地表发射率不同的估算方法对地表温度的影响

随着遥感技术的日益成熟,用热红外波段反演地表温度的方法也日趋完善,其中一个关键参数--地表发射率的估算,直接影响着地表温度的反演结果。基于TM6的热红外波段,分别采用最常用的Van的地表发射率估算方法和综合了覃志豪与Sobrino的估算方法,对北京地区的地表温度进行了反演,并在此基础上对这2种估算方法所得结果进行了比较,从而为找出更为有效的地表发射率估算公式提供依据。结果表明,对于农林地2种估算方法都比较适合,但是对于水体和城市等人口密集地区,则需要和当地的实测数据进行比对,从而确定更为合适有效的估算方法,以得到更为精确的地表温度结果。     

一个用神经网络优化的针对ASTER数据反演地表温度和发射率的多波段算法

提出了针对ASTER数据同时反演地表温度和发射率的多波段算法。即利用ASTER数据的第11~14热红外波段建立热辐射传输方程，并同时对相应波段的发射率建立近似线性方程，得到6个方程6个未知数，从而形成了针对ASTER数据的同时反演地表温度和发射率的多通道算法。利用3种方法求解方程： ①先分类，然后进行数学计算； ②利用最小二乘法； ③利用神经网络方法。利用辐射传输模型MODTRAN 4模拟数据进行反演及验证分析，结果表明，神经网络能够提高算法的精度和实用性，反演的地表温度平均误差为0.5 ℃，反演的发射率平均误差分别在0.007(11、12波段)和0.006(13、14波段)以下。     

Landsat 8地表温度反演及验证—以黑河流域为例

地表温度是区域和全球尺度地表物理过程的一个重要参数,目前已有的地表温度产品空间分辨率较低,缺乏高空间分辨率的地表温度产品。Landsat系列卫星提供了大量免费的高空间分辨率遥感数据,然而对应的高空间分辨率地表温度产品还未见到,为了获取长时间序列的高空间分辨率地表温度数据,针对Landsat 8 TIRS数据提出了一个物理单通道地表温度反演算法。该算法首先利用ASTER全球地表发射率产品(ASTER GED)结合Landsat 8地表反射率产品计算Landsat 8影像的地表发射率,然后利用快速辐射传输模型RTTOV结合MERRA大气廓线数据对热红外影像进行大气校正,最后利用物理单通道地表温度反演算法得到地表温度。利用黑河流域HiWATER试验2013年—2015年15个站点的实测地表温度数据对本文方法和普适性单通道算法进行了验证,同时对验证站点的空间异质性进行了分析。结果表明,本文方法和普适性单通道算法估算的地表温度整体精度均较高,能够获取高精度、高空间分辨率的地表温度数据,可以服务于城市热岛效应、地表蒸散发估算等相关研究。     

高分五号全谱段光谱成像仪地表温度与发射率反演

地表温度在全球能量平衡和气候变化研究中具有重要意义。中国新一代高分辨率卫星高分五号卫星(GF-5)搭载的全谱段成像光谱仪有4个40 m空间分辨率的热红外波段,可以提供高空间分辨率的地表温度信息。本文提出了适用于全谱段成像光谱仪的温度与发射率分离TES(Temperature and Emissivity Separation)算法同时反演地表温度和发射率,为了提高大气校正精度,算法加入了水汽缩放WVS(Water Vapor Scaling)大气校正方法。首先利用Seebor V5.0全球大气廓线库构建模拟数据对算法精度进行了评价;然后利用张掖地区11景ASTER影像作为替代数据和同步的地面实测数据对算法精度进行了验证。模拟数据结果表明加入WVS方法后TES算法反演地表温度的RMSE由2.59 K降低到1.54 K,4个波段地表发射率的RMSE分别从0.122、0.12、0.102和0.037降低到0.042、0.04、0.028和0.026;地表验证结果表明本文算法反演的地表温度与站点实测值具有更好的一致性,平均Bias由1.08 K降低到0.47 K,RMSE由2.17 K降低到1.7 K;反演的各波段地表发射率与地面实测结果误差均小于1%。因此,本文提出的温度与发射率分离算法具有较高精度,可以利用GF-5数据获取高精度高空间分辨率的地表温度和发射率数据,服务于其他相关研究。     

基于ASTER遥感影像的亚热带植被覆盖度信息提取

根据植被指数估算植被覆盖度的原理,以混合像元线性分解模型两个重要参数为基础,建立基于归一化植被指数(NDVI)进行估算植被覆盖度模型是研究区域植被覆盖度的一种重要方法.本文以广州市花都区为实验区,利用ASTER高光谱影像对此方法进行验证性分析,实验结果表明:用该方法提取ASTER影像的植被覆盖度具有较好的可行性.     

基于劈窗算法从ASTER遥感数据反演地表温度

应用卫星热红外遥感影像反演地表温度对于研究城市生态环境、气象过程具有重要意义,ASTER 遥感数据为此提供了有效的信息源。针对从 ASTER 数据中反演地表温度(LST)的需要,首先利用 MODIS 数据反演大气水汽含量,并模拟出大气水汽含量与大气透过率的关系,求得大气透过率,然后通过决策树分类结果和地物光谱特征计算出地表反射率,最后采用劈窗算法反演出地表温度。通过某市2个季节的试验表明该方法具有较高的精度,能够有效应用于城市热环境分析,为城市物理环境综合分析评价提供支持。     

基于ASTER数据的城市热环境遥感监测研究

以ASTER数据为数据源,采用同一颗卫星上的MODIS数据得到大气透过率;利用可见光和近红外波段对下垫面类型进行分类和利用JPL(Jet Propulsion Lab)提供的光谱库计算地表比辐射率,进而采用劈窗算法进行地表温度(Land Surface Temperature,LST)的反演。在此基础上,利用反演的LST、分类结果和归一化差值植被指数(NDVI),对沧州地区的城市热环境进行了定量分析,研究结果可为进一步深入探讨城市热岛的发生发展规律以及城市热环境的模拟调控、优化布局提供一定的科学依据。     

基于遥感的蒸散与土地利用及主要地表参数的关系研究——以乌裕尔河下游为例

以TM/ETM+影像为数据源,利用自动分类与目视解译相结合的方法提取了研究区1992～2001年共5个时相的土地利用信息;采用SEBAL模型估算影像过境当天的日蒸散量;最后对蒸散与土地利用变化、植被覆盖度及地表温度等地表参数之间的关系进行了相关性分析。结果表明:水体、湿地具有较高的日蒸散量,草地、旱地、林地次之,盐碱地、居民地最低。说明蒸散与地表温度、植被覆盖度等密切相关,土地利用变化是引起上述变化的主要驱动力之一。     

被动微波遥感反演地表发射率研究进展

微波地表发射率是表征地表特征的重要参数,也是反演地表、大气参数的重要条件.相比较物理模型,其模拟计算需要若干输入参数,且相当一部分地表、植被特征参数很难从常规资料中获取,应用星载被动微波辐射计资料可以在更大空间和时间尺度范围内直接反演地表发射率.从目前常用的几种被动微波遥感反演方法(包括经验统计方法、辐射传输方程方法、指数分析方法、神经网络方法、一维变分方法等等)回顾了微波地表发射率反演的国内外研究进展及其研究中存在的问题,并对这些方法的优、缺点进行了评价.最后指出,今后应开发识别和订正直接影响卫星观测值的无线电频率干扰(RFI)算法,改善云、雨检测算法,并且加强微波波段大气辐射传输等过程的机理研究.     

基于ASTER数据反演我国南方山地陆表温度

 以贵州省黎平县山地植被覆盖区为例，基于ASTER遥感数据进行15 m分辨率的归一化植被指数制图和地表发射率制图，在利用 MODTRAN 4大气辐射传输模型进行大气订正的基础上，基于普朗克辐射方程的推导反演陆表温度，取得了较为理想的结果。     

Himawari 8 AHI数据地表温度反演的实用劈窗算法

地表温度是水文、气象、气候和环境等研究领域中的关键参数,利用热红外遥感可快速获取区域和全球高精度的地表温度数据。Himawari 8号是日本发射的新一代地球静止轨道气象卫星,星上搭载AHI(Advanced Himawari Imager)成像仪,具有更高的时空分辨率。利用AHI第14(11.2μm)和15(12.35μm)通道星上亮温数据,提出反演地表温度的实用劈窗算法,其中输入的发射率数据利用ASTER GED(Global Emissivity Dataset)v4计算得到。劈窗算法的系数由观测角度和大气水汽含量分区决定,其中大气水汽含量由两个劈窗通道直接估算得到。利用黑河流域生态—水文过程综合遥感观测联合试验(Hi WATER)4个站点的实测数据和中国7个湖泊中心点的MODIS地表温度产品对反演结果进行验证,结果表明,算法的均方根误差(RMSE)在3 K以内,达到目前常用遥感地表温度产品的精度。同时与利用MOD11C3 C6产品估算的发射率和温度反演结果进行对比分析,发现ASTER GED反演的结果具有更高的精度,适合用来生产高精度的地表温度产品。     

热红外遥感地表温度与发射率地面验证进展

地表温度与发射率是地表—大气系统长波辐射和潜热通量交换的直接驱动力,是描述区域和全球尺度上地表能量平衡与水平衡的重要参数,其时空变化信息在气象预测、气候变化、水循环、地质勘探、农林监测和城市热环境等诸多领域具有广泛的应用.热红外遥感作为当前获取区域或全球尺度上地表温度和发射率的最有效手段之一,相较于传统的地面点位测量方...     

Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter

In this study, the multi-resolution Kalman filter (MKF) algorithm, which can handle multi-resolution problems with high computational efficiency, was used to blend two emissivity products: the Global LAnd Surface Satellite (GLASS) (BBE) product and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) narrowband emissivity (NBE) product. The ASTER NBE product was first converted into a BBE product. A new detrending method was used to transfer the BBEs into a process suitable for the MKF. The new detrending method was superior to the two existing methods. Finally, both the de-trended GLASS and ASTER BBE products were incorporated into the MKF framework to obtain the optimal estimation at each scale. Field measurements collected in North America were used to validate the integrated BBEs. Visually, the fusion map showed good continuity, with the exception of the border areas, and the quality of the fusion map was better than that of the original maps. The validation results indicate that the MKF improved the BBE product accuracy at the coarse scale. In addition, the MKF was capable of recovering missing pixels at a finer scale.     

Estimating global land surface broadband thermal-infrared emissivity using 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 near-infrared data from 1981 through 1999 was presented. Land surface was divided into three types according to its normalized difference vegetation index (NDVI) values: bare soil, vegetated area, and transition zone. For each type, BBE at 8–13.5 µm was formulated as a nonlinear function of AVHRR reflectance for Channels 1 and 2. Given difficulties in validating coarse emissivity products with ground measurements, the algorithm was cross-validated by comparing retrieved BBE with BBE derived through different methods. Retrieved BBE was initially compared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square error were less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 for transition zones, and ?0.002 and 0.005 for vegetated areas. Retrieved BBE was also compared with BBE obtained through the NDVI threshold method. The proposed algorithm was better than the NDVI threshold method, particularly for bare soil. Finally, retrieved BBE and BBE derived from MODIS data were consistent, as were the two BBE values.     

Effects of prediction accuracy of the proportion of vegetation cover on land surface emissivity and temperature using the NDVI threshold method

Predicting land surface energy budgets requires precise information of land surface emissivity (LSE) and land surface temperature (LST). LST is one of the essential climate variables as well as an important parameter in the physics of land surface processes at local and global scales, while LSE is an indicator of the material composition. Despite the fact that there are numerous publications on methods and algorithms for computing LST and LSE using remotely sensed data, accurate prediction of these variables is still a challenging task. Among the existing approaches for calculating LSE and LST, particular attention has been paid to the normalised difference vegetation index threshold method (NDVI^THM), especially for agriculture and forest ecosystems. To apply NDVI^THM, knowledge of the proportion of vegetation cover (P_V) is essential. The objective of this study is to investigate the effect of the prediction accuracy of the P_V on the estimation of LSE and LST when using NDVI^THM. In August 2015, a field campaign was carried out in mixed temperate forest of the Bavarian Forest National Park, in southeastern Germany, coinciding with a Landsat-8 overpass. The P_V was measured in the field for 37 plots. Four different vegetation indices, as well as artificial neural network approaches, were used to estimate P_V and to compute LSE and LST. The results showed that the prediction accuracy of P_V improved using an artificial neural network (R²_CV = 0.64, RMSE_CV = 0.05) over classic vegetation indices (R²_CV = 0.42, RMSE_CV = 0.06). The results of this study also revealed that variation in the accuracy of the estimated P_V affected calculation results of the LSE. In addition, our findings revealed that, though LST depends on LSE, other parameters should also be taken into account when predicting LST, as more accurate LSE results did not increase the prediction accuracy of LST.     

综合典型地表组成的城市地表温度估算

针对单一的地表物质组成并不能充分反映城市地表热环境特点这一问题,该文基于热混合影像,利用线性光谱分解方法获取地表组成信息,然后利用光谱分解热混合、线性回归、决策树方法估算地表温度。结果表明:只研究单一地表组成对地表温度的影响,有可能扩大其环境效应;决策树模型在不同规则下能更好地模拟地表温度的空间异质性;光谱分解热混合模型只需要两组数据即可估算出不同地表覆盖下的地表温度,且估算精度较其他模型高;光谱分解热混合模型和多元回归模型结合4种地表组成监测其对地表温度的影响,决策树方法通过不透水面、水体、植被预测地表温度,前两者估算精度比后者高,因此综合考虑城市典型地表组成能更好反映其对地表温度的作用。     

Landsat卫星热红外数据地表温度遥感反演研究进展

作为驱动地表与大气之间能量交换的关键物理量,地表温度在众多领域中都发挥着重要作用,包括气候变化、环境监测、蒸散发估算以及地热异常勘探等.Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中.本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状,梳理了2001年-2020年...     

光谱角法在ASTER影像土地利用分类中的应用

为了验证光谱角法(SAM)对ASTER影像分类效果,本文对SAM分类的原理进行了阐述和分析,采用了SAM方法以ASTER遥感影像数据为数据源对泸沽湖地区的土地利用进行分类研究,并对分类精度进行了分析。研究结果表明SAM方法用于ASTER数据是一种有效的分类方法,对提高ASTER影像分类精度具有重要的意义。     

A disaggregation approach for estimating high spatial resolution broadband emissivity for bare soils from Landsat surface reflectance

High spatial resolution land surface broadband emissivity (BBE) is not only useful for surface energy balance studies at local scales, but also can bridge the gap between existing coarser resolution BBE products and point-based field measurements. This study proposes a disaggregation approach that utilizes the established BBE–reflectance relationship for estimating high spatial resolution BBE for bare soils from Landsat surface reflectance data. The disaggregated BBE is compared to the BBE calculated from spatial–temporal matched Advanced Spaceborne Thermal Emission and Reflectance Radiometer emissivity product. Comparison results show that better agreement is achieved over relative homogeneous areas, but deteriorated over heterogeneous and cloud-contaminated areas. In addition, field-measured emissivity data over large homogeneous desert were also used to validate the disaggregated BBE, and the bias is 0.005. The comparison and validation results indicated that the disaggregation approach can obtain high spatial resolution BBE with better accuracy for homogeneous area.