遥感影像时—空融合的“点”—“线”—“面”质量评价

遥感影像时—空融合可集成多源数据高空间分辨率和高时间分辨率互补优势,生成时间连续的高空间分辨率影像,在遥感影像的动态监测与时序分析等方面具有重要应用价值.然而,现有多数研究往往基于单一数据产品对时—空融合算法进行评价,而在实际生产应用中,需要验证算法在多种遥感产品数据的融合表现;此外,目前研究大多基于“单点时刻”进行评...     

地表温度日变化模型偏差系数解算的地表温度降尺度

地表温度LST（Land Surface Temperature）是全球气候变化研究的关键参数,遥感是获取全球和区域尺度地表温度的一种切实可行手段,但现有的单一传感器无法提供高时空分辨率的LST数据,限制了遥感地表温度数据的深入广泛应用。现有的降尺度方法难以生成无缝高时空分辨率的地表温度数据,且降尺度效果易受高空间分辨率LST数据缺失及有效时刻分布影响。本文提出了一种基于地表温度日变化模型DTC（Diurnal Temperature Cycle）偏差系数解算的地表温度降尺度方法,采用FY-4A、MODIS和Landsat 8的LST数据生成晴空及多云条件下逐小时100 m的无缝LST数据。方法主要包含4部分：（1）利用空值重建方法获取无缝的FY-4A的LST数据;（2）建立FY-4A LST数据的DTC模型;（3）采用时空融合模型对MODIS的LST数据进行空间降尺度;（4）解算DTC模型偏差系数,获取逐小时100 m分辨率的无缝LST数据。实验结果表明,本文提出的方法具有较高的降尺度精度,可获得晴空及多云条件下无缝高时空地表温度数据,且高空间分辨率的地表温度数据缺失和有效时刻分布对本文方法降尺度结果影响较小。     

遥感高时空融合方法的研究进展及应用现状

针对遥感图像的"时空矛盾",评述了当前解决这一问题最主要的方法即遥感时空信息融合的方法,包括基于变化模型的融合、基于重建模型的融合以及基于学习模型的融合。通过分析各个模型的研究现状,指出了每种模型方法的优劣,特别重点介绍了影响较大的自适应时空融合方法的理论以及对其的改进算法。同时本文总结了当前时空融合模型在长时间序列模拟以及大区域数据集生成等方面的实际应用的效果,以及分析了影响时空融合结果的主要因素。最后基于这些问题和影响因素提出了今后时空融合模型发展的目标和方向。     

Fusion of multispectral and panchromatic Satellite images using the curvelet transform

A useful technique in various applications of remote sensing involves the fusion of different types of satellite images, namely multispectral (MS) satellite images with a high spectral and low spatial resolution and panchromatic (Pan) satellite image with a low spectral and high spatial resolution. Recent studies show that wavelet-based image fusion provides high-quality spectral content in fused images. However, the results of most wavelet-based methods of image fusion have a spatial resolution that is less than that obtained via the Brovey, intensity-hue-saturation, and principal components analysis methods of image fusion. We introduce an improved method of image fusion which is based on the amelioration de la resolution spatiale par injection de structures (ARSIS) concept using the curvelet transform, because the curvelet transform represents edges better than wavelets. Because edges are fundamental in image representation, enhancing the edges is an effective means of enhancing spatial resolution. Curvelet-based image fusion has been used to merge a Landsat Enhanced Thematic Mapper Plus Pan and MS image. The proposed method simultaneously provides richer information in the spatial and spectral domains.     

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.     

Downscaling Landsat-8 land surface temperature maps in diverse urban landscapes using multivariate adaptive regression splines and very high resolution auxiliary data

ABSTRACT

We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30?m) resolution down to 2–4?m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83?K prior to and 0.76–1.21?K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.     

单样本对卷积神经网络遥感图像时空融合

遥感图像时空融合是一种生成兼具高时空分辨率的合成遥感数据的技术。近年来,产生了一些基于卷积神经网络的时空融合方法。这些方法效果良好,但需要较多的图像样本对训练模型,限制了它们的应用。针对此问题,本文提出了一种单样本对卷积神经网络时空融合方法（SS-CNN）。该方法以高空间分辨率图像的波段平均图像提供的空间信息激励卷积神经网络建立高、低空间分辨率图像间的超分关系,进而利用该超分关系映射求解目标高空间分辨率图像。在实验中使用两个模拟数据集和一个真实数据集对该方法进行了测试,并与两种常用的时空融合方法做了比较。实验结果表明,SS-CNN在单样本对训练的情况下,可以较好地预测地物的物候变化和类型的变化,且在异质性高、地块破碎的区域表现良好。其不足之处在于会在地物边界上会造成轻微的模糊,将来需针对此问题做进一步改进。     

Fusion of VNIR and thermal infrared remote sensing data based on GA-SOFM neural network

The multi-source data fusion methods are rarely involved in VNIR and thermal infrared remote sensing at present. Therefore, the potential advantages of the two kinds of data have not yet been adequately tapped, which results in low calculation precision of parameters related with land surface temperature. A new fusion method is put forward where the characteristics of the high spatial resolution of VNIR (visible and near infrared) data and the high temporal resolution of thermal infrared data are fully explored in this paper. Non-linear fusion is implemented to obtain the land surface temperature in high spatial resolution and the high temporal resolution between the land surface parameters estimated from VNIR data and the thermal infrared data by means of GA-SOFM (genetic algorithms & self-organizing feature maps)-ANN (artificial neural network). Finally, the method is verified by ASTER satellite data. The result shows that the method is simple and convenient and can rapidly capture land surface temperature distribution of higher resolution with high precision.     

耦合MOD16和SMAP的微波土壤湿度降尺度研究

局域尺度上的水文或农业应用亟需较高空间分辨率的土壤湿度(SM)数据,微波土壤湿度空间降尺度是实现这一需求的重要途径.其中“光学/热红外与微波数据融合”的降尺度方法展现出了较大的应用潜力,然而这类方法依赖于遥感地表温度LST (Land Surface Temperature)或由LST分解得到的SM指数,受限于LST“...     

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.     

Estimating land-surface temperature under clouds using MSG/SEVIRI observations

The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e. MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Contrasting to the neighboring pixel approach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for earth surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of −3.68 K and a RMSE of 5.55 K for the validation site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ‘neighboring pixel’ scheme itself. An error propagation model applied for the proposed temporal neighboring-pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it is promising to reconstruct diurnal LST cycles from geo-stationary satellite observations.     

AMSR-E地表温度数据重建深度学习方法

地表温度对于全球气候变化等研究具有重要意义。被动微波遥感传感器AMSR-E（Advanced Microwave Scanning Radiometer for EOS）可以获得全天候地表温度,可作为多云条件下热红外地表温度数据的补充;但轨道扫描间隙限制了该数据在全球或区域尺度上的实际应用。鉴于地表温度的高时空异质性和AMSR-E LST轨道间隙数据的特点,本文提出了一种多时相特征连接卷积神经网络地表温度双向重建模型（MTFC-CNN）,利用深度学习在处理复杂非线性问题上的优势,重建轨道间隙区域的地表温度值。将2010年中国大陆四季的AMSR-E LST数据（数据未含港澳台区域）,分为白天和夜晚,形成共8个数据子集进行实验。在模拟实验中,重建结果与原始反演地表温度值平均均方根误差在1.0 K左右,决定系数R²在0.88以上,优于传统的样条空间插值和时间线性回归方法;真实实验结果具有较好的目视效果,且与对应MODIS LST产品对比发现,重建区LST值和未重建区LST值与MODIS LST产品间具有相近的平均均方根误差和决定系数。因此,本文提出的MTFC-CNN方法能有效重建AMSR-E LST轨道间隙数据,且优于传统方法。     

光谱指数趋势面的城市地表温度降尺度转换

针对城市及周边区域建造区和自然地表交织分布的特点,探讨了利用归一化植被指数(NDVI)和归一化建造指数(NDBI)构造趋势面的地表温度(LST)降尺度方法,以北京市市区及周边较平坦区域为例实现了LST自960 m向120 m的降尺度转换。分析了LST空间分布特征及NDVI、NDBI对地物的指示性特征;以北京市四至六环为界分析NDVI、NDBI趋势面对地表温度的拟合程度及各自的适用区域;在120 m、240 m、480 m和960 m 4个尺度上评价了NDVI、NDBI和NDVI+NDBI趋势面对LST的拟合程度和趋势面转换函数的尺度效应;对NDVI、NDBI和NDVI NDBI等3种方法的降尺度结果分覆盖类型、分区域对比评价。实验结果表明结合两种光谱指数的NDVI NDBI方法降尺度转换精度有所改善,改善程度取决于地表覆盖类型组合。     

城市地表温度影像时空融合方法研究

地表温度（land surface temperature,LST）是反映地表能量和水平衡物理过程的一个重要参数,受限于载荷量的限制以及传感器的技术瓶颈,当前的卫星平台均难以获取同时具有较高空间和时间分辨率的遥感地表温度影像,客观上影响了遥感地表温度影像的应用。针对地表异质性较高的城市区域,选取覆盖武汉城区的中分辨率成像光谱仪（Moderate-Resolution Imaging Spectroradiometer,MODIS）和增强型专题绘图仪（Enhanced Thematic Mapper Plus,ETM+）数据,结合时空反射率融合模型（enhanced spatial and temporal adaptive reflectance fusion model,ESTARFM）和非线性辐射温度分解算法（non-linear disaggregation procedure for radiometric surface temperature,NL-DisTrad）对地表温度影像进行时空融合研究,最终生成60 m空间分辨率的逐日地表温度融合影像。将融合影像与2002-07-09和2002-10-13的ETM+实际地表温度影像进行融合精度验证分析,其决定系数R2分别为0.80和0.86,均方根误差（root mean square error,RMSE）分别为2.65 K和1.78 K。实验结果表明,所提出的地表温度时空融合模型在城市区域的地表温度时空融合应用中具有潜在的应用前景。     

风云静止卫星地表温度产品空值数据稳健修复

静止卫星地表温度数据是研究昼夜气候和环境变化的重要参数。但现有发布的静止卫星地表温度数据由于受到云等大气因素的影响,往往出现数值缺失现象。针对该问题,提出基于昼夜变化模型的风云静止卫星地表温度空值数据的稳健修复方法。由多项式、傅里叶函数和高斯函数构建新的昼夜变化模型,并利用LevenbergMarquardt算法进行模型参数的求解与优化,进而实现空值修复。以风云2号F星数据(FY-2F)为例,模拟不同类型的像元缺失情况进行修复,并将不同模型修复结果与真实温度值比较,同时也对真实数据进行了测试。结果表明:本文提出的修复方法能有效对温度空值数据修复,且优于传统方法。     

多源卫星遥感影像时空融合研究的现状及展望

高空间分辨率的地表或者大气环境动态监测需要高时间-空间分辨率的卫星遥感影像作为数据支撑,但由于卫星传感器硬件技术及卫星发射成本等客观因素的限制,使得获取高时空分辨率遥感影像的较为便捷高效、低成本的可行手段就是将分别具有高时间和高空间分辨率的多源遥感影像进行时空融合,从而生成不同研究和应用所需的高时空分辨率卫星影像。现阶段,虽然国内外的学者进行了大量的时空融合算法研究,但是这些研究都局限于特定的数据类型、算法原理、应用目的等客观限制,而且其发展呈现出多样性。本文对现有主流的时空融合算法研究进行了归纳总结,将其分为4种:(1)基于地物组分的时空融合;(2)基于地表空间信息的时空融合;(3)基于地物时相变化的时空融合;(4)组合性的时空融合。同时,本文还对时空融合算法中存在的问题和面临的挑战进行了分析,并对其未来的发展方向进行了前瞻性的展望。     

Spatio‐temporal prediction of land surface temperature using semantic kriging

Spatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemK_NSep) and separable ST‐SemK (ST‐SemK_Sep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods.     

ETWatch中不同尺度蒸散融合方法

高分辨率遥感蒸散数据集的构建受到数据源的限制和云的影响,单一传感器无法达到高时空分辨率覆盖。本文分 析了ETWatch不同尺度遥感蒸散结果的空间特征,通过几种融合方法的比较,分析数据融合前后的数据特征和信息量,将 时空适应性反射率融合模型（STARFM）集成到ETWatch,用于不同尺度遥感蒸散数据的融合,该方法可以很好的结合高 低分辨率数据的空间分布和时间分布信息,在时间上保留了高时间分辨率数据的时间变化趋势,空间上又反映了高空间分 辨率数据的空间细节差异,STARFM融合后的日ET数据与融合前1 km 日ET数据的平均相对误差为1.75%,融合后的月ET 数据与融合前1 km 月ET数据的平均相对误差为0.2%,STARFM适合于不同尺度下遥感ET数据的融合。     

卫星遥感地表温度降尺度的光谱归一化指数法

针对卫星遥感技术监测地表温度(land surface temperature,LST)存在时空分辨率矛盾这一难题,以TsHARP温度降尺度算法为基础,根据地表覆盖类型的不同,分别选择与LST相关性更好的光谱指数(归一化植被指数,NDVI;归一化建造指数,NDBI;改进的归一化水体指数,MNDWI;增强型裸土指数,EBSI)提出了新的转换模型,并从定性和定量两个角度评价了TsHARP法和新模型的降尺度精度。结果表明:两种模型在提高LST空间分辨率的同时又能较好地保持MODIS LST影像热特征的空间分布格局,消除了原始1km影像中的马赛克效应,两种模型均能够达到较好的降尺度效果;全局尺度分析表明,不管是在降尺度结果的空间变异性还是精度方面,本文提出的模型(RMSE:1.635℃)均要优于TsHARP法(RMSE:2.736℃);TsHARP法在水体、裸地和建筑用地这些低植被覆盖区表现出较差的降尺度结果,尤其对于裸地和建筑用地更为明显(|MBE|3℃),新模型提高了低植被覆盖区地物的降尺度精度;不同季节的降尺度结果表明,两种模型都是夏、秋季的降尺度结果优于春、冬季,新模型的降尺度结果四季均好于TsHARP法,其中春、冬季的降尺度精度提升效果要优于夏、秋季。     

多源高分辨率遥感影像智能融合

本文面向多源高分辨率遥感影像自动化融合的应用需求,探索按需应用的智能化融合方法,充分利用不同分辨率和不同时相的高分辨率多源遥感影像数据资源与特性,研究了影像融合数据源选取的决策树算法,建立了遥感影像融合规则知识库,并自动化选取适合的融合算法,提出了Curvelet_HCS算法,对低频和高频系数选用不同的融合规则,改善了HCS算法的光谱失真问题,可同时融合多光谱影像的多个谱段,并保持更丰富的空间细节信息。根据融合评价结果对遥感影像融合规则知识库进行更新,实验验证表明了该套方法的有效性,为开展大规模智能化的多源遥感影像融合应用提供了重要的方法和技术支撑。