Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI

Retrieval of land-surface temperature (LST) using data from the METEOSAT Second Generation-1 (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) requires adequate estimates of land-surface emissivity (LSE). In this context, LSE maps for SEVIRI channels IR3.9, IR8.7, IR10.8, and IR12.0 were developed based on the vegetation cover method. A broadband LSE map (3-14 /spl mu/m) was also developed for estimating longwave surface fluxes that may prove to be useful in both energy balance and climate modeling studies. LSE is estimated from conventional static land-cover classifications, LSE spectral data for each land cover, and fractional vegetation cover (FVC) information. Both International Geosphere-Biosphere Program (IGBP) Data and Information System (DIS) and Moderate Resolution Imaging Spectrometer (MODIS) MOD12Q1 land-cover products were used to build the LSE maps. Data on LSE were obtained from the Johns Hopkins University and Jet Propulsion Laboratory spectral libraries included in the Advanced Spaceborne Thermal Emission and Reflection Radiometer spectral library, as well as from the MODIS University of California-Santa Barbara spectral library. FVC data for each pixel were derived based on the normalized differential vegetation index. Depending on land cover, the LSE errors for channels IR3.9 and IR8.7 spatially vary from /spl plusmn/0.6% to /spl plusmn/24% and /spl plusmn/0.1% to /spl plusmn/33%, respectively, whereas the broadband spectrum errors lie between /spl plusmn/0.3% and /spl plusmn/7%. In the case of channels IR10.8 and IR12.0, 73% of the land surfaces within the MSG disk present relative errors less than /spl plusmn/1.5%, and almost all (26%) of the remaining areas have relative errors of /spl plusmn/2.0%. Developed LSE maps provide a first estimate of the ranges of LSE in SEVIRI channels for each surface type, and obtained results may be used to assess the sensitivity of algorithms where an a priori knowledge of LSE is required.     

Average areal water equivalent of snow in a mountain basin using microwave and visible satellite data

Satellite microwave and millimeter-wave data have been used to evaluate the average areal water equivalent of snow cover in the mountainous Rio Grande basin in Colorado. Areal water equivalent data for the basin were obtained from contoured values of point measurements and from elevation-zone water volume values generated by a reliable snowmelt-runoff model using data on visible snow-cover extent. A significant relationship between the difference in brightness temperature at two different frequencies (37 and 18 GHz) and a basin-wide average snow-water equivalent value was obtained. This relationship and microwave observations were used to estimate the average areal water equivalent of the snow cover.<>     

An approach to feature selection and classification of remotesensing images based on the Bayes rule for minimum cost

Classification of remote-sensing images is usually carried out by using approaches aimed at minimizing the overall error affecting land-cover maps. However, in several remote-sensing problems, it could be useful to perform classification by taking into account the different consequences (and hence the different costs) associated with each kind of error. This allows one to obtain land-cover maps in which the total classification cost involved by errors is minimized, instead of the overall classification error. To this end an approach to feature selection and classification of remote-sensing images based on the Bayes rule for minimum cost (BRMC) is proposed. In particular a feature-selection criterion function is presented that permits one to select the features to be given as input to a classifier by taking into account the different cost associated with each confused pair of land-cover classes. Moreover, a classification technique based on the BRMC and implemented by using a neural network is described. The results of experiments carried out on a multisource data set concerning the Island of Elba (Italy) point out the ability of the proposed minimum cost approach to produce land-cover maps in which the consequences of each kind of error are considered     

Retrieval of the Water Equivalent of Snow Cover in Finland by Satellite Microwave Radiometry

The use of satellite microwave radiometry to retrieve the water equivalent of seasonal snow cover in Finland was investigated. Data from the Scanning Multichannel Microwave Radiometer (SMMR) on board Nimbus-7 were employed to examine the feasibility of several water equivalent algorithms. The satellite data set covers the winters of 1978-1979 through 1981-1982. The ground truth consists of snow water equivalent maps of Finland, compiled by the National Board of Waters. The microwave response to the water equivalent of dry snow cover was observed to depend substantially on frequency. Only algorithms including the 37-GHz channel gave adequate agreement with the manually measured water equivalent values. The algorithm involving the brightness temperature difference between 18 and 37 GHz, vertical polarization, provided the highest overall linear correlation coefficient. Two distinct categories were observed in the microwave behavior of snow-covered terrain for dry snow conditions: 1) early and mid-winter (usually about three months in Finland), and 2) late winter (a few weeks, starts after several melt-freeze cycles). Short-term variations in the microwave response to snow water equivalent can be related to variations in the surface structure and, to some degree, the temperature of the snow cover. Especially for small snow depths, the microwave response is also affected by the temperature of the underlying ground. Annual variations were observed to correlate, in addition to snow parameters, with the water content and state (frozen or thawed) of the underlying ground. The microwave response to snow water equivalent was found to depend substantially on the land-cover category.     

Inversion of the Li-Strahler canopy reflectance model for mappingforest structure

As part of a larger forest vegetation mapping process based on Landsat TM and digital terrain data, inversion of the Li-Strahler model provides estimates of tree size and cover for conifer stands. The vegetation maps are intended for use in natural resource management by the US Forest Service. Analysis of extensive field data in the form of “test” stands from four National Forests indicate the following about the Li-Strahler model: (1) the underlying assumptions of independence between tree size and crown shape are valid, (2) the means for tree geometry parameters vary between forest types, (3) estimates of forest cover are reliable, and (4) estimates of tree size are unreliable due to the breakdown in the relationship between image intra-stand variance and tree size. Improvements in estimates of tree size will require additional data beyond a single Landsat TM image, with multidirectional data a promising possibility     

基于多字典稀疏表示的遥感图像亚像元映射

本文提出了一种基于多字典稀疏表示的亚像元映射算法,利用已知的同类型高空间分辨率地物分布图像,构建能够更好反映不同类别地物空间分布模式的多个字典,将待分类亚像元用每一类字典稀疏表示,并依据重构误差最小化原则以及光谱失真程度约束条件来划分亚像元的地物类别.模拟与真实数据上的实验结果表明,本文算法能有效应对地物空间分布模式的多样性,具有更高的亚像元映射精度和更好的算法鲁棒性.     

Spatial assessment of two widely used land-cover datasets over the continental U.S

Satellite-based land-cover datasets are required for various environmental studies. Two of the most widely used land-cover datasets for the U.S. are the National Land-Cover Data (NLCD) at 30-m resolution and the Global Land-Cover Characteristics (GLCC) at 1-km nominal resolution. Both datasets were produced around 1992 and were expected to contribute similar land-cover information. This study investigated the NLCD distribution within each of 11 GLCC classes at 1-km unit in ten U.S. states. Our analyses showed that the NLCD had similar spatial distribution as the GLCC for the classes of grassland, shrubland, as well as deciduous and evergreen forests. Meanwhile, the GLCC class of cropland and pasture was highly correlated to the NLCD classes of row crops and pasture/hay. The GLCC savanna was appropriately related to the NLCD grassland, pasture/hay, and deciduous forest. The NLCD classes of row crops, pasture, and deciduous forest mainly dominated the GLCC class of cropland/woodland mosaic. Spatial similarity was lower for the GLCC classes of mixed forest, wooded wetland, and cropland/grassland mosaic. In addition to the NLCD urban areas, the GLCC urban and built-up lands were consistently related to the NLCD vegetated areas due to the common mixture of urban and vegetated lands. A set of subclass land-cover information provided through this study is valuable to understand the degrees of spatial similarity for the global vegetated and urban-related classes in selected study areas. The results of this study provide great reference for interchanging less-detailed global land-cover datasets for detailed NLCD to support environmental studies.     

Spatially complete global spectral surface albedos: value-added datasets derived from Terra MODIS land products

Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo, and direct-beam directional hemispherical (black-sky) albedo from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the National Aeronautics and Space Administration's Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which curtails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, climate models, and global change research projects. An ecosystem-dependent temporal interpolation technique is described that has been developed to fill missing or seasonally snow-covered data in the official MOD43B3 albedo product. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data. The resulting snow-free value-added products provide the scientific community with spatially and temporally complete global white- and black-sky surface albedo maps and statistics. These products are stored on 1-min and coarser resolution equal-angle grids and are computed for the first seven MODIS wavelengths, ranging from 0.47-2.1 /spl mu/m and for three broadband wavelengths 0.3-0.7, 0.3-5.0, and 0.7-5.0 /spl mu/m.     

Snow-Cover Parameters Retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Data

The Nimbus-7 satellite launched on October 24, 1978, carries a multifrequency, dual-polarized microwave imager. The instrument is designed to sense the ocean surface, the atmosphere, and land surfaces remotely. From previous ground-based and satellite-based microwave experiments, it is well known, that snow cover over land has a very distinct effect on the microwave signatures of the earth surface. It was the goal of this study to show that the three snow-cover parameters: extent, snow water equivalent, and onset of snow melt can be determined using scanning multichannel microwave radiometer (SMMR) data. Our analysis has shown, that the three snow parameters mentioned above are retrievable with sufficient accuracy to be of great value in climatology, meteorology, and hydrology. Snow extent is determined for dry snow cover with depth ?5 cm, snow water equivalent can be determined on a regional basis with ?2 g/cm2 rms accuracy, and the onset of snow melt is clearly visible by the detection of melt and refreeze cycles prior to snow runoff. The algorithms derived are simple enough to be incorporated in fully automated operational data analysis schemes.     

多偏移遥感图像的BP神经网络亚像元定位

提出了一种借助多偏移遥感图像来改进基于BP神经网络(BPNN)的亚像元定位新方法.不同于原BPNN方法使用单幅低空间分辨率观测图像,新方法利用多幅带有亚像元偏移的低空间分辨图像来确定亚像元属于各类的概率,然后根据概率值和地物覆盖比例确定亚像元类别,以降低BPNN定位模型中的不确定性和误差.实验表明,提出方法在视觉和定量评价上,均能获得更高精度的亚像元定位结果,验证了提出方法的有效性.     

新疆北疆地区雪水当量遥感定量研究

为了探讨光学遥感数据反演雪水当量可行性,2009和2010年2月对新疆北疆地区雪密度等参数进行了野外观测.运用连续统去除法对不同类型积雪光谱吸收特征进行分析得出1028nm、1252nm、1494nm和1940nm附近雪深对光谱吸收深度影响显著,积雪深度越大,光谱吸收深度越小.以500m 中分辨率成像光谱仪MODIS影...     

Super-resolution target identification from remotely sensed imagesusing a Hopfield neural network

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded     

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.     

Mapping vegetation cover change using geostatistical methods and bitemporal Landsat TM images

Accurately mapping change in vegetation cover is difficult due to the need for permanent plots to collect field data of the change; errors from georeference, coregistration, and data analysis; a small coefficient of correlation between remote sensing and field data; and limitations of existing methods. In this study, four cosimulation procedures, two collocated cokriging procedures, and two regression procedures were compared. The results showed that with the same cosimulation or collocated cokriging methods, two postestimation procedures led to more accurate estimates than the corresponding two preestimation procedures. Among three postestimation procedures with the same image data, cosimulation resulted in the most accurate estimates and reliable variances, then regression modeling and collocated cokriging. Thus, cosimulation algorithms can be recommended for this purpose. Moreover, the accuracy by a joint cosimulation procedure of 1989 and 1992 vegetation cover was similar to that by a separate cosimulation procedure; however, the joint cosimulation overestimated the average change. In addition, adding more Thematic Mapper images increased the accuracy of mapping for the cosimulation procedures, and the increase was slight for the regression procedures.     

Snow mapping in alpine regions with synthetic aperture radar

Active microwave sensors can discriminate snow from other surfaces in all weather conditions, and their spatial resolution is compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR in the Otztal Alps in 1989 and 1991, the authors examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. By comparing polarimetric SAR data to images from the Landsat Thematic Mapper obtained under clear conditions one week after the SAR flight, the authors found that SAR data at 5.3 GHz (C-band) can discriminate between areas covered by snow from those that are ice-free. However, they are less suited to discrimination of glacier ice from snow and rock. The overall pixel-by-pixel accuracies-74% from VV polarization alone with topographic information, 76% from polarimetric SAR without any topographic information, and 79% from polarimetric SAR with topographic information-are high enough to justify the use of SAR as the data source in areas that are too cloud-covered to obtain data from the Thematic Mapper. This is especially true for snow discrimination, where accuracies exceed 80%, because mapping of a transient snow cover during a cloudy melt season is often difficult with an optical sensor. The AIRSAR survey was carried out in summer during a heavy rainstorm, when the snow surfaces were unusually rough. Even better results for snow discrimination can be expected for mapping in the spring, when snow is usually smoother     

Superresolution mapping using a hopfield neural network with fused images

Superresolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by soft-classification methods. In addition to the information from the land cover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. The proposed method in this research aims to use fused imagery as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). Forward and inverse models were incorporated in the HNN to support a new reflectance constraint added to the energy function. The value of the function was calculated based on a linear mixture model. In addition, a new model was used to calculate the local endmember spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest that fine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.     

Toward Snowmelt Runoff Forecast Based on Multisensor Remote-Sensing Informnation

Snow-cover mapping is a prerequisite for deriving the main input variable for a deterministic snowmelt runoff model (SRM). Operational forecasting of snowmelt runoff has to rely on "guaranteed snow cover information" for a specific area and time. Remote sensing can provide the necessary data, especially when different sensor systems are available for combined interpretations. It has been shown already how remote-sensing information from Landsat-MSS and NOAA/AVHRR supplement each other. It is important, though, that when using coarse spatial sensor resolution, special attention to the area, its topography, and the amount of snow coverage must be taken.     

A prototype AMSR-E global snow area and snow depth algorithm

A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.     

Venous catheter based mapping of ectopic epicardial activation: training data set selection for statistical estimation

A source of error in most of the existing catheter cardiac mapping approaches is that they are not capable of acquiring epicardial potentials even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this subgroup of patients, mapping techniques that are limited to the endocardium result in localization errors and failure in subsequent ablation procedures. In addition, catheter-based electrophysiological studies of the epicardium are limited to regions near the coronary vessels or require transthoracic access. We have developed a statistical approach by which to estimate high-resolution maps of epicardial activation from very low-resolution multi-electrode venous catheter measurements. A training set of previously recorded maps is necessary for this technique so that composition of the database becomes an important determinant of accuracy. The specific hypothesis of the study was that estimation accuracy would be best when the training data set matches that of the test beat(s), whereby the matching was according to the site of initiation of the beats. This hypothesis suggests approaches to optimized selection of the training set, three of which we have developed and evaluated. One of these methods, the high-CC refinement method, was able to estimate the earliest activation site of left ventricularly paced maps within an average of 4.67 mm of the true site; in 89% of the cases (a total of 231 cases) the error was smaller than 10 mm. In another method, MHC-Spatial activation, right ventricularly paced maps (239 maps) were estimated with an error of 7.15 mm. The average correlation coefficient between the original and the estimated maps was also very high (0.97), which shows the ability of the training data set refinement methods to estimate the epicardial activation sequence. The results of these tests support the hypothesis and, moreover, suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting.     

基于多点信息统计法的土地覆盖图像超分辨率重建方法

在利用遥感技术对土地覆盖情况制图的过程中,超分辨率重建被广泛采用.包含土地覆盖图像特征的先验模型可以减少重建过程中的不确定性.多点信息统计法可以从先验模型提取其本质特征,然后将特征复制到待模拟区域.然而传统基于线性降维的多点信息统计法不能有效处理非线性数据,因此将等距特征映射引入到多点信息统计法以实现非线性数据的降维,再对降维后的数据作分类处理.比较当前数据事件和各个分类的均值,从与当前数据事件最接近的类中提取模式.同时利用低精度原始图像作为软数据以重建较高精度的土地覆盖图像.实验表明重建的超分辨率土地覆盖图像具有与参照图像相似的结构特征.