono-window algorithm and Jiménez-Muňoz and Sobrino's single channel algorithm are programmed to retrieve LST. It will be a useful software tool to study the thermal environment of ground surface or the energy balance between the ground and the bottom atmosphere by using the thermal band of Landsat TM/ETM+.     

利用TM6数据反演陆地表面温度新算法研究

陆地表面温度(LST)反演一直是热红外遥感研究中的一大难题。虽然TM 6数据具有较高的空间分辨率(120 m),但由于只有一个热通道,要得到地表真实温度,原来需要利用辐射传输方程的方法,实时资料的缺乏限制了该方法的应用。因而由TM 6数据得到的通常都是星上亮度温度,而星上亮度温度与实际地表温度差距较大,因此,其反演的温度精度不高。而单窗算法和普适性单通道算法的提出为从TM 6数据较高精度地反演陆地表面温度提供了可能。分析和研究了这两个新的单通道温度反演算法,并针对北京市的实际情况,利用2005年5月6日的TM数据对北京市的陆地表面温度进行了反演,并用实地测量数据进行了比较验证。结果表明这两种温度反演算法都取得了较高的精度,它们的rm sd值分别为1.38°和2.18°。     

Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie

Algorithms were developed from LANDSAT 7 ETM+ data for the July 1, 2000 overpass and LANDSAT 5 Thematic Mapper (TM) data for the September 27, 2000 overpass for Path 20 Row 31 (including Toledo, OH) to measure relative phycocyanin content (PC) and turbidity in the western basin of Lake Erie. Water samples were collected from discrete hydrographic stations arranged in a 20×4 km grid adjacent to the Ohio shoreline during a 6-h period spanning each of the two LANDSAT overpasses. The samples were analyzed for chlorophyll (chl) a content and turbidity. In addition, the concentration of phycocyanin, a light-harvesting pigment associated with cyanobacteria, was estimated from the ratio of phycocyanin/chl a in vivo fluorescence (IVPF/IVCF). A dark-object-subtracted, spectral ratio model derived from the July 1, 2000 data was found to be the most robust, when applied to the September 27, 2000 data. The same July 1, 2000 model (or algorithm) for PC was then applied to LANDSAT 7 ETM+ frames for July 16 and August 1, 2002 of the Path 19 Row 31 frame (including Cleveland, OH) and to the August 8, 2002 frame of Path 20 Row 31. Moderate, very low, and high PC values were detected in the western basin of Lake Erie on July 16, August, 1, and August 8, 2002, respectively. On September 17, 2002, local media reported a large Microcystis bloom in the western basin. The high PC values on August 8, 2002 may have represented early stage detection of the large Microcystis bloom that was reported 5 weeks later. The PC algorithm derived in this study will improve our understanding of the temporal and spatial dynamics of cyanobacterial bloom formation in Lake Erie and other systems. It may also serve to alert municipalities to the presence of potentially toxic bloom events.     

Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX

A sequence of five high-resolution satellite-based land surface temperature (T_s) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.     

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000-2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km² region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.     

地表温度热红外遥感反演的研究现状及其发展趋势

区域性或全球性的地表温度, 只有通过遥感手段才能获得, 在诸多应用中是一个非常重要的参数。地表温度反演是热红外遥感研究的热点和难点之一, 大气校正、温度与比辐射率的分离是必须考虑的两个重要方面。近年来有关的研究非常多, 主要反演方法可分为5 类: 单通道方法、分裂窗(双波段) 方法、多波段温度- 比辐射率分离方法、多角度温度反演方法和多角度与多通道相结合的方法。这些方法都各有利弊, 如何提高反演的精度和模型的适用性是地表温度热红外遥感的未来发展趋势, 理论和实验相结合的多种信息源的综合应用成为必然的要求。     

反演陆面温度的分裂窗口算法与应用分析

分裂窗口算法是目前由热红外遥感图象数据获取陆面温度最主要的方法。文中对１９９７年８月２２日内蒙古巴丹吉林沙漠地区的ＮＯＡＡ／ＡＶＨＲＲ热红外图像,利用辐射传输模型ＬＯＷＴＲＡＮ７计算大气参数进行大气校正,在采用Ｌｉ＆Ｂｅｃｋｅｒ算法（１９９３）反演出具有一定可信度的地表发射率基础上,选取常见的５种分裂窗口算法分别获取了该地区的地表辐射温度,并以Ｓｏｂｒｉｎｏ１９９１算法结果为标准,进行了算法间比较     

Intercomparison of versions 4, 4.1 and 5 of the MODIS Land Surface Temperature and Emissivity products and validation with laboratory measurements of sand samples from the Namib desert, Namibia

Eight new refinements were implemented in the MODIS Land Surface Temperature and Emissivity (LST&E) product suite when transitioning from version 4 (V4) to version 5 (V5). The refinements were designed to improve the spatial coverage, stability, and accuracy of the product suite. Version 4.1 (V4.1) is an interim collection which uses V5 input products (MOD02, MOD03, MOD07, MOD10, and MOD35), but the LST&E retrieval algorithm is unchanged from V4 in which the split-window and day/night temperature retrieval algorithms are only partially incorporated, and not fully incorporated as in V5. A test dataset for the V4.1 product was produced by MODAPS for a 3-month period from July through September 2004, and after an initial evaluation period, it was decided to generate the V4.1 product from mission period 2007001 onwards as a continuation of previous years of V4 data. This paper compares MODIS retrieved surface emissivities between V4, V4.1 and V5 using the level-3 MODIS daily LST&E product, MOD11B1.Comparisons of MOD11B1 retrieved surface emissivity during the Jul-Sep 2004 test period with lab measurements of sand samples collected at the Namib desert, Namibia result in a combined mean absolute emissivity difference for bands 29 (8.55 µm), 31 (11 µm) and 32 (12 µm) of 1.06%, 0.65% and 1.93% for V4, V4.1 and V5 respectively. Maximum band 29 emissivity differences with the lab results were 4.10%, 2.96% and 8.64% for V4, V4.1 and V5 respectively. These results indicate that over arid and semi-arid areas, users should consider using MODIS V4 or V4.1 data instead of V5. Furthermore, users should be careful not to develop time series from a mixture of product versions that could introduce artifacts at version boundaries.     

土地沙漠化监测中TM影像的利用

沙漠化是干旱、半干旱和部分半湿润地区由于不合理的人类活动与脆弱的生态环境相互作用所造成的地表呈现类似沙漠景观的土地退化。利用TM影像开展沙漠化土地的动态监测是一种行之有效的方法。探讨了沙漠化土地的遥感监测方法、影响利用TM影像对沙漠化监测效果的因素、沙漠化土地的TM影像特征和TM影像监测的指标体系。建立了一般性的沙漠化指征及其程度分级系统,其中以地表形态的变化为主要指征,同时考虑土壤、植被及生态系统各方的变化,并使之具有普遍的代表意义和明确的易于判读的性质。在此基础上,通过对毛乌素地区的实际工作,揭示了该地区沙漠土地的动态变化的情况。毛乌素地区沙漠化土地面积从1987年的32586km^2（占监测区域总面积的67．5％）下降到1993年的30650km^2（占监测区域总面积的63．5％）,减少了1936km^2,在这一段时间里,沙漠化过程总体上是处于逆转中,平均每年均有277km^2的沙漠化土地得到了治理。     

Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data

The accuracy of the Land Surface Temperature (LST) product generated operationally by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) from the data registered by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary METEOSAT Second Generation 2 (MSG2, Meteosat 9) satellite was assessed on two test sites in Eastern Spain: a homogeneous, fully vegetated rice field and a high-plain, homogeneous area of shrubland. The LSA SAF LSTs were compared with ground LST measurements in the conventional temperature-based (T-based) method. We also validated the LSA SAF LST product by using an alternative radiance-based (R-based) method, with ground LSTs calculated from MSG-SEVIRI channel 9 brightness temperatures (at 10.8 μm) through radiative transfer simulations using atmospheric temperature and water vapor profiles together with surface emissivity data. Two lakes were also used for validation with the R-based method. Although the LSA SAF LST algorithm works mostly within the uncertainty expectation of ± 2 K, both validation methods showed significant biases for the LSA SAF LST product, up to 1.5 K in some cases. These biases, with the LSA SAF LST product overestimating reference values, were also observed in previous studies. Nevertheless, the present work points out that the biases are related to the land surface emissivities used in the operational generation of the product. The use of more appropriate emissivity values for the test sites in the LSA SAF LST algorithm led to better results by decreasing the biases by 0.7 K for the shrubland validation site. Furthermore, we proposed and checked an alternative algorithm: a quadratic split-window equation, based on a physical split-window model that has been widely proved for other sensors, with angular-dependent coefficients suitable for the MSG coverage area. The T-based validation results for this algorithm showed LST uncertainties (robust root-mean-squared-errors) from 0.2 K to 0.5 K lower than for the LSA SAF LST algorithm after the emissivity replacement. Nevertheless, the proposed algorithm accuracies were significantly better than those obtained for the current LSA SAF LST product, with an average accuracy difference of 0.6 K.     

TM热红外波段等效比辐射率估算

地表比辐射率是热红外遥感获取地表温度必不可少的参数。目前,实验室或野外实际测量的都是8~14um热红外波段范围内的地表比辐射率,这与Landsat 5 TM热红外波段10.4~12.5um范围内的地表比辐射率还存在着一定的差异。本文将着重探讨TM热红外范围内地表比辐射率的估算方法,然后根据估算出的地表比辐射率,利用覃志豪等提出的单窗算法[1~2],对北京城八区进行地表温度反演。结果表明,该方法能获得较为合理的地表温度反演结果。     

Temperature and emissivity separation from ASTER data for low spectral contrast surfaces

The performance of Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) thermal infrared (TIR) data product algorithms was evaluated for low spectral contrast surfaces (such as vegetation and water) in a test site close to Valencia, Spain. Concurrent ground measurements of surface temperature, emissivity, and atmospheric radiosonde profiles were collected at the test site, which is a thermally homogeneous area of rice crops with nearly full vegetation cover in summer. Using the ground data and the local radiosonde profiles, at-sensor radiances were simulated for the ASTER TIR channels and compared with L1B data (calibrated at-sensor radiances) showing discrepancies up to 3% in radiance for channel 10 at 8.3 μm (equivalently, 2.5 °C in temperature or 7% in emissivity), whereas channel 13 (10.7 μm) yielded a closer agreement (maximum difference of 0.5% in radiance or 0.4 °C in temperature). We also tested the ASTER standard products of land surface temperature (LST) and spectral emissivity generated with the Temperature-Emissivity Separation (TES) algorithm with standard atmospheric correction from both global data assimilation system profiles and climatology profiles. These products showed anomalous emissivity spectra with lower emissivity values and larger spectral contrast (or maximum-minimum emissivity difference, MMD) than expected, and as a result, overestimated LSTs. In this work, a scene-based procedure is proposed to obtain more accurate MMD estimates for low spectral contrast materials (vegetation and water) and therefore a better retrieval of LST and emissivity with the TES algorithm. The method uses various gray-bodies or near gray-bodies with known emissivities and assumes that the calibration and atmospheric correction performed with local radiosonde data are accurate for channel 13. Taking the channel 13 temperature (atmospherically and emissivity corrected) as the true LST, the radiances for the other channels were simulated and used to derive linear relationships between ASTER digital numbers and at-ground radiances for each channel. The TES algorithm was applied to the adjusted radiances and the resulting products showed a closer agreement with the ground measurements (differences lower than 1% in channel 13 emissivities and within ± 0.3 °C in temperature for rice and sea pixels).     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光G近红外多光谱波段的基础上,新增加的８个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5％。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data

Vapor Pressure Deficit (VPD) is a principle mediator of global terrestrial CO₂ uptake and water vapor loss through plant stomata. As such, methods to estimate VPD accurately and efficiently are critical for ecosystem and climate modeling efforts. Based on prior work relating energy partitioning, remotely sensed land surface temperature (LST), and VPD, we developed simple linear models to predict VPD using saturated vapor pressure calculated from MODIS LST at a number of different temporal and spatial resolutions. We developed and assessed the LST-VPD models using three data sets: (1) instantaneous and daytime average ground-based VPD and radiometric temperature from the Soil Moisture Experiments in 2002 (SMEX02); (2) daytime average VPD from AmeriFlux eddy covariance flux tower observations; and (3) estimated daytime average VPD from Global Surface Summary of Day (GSSD) observations. We estimated model parameters for VPD estimation both regionally (MOD11 A2) and globally (MOD11 C2) with RMSE values ranging from .32 to .38 kPa. VPD was overestimated along coastlines and underestimated in arid regions with low vegetation cover. Also, residuals were larger with higher VPDs because of the non-linear function of saturation vapor pressure with LST. Linear relationships were seen at multiple scales and appear useful for estimation purposes within a range of 0 to 2.5 kPa.     

Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI

This work addressed the retrieval of Land Surface Emissivity (LSE) from combined mid-infrared and thermal infrared data of Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the geostationary satellite—Meteosat Second Generation (MSG). To correct for the atmospheric effects in satellite measurements, a new atmospheric correction scheme was developed for both Middle Infra-Red (MIR) and Thermal Infra-Red (TIR) channels. For the MIR channel, because it is less sensitive to the change of water vapor content, the clear-sky and time-nearest European Centre for Median-range Weather Forecast (ECMWF) atmospheric data were used for the images where no atmospheric data are available. For TIR channels, a modified model of Diurnal Temperature Cycle (DTC) used by Göttsche and Olesen [Göttsche, F. M., and Olesen, F. S. (2001). Modeling of diurnal cycles of brightness temperature extracted from METEOSAT data. Remote Sensing of Environment, 76, 337-348.] and Schädlich et al. [Schädlich, S., Göttsche, F. M., and Olesen, F. S. (2001). Influence of land surface parameters and atmosphere on METEOSAT brightness Temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment, 75, 39-46.] was adopted. The separation of Land Surface Temperature (LST) and LSE is based on the concept of the Temperature Independent Spectral Indices (TISI) [Becker, F., and Li, Z. L. (1990a). Temperature independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17-33.] constructed with one channel in MIR and one channel in TIR. The results of two different combinations (combination of channels 4 and 9 and of channels 4 and 10) and two successive days at six specific locations over North Africa show that the retrievals are consistent. The range of emissivity in MSG-SEVIRI channel 4 goes from 0.5 for bare areas to 0.96 for densely vegetated areas, whereas the emissivities in MSG-SEVIRI channels 9 and 10 are usually from 0.9 to 0.95 for bare areas and from 0.95 to 1.0 for vegetated areas. For densely vegetated areas, the emissivities in MSG-SEVIRI channel 9 are larger than the ones in channel 10, whereas the opposite is observed over bare areas. The rms differences between two combinations over the whole studied region are 0.017 for emissivity in channel 4, 0.008 for emissivity in channel 9 and 0.007 for emissivity in channel 10.     

The North American ASTER Land Surface Emissivity Database (NAALSED) Version 2.0

Thermal Infrared (TIR) data are supplied by instruments on several satellite platforms including the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), which was launched on the Terra satellite in 1999. ASTER has five bands in the TIR and a spatial resolution of 90 m. A mean seasonal, gridded, Land Surface Temperature and Emissivity (LST&E) database has been produced at 100 m spatial resolution using all the ASTER scenes acquired for the months of Jan-Mar (winter) and Jul-Sep (summer) over North America. Version 2.0 of the North American ASTER Land Surface Database (NAALSED) (http://emissivity.jpl.nasa.gov) has now been released and includes two key refinements designed to improve the accuracy of emissivities over water bodies and account for the effects of fractional vegetation cover. The water adjustment replaces ASTER emissivity values over inland water bodies with a measured library emissivity spectrum of distilled water, and then re-calculates the surface temperatures using a split-window algorithm. The accuracy of ASTER emissivities over vegetated surfaces is improved by applying a fractional vegetation cover adjustment (TES_Pv) to the ASTER Temperature Emissivity Separation (TES) calibration curve. Comparisons of NAALSED emissivity spectra with in-situ data measured over a grassland in Northern Texas resulted in a combined absolute difference for all five ASTER bands of 1.0% for the summer emissivity data, and 0.1% for the winter data—a 33-50% improvement over the original TES results.     

Land surface temperature retrieval from MSG1-SEVIRI data

We have developed a physical-based split-window Land Surface Temperature (LST) algorithm for retrieving the surface temperature from SEVIRI/MSG1 (Spinning Enhanced Visible and Infrared Imager/Meteosat Second Generation1) data in two thermal infrared bands (IR 10.8 and IR 12.0). The proposed algorithm takes into account the SEVIRI angular dependence. The numerical values of the split-window coefficients have been obtained from a statistical regression method, using synthetic data. The look-up tables for atmospheric transmission, path radiance, and downward thermal irradiance are calculated with the MODTRAN3 code. The new LST algorithm has been tested with simulated SEVIRI/MSG1 data over a wide range of atmospheric and surface conditions. Comprehensive sensitivity and error analyses have been undertaken to evaluate the performance of the proposed LST algorithm and its dependence on surface properties, the ranges of atmospheric conditions and surface temperatures, and on the noise-equivalent temperature difference. The results show that the algorithm is capable of producing LST with a standard deviation lower than 1.5 K for viewing zenith angles lower than 50°. Since MSG1 is becoming fully operational in 2004, the proposed algorithm will allow MSG1 users to obtain surface temperatures immediately.