针对MODIS 数据的地表温度非线性迭代反演方法

地表温度是气象、水文、生态等研究领域中的一个重要参数。构建了MODIS31/ 32 波段的热辐射传输方程, 讨论了方程的数值迭代解法, 提出了针对MODIS 数据地表温度的非线性迭代反演方法, 并介绍了大气透过率和地表比辐射率这两个中间参数的估计方法。误差及敏感性分析表明,提出的方法对大气透过率和地表比辐射率都不敏感, 反演精度优于传统的线性分裂窗算法。     

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。     

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MODIS 数据的南京市夏季城市热岛分析

城市热岛效应是当前城市环境与气候主要研究内容之一。地表温度与气温之间有紧密的联系, 通过遥感反演地表温度已成为研究城市热岛的有效手段。利用MODIS 数据, 获取地表比辐射率与大气透过率2 个基本参数, 运用劈窗算法反演南京市夏季地表温度。基于不同时相的MODIS数据, 对4 幅南京市地表温度反演图像作对比分析, 较好地显示了南京市城市热岛的空间分布、热岛范围和城市热岛强度, 结果表明南京市夏季热岛问题较为严重。     

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。     

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。     

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。     

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。     

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。     

基于MODIS数据的典型能见度条件下大气透过率计算

现有的辐射传输模型仅考虑气溶胶影响下的大气透过率，在能见度低于5km时，会给大气透过率计算带来较大的误差。本文综合考虑影响大气透过率的气溶胶和水汽因素，并利用中光谱分辨率MODIS（moderate resolution imaging spectmradiometer）数据，在特征参数的空间及时间尺度变化均比较大时，对大气透过率进行了定量反演，并进一步建立了整层大气透过率与行星反照率的关系模型，为近地层大气能见度的遥感监测提供了方法。     

Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.     

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.     

Land Surface Temperature Data Fusion of Landsat ETM and MODISby Combining TsHARP and STITFM Model

Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere,and LST has widely used in the fields of urban heat island effect,soil moisture and surface radiative flux.Currently,no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution,which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image,a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm,defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting,the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method,the fusion results in the selected areas show some regularity with the increasing of the window.In general,a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.     

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.     

Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil

The paper investigated the application of MODIS data for mapping regional land cover at moderate resolutions (250 and 500 m), for regional conservation purposes. Land cover maps were generated for two major conservation areas (Greater Yellowstone Ecosystem—GYE, USA and the Pará State, Brazil) using MODIS data and decision tree classifications. The MODIS land cover products were evaluated using existing Landsat TM land cover maps as reference data. The Landsat TM land cover maps were processed to their fractional composition at the MODIS resolution (250 and 500 m). In GYE, the MODIS land cover was very successful at mapping extensive cover types (e.g. coniferous forest and grasslands) and far less successful at mapping smaller habitats (e.g. wetlands, deciduous tree cover) that typically occur in patches that are smaller than the MODIS pixels, but are reported to be very important to biodiversity conservation. The MODIS classification for Pará State was successful at producing a regional forest/non-forest product which is useful for monitoring the extreme human impacts such as deforestation. The ability of MODIS data to map secondary forest remains to be tested, since regrowth typically harbors reduced levels of biodiversity. The two case studies showed the value of using multi-date 250 m data with only two spectral bands, as well as single day 500 m data with seven spectral bands, thus illustrating the versatile use of MODIS data in two contrasting environments. MODIS data provide new options for regional land cover mapping that are less labor-intensive than Landsat and have higher resolution than previous 1 km AVHRR or the current 1 km global land cover product. The usefulness of the MODIS data in addressing biodiversity conservation questions will ultimately depend upon the patch sizes of important habitats and the land cover transformations that threaten them.     

Land surface temperature and emissivity retrieval by integrating MODIS data onboard Terra and Aqua satellites

This paper presents an algorithm to retrieve land surface temperature (LST) and emissivity by integrating MODIS (Moderate Resolution Imaging Spectroradiometer) data onboard Terra and Aqua satellites. For a study area, there will be four pairs of day and night observations by MODIS onboard two satellites every day. Solar zenith angle, view zenith angle, and atmospheric water vapour have first been taken as independent variables to analyse their sensitivities to the same infrared channel measurements of MODIS on both Terra and Aqua satellites. Owing to their similar influences on the same MODIS band from Terra and Aqua satellites, four pairs of MODIS data from Terra and Aqua satellites can be thought of as MODIS measurement on a satellite at different viewing angles and viewing time. Comparisons between the retrieved results and in-situ measurements at three test sites (Qinghai Lake, Poyang Lake and Luancheng in China) indicate that the root mean square (rms) error is 0.66 K, except for the sand in Poyang Lake area. The rms error is less than 0.7 K when the retrieved results are compared with Earth Observing System (EOS) MODIS LST data products using the physics-based day/night algorithm. Emissivities retrieved by this algorithm are well compared to EOS MODIS emissivity data products (V5). The proposed algorithm can therefore be regarded as complementary and an extension to the EOS physics-based day/night algorithm.     

Fire and smoke observed from the Earth Observing System MODIS instrument--products,validation, and operational use

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, launched on the National Aeronautics and Space Administration Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 µm and 400 K at 11 µm, which can only be attained in rare circumstances at the 1 km fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. Advanced Very High Resolution Radiometer and Along Track Scanning Radiometer), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MODIS solar channels, extending from 0.41 µm to 2.1 µm. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 µm channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern USA in Summer 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real-time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.