Land surface temperature and emissivity estimation for Urban Heat Island assessment using medium- and low-resolution space-borne sensors: A review

The Urban Heat Island (UHI) phenomenon, a typical characteristic on urban landscapes, has been recognised as a key driver to the transformation of local climate. Reliable retrieval of urban and intra-urban thermal characteristics using satellite thermal data depends on accurate removal of the effects of atmospheric attenuations, angular and land surface emissivity. Several techniques have been proposed to retrieve land surface temperature (LST) from coarse resolution sensors. Medium spatial resolution sensors like the Advanced Space-borne Thermal Emission and Reflection Radiometer and the Landsat series offer a viable option for assessing LST within urban landscapes. This paper reviews the theoretical background of LST estimates from the thermal infrared part of the electromagnetic spectrum, LST retrieval algorithms applicable to each of the commonly used medium-resolution sensors and required variables for each algorithm. The paper also highlights LST validation techniques and concludes by stipulating the requirements for LST temporal and spatial configuration.     

机载高光谱热红外数据的地表温度和发射率反演与应用

机载高分辨率遥感是高分对地观测的重要部分,其中高分辨率高光谱热红外数据的光谱发射率可以用于矿物识别,是对可见光遥感地物识别的有效补充。机载高光谱热红外传感器TASI（Thermal Airborne Hyperspectral Imager）在8—11.5 μm范围内设置了32个波段,在国内外常被用于地表热辐射信息、矿产资源探测等领域。本文利用2018-10在新疆富蕴县研究区的TASI航空飞行数据,首先基于再分析大气廓线NCEP数据与MODTRAN实现了TASI高光谱热红外数据的大气校正,并在基础上发展了温度与发射率分离方法TES（Temperature and Emissivity Separation method）反演研究区地表温度与发射率,采用多波段热辐射计CE312测量的地面发射率对反演结果进行了有效验证,结果表明波长大于9.6 μm的波段的发射率误差约为0.01。最后,结合反演的TASI发射率光谱曲线,采用光谱角度匹配方法提取了研究区高岭石的空间分布。研究工作涉及的相关算法与应用成果可为星载高分辨率热红外载荷数据的应用提供了相关参考。     

模拟退火法及其在抗差估计中的应用

抗差估计结果的好坏与初值的选取密切相关。当迭代初值偏离较大时,ρ函数不是严凸时,采取的选权迭代法往往不能获得全局最优值,而只能得到局部的最优解。本文提出一种新的抗差估计求解过程,即把它看作是关于未知参数的全局最优化问题,应用模拟退火法进行求解。计算结果表明:相对于传统的选权迭代法,模拟退火法对初值的选取不敏感,能给出全局最优值。     

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.     

TASI数据的温度与发射率分离算法

建立了适合于TASI的热红外高光谱发射率的经验关系,以此修正了分离方法(TES)中的MMD模型,在对温度和发射率进行精度评价的基础上,将反演结果应用于城市地表温度的日较差分析。结果表明：(1)反演的温度与实测温度的平均绝对误差为1.8 K,平均相对误差为0.59%;(2)地面实测宽波段发射率与反演发射率的平均差值为0.036,差值的标准差为0.032;(3)同种地物的发射率曲线的反演结果与实验室测量发射率曲线的波形是一致的;(4)温度日较差分析结果是合理的。因此,本文的反演精度能满足研究需求,将该方法应用于TASI数据的温度与发射率的分离是可行的。     

Some problems in the theory of determining geodetic refraction by dispersion method

The effect of the second refraction index gradient and that of humidity on the accuracy of determining refraction by dispersion method is analyzed. The feasibility of obtaining those parameters instrumentally is suggested.     

Angular effect of MODIS emissivity products and its application to the split-window algorithm

The angular effects of emissivity are ignored in current land surface temperature (LST) products. As a result, the directionality of these LST products limits their further application in many fields. Accurate correction of the angular problem of LST products requires explicit understanding of the angular effects of emissivity at the pixel scale. Currently, nearly ten years of global emissivity products of MODIS are available. However, the pixel-scale directionality of emissivity has never been analyzed. By performing a statistical analysis of 5-year MODIS emissivity products over most of East Asia, we generated the empirical relationships between the directional emissivity, land cover, and seasonal variations. Two look-up tables (LUTs) of directional emissivity were created for typical land cover types and applied to the generalized split-window algorithm to modify the MODIS LST. The results showed that the angular effect of emissivity could introduce a significant bias of −1-3 K to the 1 km resolution LST. Finally, the spatial scale effects of emissivity were analyzed, and it was found that the temperature differences caused by scale effects fell within +/−0.5 K for most pixels if 5 km emissivity was used in 1 km LST retrieval. Therefore, wide use of the LUTs can be expected.     

Distributional theory for the DIA method

The DIA method for the detection, identification and adaptation of model misspecifications combines estimation with testing. The aim of the present contribution is to introduce a unifying framework for the rigorous capture of this combination. By using a canonical model formulation and a partitioning of misclosure space, we show that the whole estimation–testing scheme can be captured in one single DIA estimator. We study the characteristics of this estimator and discuss some of its distributional properties. With the distribution of the DIA estimator provided, one can then study all the characteristics of the combined estimation and testing scheme, as well as analyse how they propagate into final outcomes. Examples are given, as well as a discussion on how the distributional properties compare with their usage in practice.     

Retrieval of land surface temperature and emissivity from satellite data: Physics, theoretical limitations and current methods

Remote sensing from satellites is the only means to obtain land surface temperature (LST) and emissivity on a larger scale. LST has many applications, e.g., in radiation budget experiments and global warming, and desertification studies. Over the last decades, substantial amount of research was dedicated towards extracting LST and emissivity from surface-leaving radiance and de-coupling the two from each other. This paper provides the physical basis, discusses theoretical limitations, and gives an overview of the current methods for space-borne passive sensors operating in the infrared range, e.g., NOAA-AVHRR, Meteosat, ERS-ATSR, TERRA-MODIS, and TERRA-ASTER. Atmospheric effects on estimated LST are described and atmospheric-correction using a radiative transfer model (RTM) is explained. The methods discussed are the single channel method, the split window techniques (SWTs), and the multi-angle method.     

An iterative PS-InSAR method for the analysis of large spatio-temporal baseline data stacks for land subsidence estimation

South-west of Tehran, the capital city of Iran, is subjected to a high deformation rate due to excessive groundwater extractions. Persistent Scatterrer SAR Interferometry (PS-InSAR) technique is used to monitor Tehran’s deformation. Three time series data including two Sentinel-1A (S-1A) spanning from 2014 to 2017, and an ENVISAT-ASAR data stack spanning from 2004 to 2010, are analyzed. The PS-InSAR technique does not perform well on ENVISAT-ASAR due to poor selection of PS points induced by large perpendicular baselines and strong temporal decorrelation of the dataset. In this paper, a novel Iterative PSI method (IPSI) is proposed to increase the PS points which are lost in PS-InSAR technique because of the unsuccessful derivation of the absolute phase value due to an integer ambiguity. The method selects PS points based on simultaneous analysis of their amplitude and phase. Results demonstrate that the density of PSs has been increased by about 4.5 times. Line of Sight (LOS) velocities obtained from both S-1A and ENVISAT-ASAR data analysis are highly compatible with each other, indicating the reliability of the both applied methods. The maximum cumulative displacements are estimated as 39.6 cm and 88.4 cm for Sentinel-1A and ENVISAT-ASAR datasets respectively. Moreover, the subsidence area has grown in the period between the data acquisition time. The methods are successfully validated by subsidence rates obtained from precise leveling and GPS observations.     

MLAMBDA: a modified LAMBDA method for integer least-squares estimation

The least-squares ambiguity Decorrelation (LAMBDA) method has been widely used in GNSS for fixing integer ambiguities. It can also solve any integer least squares (ILS) problem arising from other applications. For real time applications with high dimensions, the computational speed is crucial. A modified LAMBDA (MLAMBDA) method is presented. Several strategies are proposed to reduce the computational complexity of the LAMBDA method. Numerical simulations show that MLAMBDA is (much) faster than LAMBDA. The relations between the LAMBDA method and some relevant methods in the information theory literature are pointed out when we introduce its main procedures.     

GNSS模糊度整数估计方法图形可视化软件设计与应用分析

模糊度快速准确估计是全球卫星导航系统(GNSS)高精度定位的关键,整数取整、序贯取整和整数最小二乘估计是模糊度常用的三类整数估计方法.尽管从程序上较易实现三类估计方法,但是如何根据模糊度浮点解和精度构建整数估值的几何图形却缺乏较多的研究,不利于我们对整数估计过程的直观认知.因此,本文从理论上分别给出三类估计方法的一般形式,然后基于MATLAB GUI设计了一套三类估计方法二维几何图形构建的可视化分析软件,其功能包括三类估计方法的归整域构建、映射图构建和蒙特卡洛模拟及成功率计算.实验测试结果表明,本文设计的软件能够从几何图形角度较直观地表达出三类整数估计过程及其解算性能.      

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...     

基于场论的动态缓冲区生成方法与应用

缓冲区可分为影响度只呈单一距离关系的静态缓冲区和影响度随距离变化而呈不同强度的扩散或衰减的动态缓冲区。本文通过对现有动态缓冲区算法在实际应用中存在的不足与局限的分析,提出了一种新的基于场论的动态缓冲区生成方法。该方法以场论的观点为指导,打破了传统缓冲区在进行重叠合并时简单地采用影响度小的服从影响度大的方法,而是按照场强叠加的原理,将各个影响度进行叠加来生成动态缓冲区。论文按照实际需要,提出了两种新的动态缓冲区模型,给出基于场强积分原理的动态缓冲区影响度的计算方法,实现了基于场论的动态缓冲区的生成,并成功应用于实践中。     

基于亮温和SVM模型的干球温度推算方法

干球温度(气温)是地面气象观测中所要测定的常规要素之一。目前基于遥感数据获取该量的方法多采用线性拟合或直接应用遥感反演的温度近似代替干球温度,但是由于下垫面复杂,导致误差较大。本文提出用支持向量机(SVM)模型进行干球温度推算。选择广西省南宁市为研究区域,首先通过遥感反演温度与气象实测温度的对比,证明了利用遥感数据推算干球温度的可能性。然后,构建了针对干球温度的SVM推算模型。最后,尝试了分别使用表观亮温和遥感反演地温作为SVM模型的输入进行干球温度的推算。结果表明,SVM模型推算的干球温度与实测值更为接近,和传统方法相比,精度得到明显提高;且用表观亮温进行推算更为简单,更适合业务化的应用。     

非线性模型精度评定的Bootstrap方法及其加权采样改进方法

本文将Bootstrap方法引入非线性模型精度评定理论中,通过对原始样本值或因变量的残差向量进行重采样,以获取自助样本的方式代替复杂的求导运算,给出了Bootstrap方法解决非线性精度评定问题的完整算法.针对Bootstrap方法中对模型随机项的等概率采样,通过获取采样过程中随机变量的经验分布函数,提出了加权采样策略,并分别给出了将改进方法用于非线性模型精度评定中的详细计算步骤.通过案例研究分析表明:重采样观测值的Bootstrap方法和重采样残差的Bootstrap方法能够得到比近似函数法、Jackknife法更为合理的参数标准差,具有更强的适用性;而加权采样的重采样观测值Bootstrap方法和加权采样的重采样残差Bootstrap方法能够获取更加精确的精度信息且更具优势,从而验证了将Bootstrap方法用于非线性精度评定及本文改进算法的可行性和有效性.     

全对称系统的变焦理论及其应用

本文主要研究全对称系统的变倍比、理想光学公式和缩放比的计算方法等理论问题。全对称半部为"-、+、-"排列方式的三片式结构,变倍组采用物象交换原则。本理论可实际应用于β=-1全对称型复消色差制版物镜的变距焦设计。文章着重讨论其光焦度分配方案,并给出了一个可同时满足平象场和复消色、长短焦距端值为600和450毫米的二级变焦距复消色差制版物镜光焦度分配的数字例子。     

半参数模型估计的补偿最小二乘法与正则核方法

本文根据测量数据处理中利用半参数对系统误差去除的效果,提出了半参数补偿最小二乘法和正则核方法两种方法进行比较.两种去除系统误差的方法都存在着局限性,应该根据系统误差的具体情况,选择不同的方法;最后通过模拟算例去除系统误差,并与实测数据进行拟合验证两种方法的可行性.     

加权混合估计中权值的确定方法

综合了大地测量中各种异方差多源观测模型和联合平差方法,说明了混合估计方法可以用于测量数据融合,平衡附加信息和样本信息对参数估计的影响。通过求取权值使参数估计的协方差阵的迹最小的方法,给出了一个权的最优选择方法。本文扩展了已有的加权混合估计方法,使得新方法中的权不受验前单位权方差的限制,能有效应用于大地测量数据处理。