Fully constrained target fraction estimation from noise-reduced hyperspectral images

Estimating abundance fractions of materials in hyperspectral images is a problem often studied in remote sensing. It can be used in reconnaissance and surveillance applications. The major difficulty of fraction estimation in hyperspectral images is due to the fact that the sampling distance is generally larger than the size of the targets of interest. Under this circumstance, estimation should be carried out at the sub-pixel level. In a linear mixture model, the spectrum of a mixed pixel is represented as a linear combination of the endmember spectra present in the pixel area weighted by fractional area coverage. Accurate fraction estimation requires two constraints (SC) imposed on abundance fractions: the abundance sum-to-one constraint and abundance non-negativity constraint (NC). In this article, we present a new fully constrained least squares computational method to estimate abundance fractions. Another contribution of this article is that the estimation is, unlike many other proposed methods, performed on noise-reduced hyperspectral images instead of original images. Experiments using synthetic data and Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data demonstrate that this fully constrained estimation outperforms the unconstrained and partially constrained least squares methods and that noise reduction can considerably improve the capability of our approach when the noise intensity rises.     

多尺度卫星雪盖面积获取的对比研究

系统地开展尺度和尺度效应的研究,综合利用日益增多的不同分辨率的遥感影像数据,是地球空间信息科学发展的趋势之一。作为多尺度转换大命题中的前期工作,旨在通过试验的手段检验不同尺度产品的真实性,发现多尺度转换中潜在的各种问题,以及探索可行性的尺度转换方法,为进一步的多尺度转换研究工作提供良好的背景知识。多尺度雪盖面积的获取,包括两样区1 m分辨率野外人工子像元雪盖、两样区30 m分辨率子像元重采样雪盖、30 m Hyperion和TM卫星反演雪盖、以及MODIS 500 m分辨率的MOD10A1雪盖日产品。通过对上述不同尺度获取的雪盖面积的相互对比研究,我们发现：①1 m样区的雪盖>30 m重采样雪盖>30 m Hyperion和TM的雪盖 ;②若把1 m样区看做500 m像元的单点试验,该单点不能完全正确地表征同位置像元上的地物特征 ;③MOD10A1产品有云覆盖地区,宜采用前后雪盖合成的方法来辅助判断并恢复当日云层下的地表类型。同时,通过对各像元级尺度的雪盖面积的真实性检验,我们也发现尺度转换需关注的潜在关键问题：①精确的像元匹配 ;②重采样方式 ;③数据获取时间以及产品时间序列 ;④多传感器图像处理 ;⑤产品算法的影响 ;⑥混合像元的影响 ;⑦试验样方的大小设计 ;⑧地面同步物理参数的测量 ;⑨空间异质性的定量表达。     

Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: Methodology and accuracy assessment

This study describes a comprehensive method to produce routinely regional maps of seasonal snow cover in the Southern Alps of New Zealand (upper Waitaki basin) on a subpixel basis, and with the MODerate Resolution Imaging Spectroradiometer (MODIS). The method uses an image fusion algorithm to produce snow maps at an improved 250 m spatial resolution in addition to the 500 m resolution snow maps. An iterative approach is used to correct imagery for both atmospheric and topographic effects using daily observations of atmospheric parameters. The computation of ground spectral reflectance enabled the use of image-independent end-members in a constrained linear unmixing technique to achieve a robust estimation of subpixel snow fractions. The accuracy of the snow maps and performance of the algorithm were assessed carefully using eight pairs of synchronic MODIS/ASTER images. ‘Pixel-based’ metrics showed that subpixel snow fractions were retrieved with a Mean Absolute Error of 6.8% at 250 m spatial resolution and 5.1% after aggregation at 500 m spatial resolution. In addition, a ‘feature-based’ metric showed that 90% of the snowlines were depicted generally within 300 m and 200 m of their correct position for the 500-m and 250-m spatial resolution snow maps, respectively. A dataset of 679 maps of subpixel snow fraction was produced for the period from February 2000 to May 2007. These repeated observations of the seasonal snow cover will benefit the ongoing effort to model snowmelt runoff in the region and to improve the estimation and management of water resources.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

基于最小噪声分离的约束能量最小化亚像元目标探测方法

提出了一种基于最小噪声分离的约束能量最小化亚像元目标探测方法。利用最小噪声分离变换,降低高光谱遥感影像的维数,同时分离高光谱遥感影像中的噪声。利用约束能量最小化方法对低维数据进行亚像元目标探测,避免了求解影像虚拟维数和病态矩阵求逆的问题。实验结果表明,该方法可以很好地抑制噪声的影响,亚像元目标探测率较高,是一种快速有效的高光谱遥感影像亚像元目标探测方法。     

Potential operational multi-satellite sensor mapping of snow cover in maritime sub-polar regions

Scottish snow cover as an example of a maritime sub-polar region has two principal problems for an operational monitoring programme: the often ephemeral nature of the snow cover, and the loss of direct access to snow imagery due to clouds. At present only the NOAA AVHRR series provides images with the required temporal and spatial resolutions. Based on the availability of data from the Dundee satellite data receiving station a range of NOAA-12, -14 and -15 day and night passes were collected and processed. Three snow cover products were produced from the NOAA AVHRR/2 data: snow area based on channel 134 ISODATA classifications, percentage snow cover based on multi-temporal Normalized Difference Vegetation Index (NDVI), and daily maximum snow surface temperature maps using split-window combinations of thermal channels. Noted improvements were evident in the accuracy and resolution of snow cover classifications based on provisional testing of AVHRR/3 data. Maximum snow surface temperature maps indicated a potential for mapping areas of snow melt. The principal limitation in the operational snow cover mapping with AVHRR, however, remains the loss of temporal resolution due to cloud cover.     

面向亚像元级积雪信息提取的MODIS影像波段融合方法研究

MODIS影像因其共享性和时间序列的完整性而成为大区域积雪监测研究广泛使用的数据源,进行MODIS影像波段间融合,能够为积雪研究提供较高分辨率的影像数据源。为了充分利用MODIS影像250 m分辨率波段的空间和光谱信息,提取亚像元级的积雪面积,使用两种具有高光谱保真度的影像融合方法：基于SFIM变换和基于小波变换的融合方法,采取不同的波段组合策略,对MODIS影像bands 1~2和bands 3~7进行融合,并以Landsat TM影像的积雪分类图作为“真值”,对融合后影像进行混合像元分解得到的积雪丰度图的精度进行评价。结果表明：利用基于SFIM变换和小波变换方法融合后影像提取的积雪分类图精度较高,数量精度为75%,比未融合影像积雪分类图的精度提高了6%,表明MODIS影像波段融合是一种提取高精度积雪信息的有效方法。     

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

SPOT‐4 VEGETATION multi‐temporal compositing for land cover change studies over tropical regions

Multi‐temporal compositing of SPOT‐4 VEGETATION imagery over tropical regions was tested to produce spatially coherent monthly composite images with reduced cloud contamination, for the year 2000. Monthly composite images generated from daily images (S1 product, 1‐km) encompassing different land cover types of the state of Mato Grosso, Brazil, were evaluated in terms of cloud contamination and spatial consistency. A new multi‐temporal compositing algorithm was tested which uses different criteria for vegetated and non‐vegetated or sparsely vegetated land cover types. Furthermore, a principal components transformation that rescales the noise in the image—Maximum Noise Fraction (MNF)—was applied to a multi‐temporal dataset of monthly composite images and tested as a method of additional signal‐to‐noise ratio improvement. The back‐transformed dataset using the first 12 MNF eigenimages yielded an accurate reconstruction of monthly composite images from the dry season (May to September) and enhanced spatial coherence from wet season images (October to April), as evaluated by the Moran's I index of spatial autocorrelation. This approach is useful for land cover change studies in the tropics, where it is difficult to obtain cloud‐free optical remote sensing imagery. In Mato Grosso, wet season composite images are important for monitoring agricultural crop cycles.     

Restoration of multichannel microwave radiometric images

A constrained iterative image restoration method is applied to multichannel diffraction-limited imagery. This method is based on the Gerchberg-Papoulis algorithm utilizing incomplete information and partial constraints. The procedure is described using the orthogonal projection operators which project onto two prescribed subspaces iteratively. Its properties and limitations are presented. The effect of noise was investigated and a better understanding of the performance of the algorithm with noisy data has been achieved. The restoration scheme with the selection of appropriate constraints was applied to a practical problem. The 6.6, 10.7, 18, and 21 GHz satellite images obtained by the scanning multichannel microwave radiometer (SMMR), each having different spatial resolution, were restored to a common, high resolution (that of the 37 GHz channels) to demonstrate the effectiveness of the method. Both simulated data and real data were used in this study. The restored multichannel images may be utilized to retrieve rainfall distributions.     

Operational snow mapping using multitemporal Meteosat SEVIRI imagery

The Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation is the first geostationary satellite instrument with all visual and infrared channels that are important for snow mapping. In this paper, we present an algorithm for deriving snow cover maps from SEVIRI data that makes use of the unique combination of adequate spectral resolution and very high frequency. The short interval of 15 min between images makes it possible to extend traditional spectral classification with a detection of changes between images. This improves the detection of clouds and cloud shadows in instantaneous images, because these often display more variation in time than the surface. It therefore allows a more accurate mapping of surface snow cover, as is shown by a validation of the results with ground observations and other satellite data. The accurate classification of each single image allows the generation of temporal composite snow maps in near real-time, which is for example of interest for numerical weather prediction models. When compared to many in situ measurements from the winter of 2005/2006, the accuracy of the algorithm is 95%.     

A combined high and low spatial resolution approach for mapping snow covered areas in the Atlas mountains

Dynamics of snow in semi‐arid mountains are poorly investigated despite the fact that snow may represent an important source of water for downstream populations especially in the spring and early summer. Data acquired by space‐borne optical sensors (i.e. reflectance and derived snow indices) may be suitable for spatial and temporal monitoring of snow cover. However, due to prevailing terrain and climatic conditions, the use of satellite sensor data to monitor snow dynamics is not trivial over such regions. Snowfall as well as precipitation are characterized by strong space–time variability. Indeed snow can fall and melt within one week. Under such conditions, appropriate monitoring of snow dynamics requires space instruments that provide data with high spatial and high temporal resolutions. In this context we developed a new approach based on the combination of two types of instruments: low spatial and high temporal resolution (Système Pour l'Observation de la Terre (SPOT)‐VEGETATION) and high spatial and low temporal resolution (Landsat Thematic Mapper). This new approach improves the relationship between snow index and snow area. The method is validated against snow maps derived from classification of high spatial resolution data on the Atlas range, in Morocco. It is then applied to a one‐year series of SPOT‐4 VEGETATION images allowing to derive a temporal snow cover profile at 1?km spatial resolution over the entire Atlas. The yearly snow profile obtained is of great interest for hydrological modelling.     

东北地区MODIS亚像元积雪覆盖率反演及验证

以中巴资源卫星数据作为地面“真值”影像,根据东北地区地理环境与气候特点对Salomoson亚像元积雪覆盖率模型参数进行修正,反演东北地区MODIS像元积雪覆盖率,并用不同方案对模型的稳定性和精度进行分析。研究结果表明,经修正后的Salomoson亚像元积雪覆盖率反演模型对不同地貌--景观单元具有稳定性,其中较小的波动源于积雪物理性质差异、大气效应、积雪影像分类误差及影像配准误差。在东北平原区,NDSI值在0.52~0.65时,模型反演精度高,但反演雪盖率总体偏低,主要是由NDSI基于对波段反射率的非线性转换引起的;雪盖率高估的像元主要分布在城区外围以及农村居民点,而覆盖城区、乡、镇以及居民点之间道路的像元雪盖率误差小,其原因是人类活动频率影响像元内积雪组分与非积雪组分的光谱特性的差异程度。与MODIS雪产品进行对比分析,积雪覆盖率提供较传统雪盖制图更加丰富的信息,然而对林区冠层下积雪覆盖二者均未给出准确估计。     

卫星遥感雪盖制图方法对比与分析

利用ＬａｎｄｓａｔＴＭ、ＮＯＡＡ／ＡＶＨＲＲ和中分辨率成像光谱仪（ＭＯＤＩＳ）三个平台传感器的遥感数据,分别使用训练样本监督分类、阈值数字信号统计、雪盖指数方法制作雪盖图和提取积雪面积。结果表明：不同传感器遥感图像因时相和时空分辨率的差异,提取积雪信息的有效方法有所不同。但基于反射特性的雪盖指数计算法具有普遍的实际操作性意义,即雪盖制图精度高,分类合理,是提取积雪信息的最佳技术手段;当使用监督积雪分类时,只有取得精确的信号文件,分类结果才是可信的;而阈值数字信号统计的雪的阈值确定具有很大的经验性和随机性,但对数据不完整或只有单波段时也不失为有效和简便的途径;山影补偿处理法基本可以消除地形阴影的影响;而去云后其覆盖下的积雪恢复技术值得进一步讨论。     

Mapping snow cover in the pan-Arctic zone,using multi-year (1998-2001) images from optical VEGETATION sensor

Timely information on spatial distribution and temporal dynamics of snow cover in the pan-Arctic zone is needed, as snow cover plays an important role in climate, hydrology and ecological processes. Here we report estimates of snow cover in the pan-Arctic zone (north of 45° N) at 1-km spatial resolution and at a 10-day temporal interval over the period of April 1998 to December 2001, using 10-day composite images of VEGETATION sensor onboard Système Pour l'Observation de la Terre (SPOT)-4 satellite. The results show that snow covered area (SCA) in North America (north of 45° N) increased from 1998 to 2001, while SCA in Eurasia (north of 45° N) decreased from 1998 to 2000 but increased in 2001. There were large spatial and temporal variations of snow cover in the pan-Arctic zone during 1998-2001.     

融合FY-3C号和FY-4A号卫星数据的积雪面积变化研究—以祁连山区为例

祁连山区积雪类型丰富、判识复杂,是中国积雪研究的典型区域。因此,精确地监测祁连山区积雪面积变化及其时空演变,对祁连山区生态环境和社会经济发展等具有重要意义。FY-3C MULSS利用多阈值积雪指数模型提供全球日积雪覆盖产品,FY-4A AGRI传感器每15~60 min提供一景覆盖全球的多光谱影像。基于FY-4A AGRI高时间分辨率的特征,构建适合于FY-4A号数据的动态多阈值多时相云隙间积雪识别方法,很大程度上减小了云对光学数据识别积雪造成的影响,并结合FY-3C MULSS积雪覆盖日产品较高空间分辨率的优势,融合得到去除云后的FY3C4积雪覆盖数据。利用Landsat 8 OLI卫星数据对融合后的积雪数据进行对比验证,结果表明融合FY-3C和FY-4A后的数据能更好地判识祁连山区的积雪覆盖情况。以MODIS MOD10A2积雪产品为真实值,随机检验了2018年3月~2019年3月融合后数据的积雪判识精度,发现无云情况下方法的总体精度可达到85.25%。进一步研究发现祁连山区积雪面积在海拔、气候和坡向等因素的影响下时空分布极不均匀,总体呈现出冬春季节大于夏秋季节,以及东部积雪面积大于西部积雪面积的特征。     

Development of a sub-pixel analysis method applied to dynamic monitoring of floods

Traditional ‘in situ’ measurement techniques often fail to record the spatial distribution of floodplains. In that case, remote sensing provides inexpensive and reliable methodologies to map flooded areas and compute flood damage. The identification and monitoring of floods, due to their highly dynamic nature, require the use of high-time-resolution satellite images with the drawback that such images usually have low to medium spatial resolution. In this context, the traditional classification techniques would not be suitable for delineating floods because they use ‘hard methods’ of classification, where the coarse pixel is assigned to a unique land cover class, generating inaccurate maps of the flooded area. In contrast, the ‘soft methods’ assign several land cover classes within the coarse pixels. In this article, the theoretical basis regarding an innovative methodology of sub-pixel analysis (SA) to identify flooded areas is developed. The improvement in flood delineation is achieved with the use of primary topographic attributes, which stem from a digital elevation model (DEM). The methodology was applied to the monitoring of flood events in the lower Senegal River Valley, using satellite images with moderate spatial resolution. The proposed methodology was demonstrated to be effective for mapping the flood extent: the correct mapping of flooded areas was about 80% in all considered regions, whilst the better performance of supervised classification was 53%.     

Evaluating MODIS soil fractional cover for arid regions,using albedo from high-spatial resolution satellite imagery

Broad-scale high-temporal frequency satellite imagery is increasingly used for environmental monitoring. While the normalized difference vegetation index (NDVI) is the most commonly used index to track changes in vegetation cover, newer spectral mixture approaches aim to quantify sub-pixel fractions of photosynthesizing vegetation, non-photosynthesizing vegetation, and exposed soil. Validation of the unmixing products is essential to enable confident use of the products for management and decision-making. The most frequently used validation method is by field data collection, but this is very time consuming and costly, in particular in remote regions where access is difficult.

This study developed and demonstrates an alternative method for quantifying land-cover fractions using high-spatial resolution satellite imagery. The research aimed to evaluate the bare soil fraction in a sub-pixel product, MODIS Fract-G, for the natural arid landscapes of the far west of South Australia. Twenty-two sample regions, of 3400 sampling points each, were investigated across several arid land types in the study area. Albedo thresholds were carefully determined in Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument Stereo Mapping (ALOS PRISM) images (2.5 m spatial resolution), which separated predominantly bare soil from predominantly vegetated or covered soil, and created classified images. Correlation analysis was carried out between MODIS Fract-G bare soil fractional cover and ALOS PRISM bare soil proportions for the same areas. Results showed much lower correlations than expected, though limited agreement was found in some specific areas. It is posited that the Moderate Resolution Imaging Spectroradiometer (MODIS) fractional cover product, which is based on unmixing using the NDVI and a cellulose absorption index (CAI) proxy, may be generally unable to separate soil from vegetation in situations where both indices are low. In addition, separation is hampered by the lack of ‘pure pixels’ in this heterogeneous landscape. This suggests that the MODIS fractional cover product, at least in its present form, is unsuited to monitor sparsely vegetated arid landscapes.     

Assimilation of meteorological and remote sensing data for snowmelt runoff forecasting

A versatile data assimilation scheme for remote sensing snow cover products and meteorological data was developed, aimed at operational use for short-term runoff forecasting. Spatial and temporal homogenisation of the various input data sets is carried out, including meteorological point measurements from stations, numerical weather predictions, and snow maps from satellites. The meteorological data are downscaled to match the scale of the snow products, derived from optical satellite images of MODIS and from radar images of Envisat ASAR. Snow maps from SAR and optical imagery reveal systematic differences which need to be compensated for use in snowmelt models. We applied a semi-distributed model to demonstrate the use of satellite snow cover data for short-term runoff forecasting. During the snowmelt periods 2005 and 2006 daily runoff forecasts were made for the drainage basin Ötztal (Austrian Alps) for time lags up to 6 days. Because satellite images were obtained intermittently, prognostic equations were applied to predict the daily snow cover extent for model update. Runoff forecasting uncertainty is estimated by using not only deterministic meteorological predictions as input, but also 51 ensemble predictions of the EPS system of the European Centre for Medium Range Weather Forecast. This is particularly important for water management tasks, because meteorological forecasts are the main error source for runoff prediction, as confirmed by simulation studies with modified input data from the various sources. Evaluation of the runoff forecasts reveals good agreement with the measurements, confirming the usefulness of the selected data processing and assimilation scheme for operational use.     

Remote sensing and land cover area estimation

This article gives an overview of different ways to use satellite images for land cover area estimation. Approaches are grouped into three categories. (1) Estimates coming essentially from remote sensing. Ground data, are used as an auxiliary tool, mainly as training data for image classification, or sub-pixel analysis. Area estimates from pixel counting are sometimes used without a solid statistical justification. (2) Methods, such as regression, calibration and small area estimators, combining exhaustive but inaccurate information (from satellite images) with accurate information on a sample (most often ground surveys). (3) Satellite images can support area frame surveys in several ways: to define sampling units, for stratification; as graphic documents for the ground survey, or for quality control.

Cost-efficiency is discussed. Operational use of remote sensing is easier now with cheaper Landsat Thematic Mapper images and computing, but many administrations are reluctant to integrate remote sensing in the production of area statistics.