Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors

There is considerable interest in using remote sensing to characterize the hydrologic behavior of the land surface on a routine basis. Information on moisture fluxes between the surface and lower atmosphere reveals linkages and land-atmosphere feedback mechanisms, aiding our understanding of energy and water balance cycles. Techniques that combine information on land and atmospheric properties with remotely sensed variables would allow improved prediction for a number of hydrological variables. Over the last few decades, there has been a focus on better determining evapotranspiration and its spatial variability, but for many regions routine prediction is not generally available at a spatial resolution appropriate to the underlying surface heterogeneity. Over agricultural regions, this is particularly critical, since the spatial extent of typical field scales is not regularly resolved within the pixel resolution of satellite sensors. Understanding the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes as observed by satellite sensors with different spatial resolutions is a critical research need. To attend this task, data from Landsat-ETM (60 m), ASTER (90 m), and MODIS (1020 m) satellite platforms are employed to independently estimate evapotranspiration. The range of the satellite sensor resolutions allows analyses that span scales from (point-scale) in-situ tower measurements to the MODIS kilometer-scale. Evapotranspiration estimates derived at these multiple resolutions were assessed against eddy covariance flux measurements collected during the 2002 Soil Moisture Atmospheric Coupling Experiment (SMACEX) over the Walnut Creek watershed in Iowa. Together, these data allow a comprehensive scale intercomparison of remotely sensed predictions, which include intercomparisons of the evapotranspiration products from the various sensors as well as a statistical analysis for the retrievals at the watershed scale. A high degree of consistency was observed between the retrievals from the higher-resolution satellite platforms (Landsat-ETM and ASTER). The MODIS-based estimates, while unable to discriminate the influence of land surface heterogeneity at the field scale, effectively reproduced the watershed average response, illustrating the utility of this sensor for regional-scale evapotranspiration estimation.     

The selection of natural scales in 2D images using adaptive Gaborfiltering

This paper analyzes how the natural scales of the shapes in 2D images can be extracted. Spatial information is analyzed by multiple units sensitive to both spatial and spatial-frequency variables. Scale estimates of the relevant shapes are constructed only from strongly responding detectors. The meaningful structures in the response of a detector (computed through 2D Gabor filtering) are, at their natural level of resolution, relatively sharp and have well-defined boundaries. A natural scale is so defined as a level producing local minimum of a function that returns the relative sharpness of the detector response filtered over a range of scales. In a second stage, to improve a first crude estimate of the local scale, the criterion is also rewritten to directly select scales at locations of significant features of each activated detector     

Evapotranspiration monitoring with Meteosat Second Generation satellites: improvement opportunities from moderate spatial resolution satellites for vegetation

The Satellite Application Facility on Land Surface Analysis proposes a land evapotranspiration (ET) product, generated in near-real time. It is produced by an energy balance model forced by radiation components derived from data of the Spinning Enhanced Visible and Infrared Imager aboard Meteosat Second Generation geostationary satellites, at a spatial resolution of approximately 3 km at the equator and covering Europe, Africa, and South America. In this article, we assess the improvement opportunities from moderate spatial resolution satellites for ET monitoring at the Meteosat Second Generation satellite scale. Four variables, namely the land cover, the leaf area index (LAI), the surface albedo, and the open water fraction, derived from moderate-resolution satellites for vegetation monitoring are considered at two spatial resolutions, 1 km and 330 m, corresponding to the imagery provided by Satellite Pour l’Observation de la Terre (SPOT)-VEGETATION and future Project for On-Board Autonomy – Vegetation (PROBA-V) space-borne sensors. The variables are incorporated into the ET model, replacing or complementing input derived from the sensor aboard the geostationary satellite, and their relative effect on the model output is analysed. The investigated processes at small scales unresolved by the geostationary satellite are better taken into account in the final ET estimates, especially over heterogeneous and transition zones. Variables derived from sensors at 250–300 m are shown to have a noticeable effect on the ET estimates compared to the 1 km resolution, demonstrating the interest of PROBA-V 330 m-derived variables for the monitoring of ET at Meteosat Second Generation resolution.     

Dryland observation at local and regional scale — Comparison of Landsat TM/ETM+ and NOAA AVHRR time series

During the last few decades, many regions have experienced major land use transformations, often driven by human activities. Assessing and evaluating these changes requires consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific characteristics associated with both observation scales. The aim of this study was to evaluate the potentials and limits of remote sensing time series for change analysis of drylands. We focussed on the assessment and monitoring of land change processes using two scales of remote sensing data. Special interest was given to the influence of the spatial and temporal resolution of different sensors on the derivation of enhanced vegetation related variables, such as trends in time and the shift of phenological cycles. Time series of Landsat TM/ETM+ and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a study area in the Mediterranean. The test site is located in Central Macedonia (Greece) and represents a typical heterogeneous Mediterranean landscape. It is undergoing extensification and intensification processes such as long-term, gradual processes driven by changing rangeland management and the extension of irrigated arable land. Time series analysis of NOAA AVHRRR and Landsat TM/ETM+ data showed that both sensors are able to detect this kind of land cover change in complementary ways. Thereby, the high temporal resolution of NOAA AVHRR data can partially compensate for the coarse spatial resolution because it allows enhanced time series methods like frequency analysis that provide complementary information. In contrast, the analysis of Landsat data was able to reveal changes at a fine spatial scale, which are associated with shifts in land management practice.     

Spatial-frequency multiplexed fiber-optic Fizeau strain sensor system with optical amplification

A novel method for multiplexing fiber-optic Fizeau strain sensors with optical amplification is proposed and demonstrated. This method overcomes the two intrinsic disadvantages of fiber-optic Fabry–Perot (F–P) strain sensors, i.e. weak signal and difficult multiplexing. The amplified spontaneous emission (ASE) and optical amplification are used simultaneously to enhance the interferometric signal considerably. A Fizeau interferometer formed by two fiber ends with a quite different reflectivity is used to replace the F–P cavity in sensor head design. Such a Fizeau cavity can enlarge the cavity length by at least an order of magnitude and allows more than 10 sensors to be multiplexed simultaneously by using spatial-frequency multiplexing. The operating principle of the sensor system is discussed and an experiment is carried out to verify the concept of the method proposed. It is anticipated that such a sensor system could find important applications for health monitoring of large structures.     

Laplacian pyramid networks: A new approach for multispectral pansharpening

Pansharpening is about fusing a high spatial resolution panchromatic image with a simultaneously acquired multispectral image with lower spatial resolution. In this paper, we propose a Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, aiming at getting a higher spatial resolution multispectral image. The proposed architecture considers three aspects. First, we use the Laplacian pyramid method whose blur kernels are designed according to the sensors’ modulation transfer functions to separate the images into multiple scales for fully exploiting the crucial spatial information at different spatial scales. Second, we develop a fusion convolutional neural network (FCNN) for each scale, combining them to form the final multi-scale network architecture. Specifically, we use recursive layers for the FCNN to share parameters across and within pyramid levels, thus significantly reducing the network parameters. Third, a total loss consisting of multiple across-scale loss functions is employed for training, yielding higher accuracy. Extensive experimental results based on quantitative and qualitative assessments exploiting benchmarking datasets demonstrate that the proposed architecture outperforms state-of-the-art pansharpening methods. Code is available at https://github.com/ChengJin-git/LPPN.     

异源、同源传感器影像融合的比较研究

遥感影像的融合是遥感界的一个研究热点。根据数据源的不同,影像融合可分为异源传感器影像融合和同源传感器影像融合。以TM与SPOT作为异源影像融合的例子,以IKONOS的MS与Pan作为同源影像融合的例子,用5种算法对两种融合类型进行实验与比较。结果表明,同源传感器影像的融合效果好于异源传感器影像的融合效果;不同的融合算法在异源和同源传感器影像融合中的表现不尽相同。SVR变换可同时应用于异源及同源传感器影像的融合,且在提高影像空间分辨率、信息量和清晰度的同时能很好地保持原始多光谱影像的光谱特征。SFIM虽然也可以在两种数据源的融合实验中获得较好的融合效果,但其高频信息融入度最差。MB虽然提高了融合影像的高频信息融入程度,但光谱保真度、信息量和清晰度却不理想。Ehlers适用于异源传感器影像间的融合,而WT则适用于同源传感器影像的融合。     

Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric

Mobile sensors cover more area over a fixed period of time than do the same number of stationary sensors. However, the quality of coverage (QoC) achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed, and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper, we consider the following event capture problem: the events of interest arrive at certain points in the sensor field and disappear according to known arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. We analyze how the QoC scales with velocity, path, and number of mobile sensors. We characterize cases where the deployment of mobile sensors has no advantage over static sensors, and find the optimal velocity pattern that a mobile sensor should adopt. We also present algorithms for two motion planning problems: 1) for a single sensor, what is the sensor trajectory and the minimum speed required to satisfy a bound on the event loss probability and 2) for sensors with fixed speed, what is the minimum number of sensors required to satisfy a bound on the event loss probability. When the robots are restricted to move along a line or a closed curve, our algorithms return the optimal velocity for the minimum velocity problem. For the minimum sensor problem, the number of sensors used is within a factor of 2 of the optimal solution. For the case where the events occur at arbitrary points on a plane, we present heuristic algorithms for the aforementioned motion planning problems and bound their performance with respect to the optimal.     

Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health

Research was conducted in a forest adjacent to an abandoned acid mine tailings site to assess forest structural health using high spatial and spectral resolution digital camera imagery. Conventional approaches to this problem involve the use image spectral information, basic spectral transformations, or occasionally spatial transformations of image brightness. This research introduces fractional textures and semivariance analysis of image fractions. They were integrated with conventional image measures in stepwise multiple regression modelling of forest structure (canopy and crown closure, stem density, tree height, crown size) and health (a visual stress index). The goal was to conduct a relative comparison of the potential of the various image variable types in modelling of forest structure and health. Analysis was conducted for both canopy (crowns and shadows) and individual tree crown sample data sets extracted from 10 nm bandwidth spectral bands at three resolutions (0.25, 0.5, 1.0 m). Spatial transformations (texture, semivariogram range) of image brightness (DN) and image fractions (IF) were consistently the most significant and first entered variables in the best models of the forest parameters. At the canopy-scale, despite a limited number of available plots (6), stable models were produced that demonstrated the potential for spatially transformed variables. Semivariogram range explained 88% of the total variation of 9 of the 18 models and represented 56% of the variables used in all models while texture variables explained 51% of model variance in 8 of the 18 models and represented 40% of the variables used. At the tree crown scale (n=31), 88% of the total variation of six of eight models was explained by texture variables and 6% by semivariogram variables. DN and IF variables that were not spatially transformed contributed little to the models at both scales. They represented 4% and 6%, respectively, of the variables used in all models. Spatial information in image fractions and image brightness has proven to be more significant than spectral information in these analyses. Of the spatial resolutions evaluated, 0.5 m consistently produced similar or better models than those using the 0.25 or 1.0 m resolutions. These results demonstrate the potential for integration of spatial transforms of image fractions and raw brightness in high-resolution modelling of forest structure and health.     

A mobility constraint model to infer sensor behaviour in forest fire risk monitoring

Wireless sensor networks (WSNs) play an important role in forest fire risk monitoring. Various applications are in operation. However, the use of mobile sensors in forest risk monitoring remains largely unexplored. Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour. This behaviour is focused on achieving a suitable spatial coverage of the WSN when monitoring forest fire risk. The proposed mobility constraint model makes use of a Bayesian network approach and consists of three components: (1) a context typology describing different contexts in which a WSN monitors a dynamic phenomenon; (2) a context graph encoding probabilistic dependencies among variables of interest; and (3) contextual rules encoding expert knowledge and application requirements needed for the inference of sensor behaviour. As an illustration, the model is used to simulate the behaviour of a mobile WSN to obtain a suitable spatial coverage in low and high fire risk scenarios. It is shown that the implemented Bayesian network within the mobility constraint model can successfully infer behaviour such as sleeping sensors, moving sensors, or deploying more sensors to enhance spatial coverage. Furthermore, the mobility constraint model contributes towards mobile sensing in which the mobile sensor behaviour is driven by constraints on the state of the phenomenon and the sensing system.     

土地利用最佳模拟尺度选择及空间格局模拟

土地利用变化是一个受到多重因素相互影响的动态过程。目前,已经成为全球环境变化和可持续发展的重要内容,而区域土地利用空间格局模拟已成为LUCC研究的关键内容。以2000年以及2010年的TM遥感影像解译数据以及数字高程模型、水系、铁路、公路、降雨量和气温等数据为基础,运用二元逻辑斯蒂回归模型对黄土台塬区的土地利用最佳模拟尺度进行了选择,并在此基础上对研究区的各种土地利用进行了空间格局模拟。研究结果显示：（1）在土地利用格局模拟的十个空间尺度上,土地利用变化空间格局与其驱动力因子之间存在着一定的尺度相关性特征;（2）黄土台塬区耕地、林地、草地的ROC值在十个空间尺度上均呈现出先增加后减少的趋势,转折点在400 m尺度附近,说明黄土台塬区的土地利用在尺度效应和尺度转换的效应下,400 m×400 m是此区域土地利用格局优化的最佳模拟尺度;（3）在400 m最佳模拟尺度上所模拟出的草地和林地的分布格局都与人均GDP和地形综合指数两个变量显著相关,而对耕地的分布影响最为明显的变量则是地形综合指数。     

Modelling small area counts in the presence of overdispersion and spatial autocorrelation

The problems arising when modelling counts of rare events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present or anticipated are considered. Different models are presented for handling inference in this case. The different strategies are implemented using data on offender counts at the enumeration district scale for Sheffield, England and results compared. This example is chosen because previous research suggests that social processes and social composition variables are key to understanding geographical variation in offender counts which will, as a consequence, show evidence of clustering both at the scale of the enumeration district and at larger scales. This in turn leads the analyst to anticipate the presence of overdispersion and spatial autocorrelation. Diagnostic measures are described and different modelling strategies are implemented. The evidence suggests that modelling strategies based on the use of spatial random effects models or models that include spatial filters appear to work well and provide a robust basis for model inference but gaps remain in the methodology that call for further research.     

Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations

Abstract

The spatial resolution of the next generation of sensors for the global monitoring of vegetation is assessed with particular reference to the proposed Moderate Resolution Imaging Spectrometer (MODIS). The main innovative use of such instruments will lie in their ability to monitor land transformations at global and continental scales. Reliable monitoring is shown to rely on the success with which the changes in the phenomena being analysed can be separated from other temporal changes. Depending on the type of spatial change being monitored, sensor properties such as accuracy of registration, resolution and radiometric sensitivity are shown to have greatest importance.

An empirical investigation of the required spatial resolution is based on eight Landsat multispectral scanner system images of the normalized difference vegetation index degraded to candidate resolutions between 250 m and 4000 m. Pairs of images from different dates were registered and different images were then generated. Spatial analysis by Fourier and scale variance analyses indicate that resolutions finer than I km are highly desirable for change detection. A resolution of 250 m will probably generate an impractically high quantity of data on a global basis if all the proposed spectral bands are included. A sensor with a resolution of 500 m is recommended as providing the best compromise between detail of changes detected and the size of the resultant data volume but several other options are also suggested, including one involving one or two finer resolution bands to assist multitemporal registration.     

The identification of tropical deforestation fronts at broad spatial scales

Land cover changes are measured at increasingly broader spatial scales. Yet understanding and modelling change processes with a satisfactory accuracy require fine scale observations. The objective of this study is to design and test a methodology to detect tropical deforestation 'hot spots' at broad spatial scales. This methodology is designed to allow concentration of the data collection and modelling of change processes only on the areas characterized by a high rate of change. The procedure is based on a hierarchical set of decision rules with selection criteria being first measured on an exhaustive basis at a global scale and then only for the areas retained in the first sorting, with increasingly selective constraints. The first set of criteria, i.e., proportions in key land cover, landscape fragmentation, and fire activities, were derived from subcontinental scale remote sensing data. Socio-economic variables were also measured at that scale. These different variables were combined over West Africa and the northern boundary of the Central African evergreen forest to identify potential tropical deforestation fronts. Different models were used to generate maps of deforestation hot spots. These were validated with data from the literature.     

Single Sensor Compressive Light Field Video Camera

This paper presents a novel compressed sensing (CS) algorithm and camera design for light field video capture using a single sensor consumer camera module. Unlike microlens light field cameras which sacrifice spatial resolution to obtain angular information, our CS approach is designed for capturing light field videos with high angular, spatial, and temporal resolution. The compressive measurements required by CS are obtained using a random color-coded mask placed between the sensor and aperture planes. The convolution of the incoming light rays from different angles with the mask results in a single image on the sensor; hence, achieving a significant reduction on the required bandwidth for capturing light field videos. We propose to change the random pattern on the spectral mask between each consecutive frame in a video sequence and extracting spatio-angular-spectral-temporal 6D patches. Our CS reconstruction algorithm for light field videos recovers each frame while taking into account the neighboring frames to achieve significantly higher reconstruction quality with reduced temporal incoherencies, as compared with previous methods. Moreover, a thorough analysis of various sensing models for compressive light field video acquisition is conducted to highlight the advantages of our method. The results show a clear advantage of our method for monochrome sensors, as well as sensors with color filter arrays.     

A methodology and a software tool for sensor data validation/reconstruction: Application to the Catalonia regional water network

In this paper, a sensor data validation/reconstruction methodology applicable to water networks and its implementation by means of a software tool are presented. The aim is to guarantee that the sensor data are reliable and complete in case that sensor faults occur. The availability of such dataset is of paramount importance in order to successfully use the sensor data for further tasks e.g. water billing, network efficiency assessment, leak localization and real-time operational control. The methodology presented here is based on a sequence of tests and on the combined use of spatial models (SM) and time series models (TSM) applied to the sensors used for real-time monitoring and control of the water network. Spatial models take advantage of the physical relations between different system variables (e.g. flow and level sensors in hydraulic systems) while time series models take advantage of the temporal redundancy of the measured variables (here by means of a Holt–Winters (HW) time series model). First, the data validation approach, based on several tests of different complexity, is described to detect potential invalid or missing data. Then, the reconstruction process is based on a set of spatial and time series models used to reconstruct the missing/invalid data with the model estimation providing the best fit. A software tool implementing the proposed data validation and reconstruction methodology is also described. Finally, results obtained applying the proposed methodology to a real case study based on the Catalonia regional water network is used to illustrate its performance.     

Modelling the semivariograms and cross-semivariograms required in downscaling cokriging by numerical convolution–deconvolution

A practical problem of interest in remote sensing is to increase the spatial resolution of a coarse spatial resolution image by fusing the information of that image with another fine spatial resolution image (from the same sensor or from sensors on different satellites). Thus, the problem is how to introduce spatial ‘detail’ into a coarse spatial resolution image (decrease the pixel size) such that it is coherent with the spectral information of the image. Cokriging provides a geostatistical solution to the problem and has several interesting advantages: it is a sound statistical method by being unbiased and minimizing a prediction variance (c.f. ad hoc procedures), it takes into account the effect of pixel size, and also autocorrelation in each image as well as the cross-correlation between images, it may be extended to incorporate extra information from other sources and it provides an estimation of the uncertainty of the final predictions. When formulating the cokriging system, semivariograms and cross-semivariograms (or covariances and cross-covariances) appear, some of which cannot be estimated from data directly. Cross-variograms between different variables as well as cross-semivariograms between different supports for the same variable are required. The problem is solved by using linear systems theory in which any variable for any pixel size is seen as the output of a linear system when the input is the same variable on a point support. In remote-sensing applications, the linear system is specified by the point-spread function (or impulse response) of the sensor. Linear systems theory provides the theoretical relations between the different semivariograms and cross-semivariograms. Overall, one must ensure that the whole set of covariances and cross-covariances is positive-definite and models must be estimated for non-observed semivariograms and cross-semivariograms. The models must also be realistic, taking into account, for example, the parabolic behaviour close to the origin presented in regularized semivariograms and cross-semivariograms. The solution proposed is to find by numerical deconvolution a positive-definite set of point covariances and cross-covariances and then any required model may be obtained by numerical convolution of the corresponding point model. The first step implies several numerical deconvolutions where some model parameters are fixed, while others are estimated using the available experimental semivariograms and cross-semivariograms, and some goodness-of-fit measure. The details of the proposed procedure are presented and illustrated with an example from remote sensing.     

空-频域联合投票的交通视频阴影去除方法

交通场景中的静止或运动阴影往往会降低车辆目标跟踪的精度,因此有效地去除阴影是交通监控视频处理的重要环节之一。然而,目前尚无一种能够同时发掘阴影的空间域和频率域特性且抵抗静止和运动阴影干扰的阴影去除方法。为此,提出了一种基于空-频域联合投票策略的交通视频阴影去除方法。首先,将视频帧从RGB颜色空间转换到HSV颜色空间,再进行非下采样剪切波变换;其次,假设变换系数服从高斯分布,采用变换系数的均值和标准差计算每个尺度的加权掩码;然后,根据多尺度变换系数的零树分布特性,利用粗尺度的加权掩码校正细尺度的加权掩码,将各个尺度、各个颜色通道的加权掩码进行线性组合后得到一个公共掩码,再采用基于最小二乘法拟合的最大熵方法计算自适应分割阈值,对公共掩码进行二值化;最后,联合频率域加权掩码、S通道和V通道的掩码进行投票,进而确定去除阴影后的运动车辆区域。实验结果表明,该算法可有效去除交通监控视频中的静态/运动阴影,抑制阴影的干扰,将传统Meanshift算法的输出车辆轨迹与真实轨迹间的平均欧氏距离缩小95%,且未出现目标丢失的现象,增强了智能分析算法的鲁棒性。研究结果说明,该算法有效联合交通监控视频的空间域和频率域表示,充分发掘了运动车辆区域与阴影区域之间的纹理特性和颜色特性差异,有利于获得更精确的阴影去除结果,进而提高车辆目标跟踪的精度。     

A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of field, AVIRIS and MODIS data

Using field data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery, and Moderate-Resolution Imaging Spectroradiometer (MODIS) data, a multi-scale analysis of ecosystem optical properties was performed for Sky Oaks, a Southern California chaparral ecosystem in the spectral network (SpecNet) and FLUXNET networks. The study covered a 4-year period (2000-2004), which included a severe drought in 2002 and a subsequent wildfire in July 2003, leading to extreme perturbation in ecosystem productivity and optical properties. Two vegetation greenness indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)), and a measure of the fraction of photosynthetically active radiation absorbed by vegetation (fPAR), were compared across sampling platforms, which ranged in pixel size from 1 m (tram system in the field) to 1000 m (MODIS satellite sensor). The three MODIS products closely followed the same seasonal trends as the tram and AVIRIS data, but tended to be higher than the tram and AVIRIS values, particularly for fPAR and NDVI. Following a wildfire that removed all green vegetation, the overestimation in MODIS fPAR values was particularly clear. The MODIS fPAR algorithm (version 4 vs. v.4.1) had a significant effect on the degree of overestimation, with v. 4.1 improving the agreement with the other sensors (AVIRIS and tram) for vegetated conditions, but not for low, post-fire values. The differences between MODIS products and the products from the other platform sensors could not be entirely attributed to differences in sensor spectral responses or sampling scale. These results are consistent with several other recently published studies that indicate that MODIS overestimates fPAR and thus net primary production (NPP) for many terrestrial ecosystems, and demonstrates the need for proper validation of MODIS terrestrial biospheric products by direct comparison against optical signals at other spatial scales, as is now possible at several SpecNet sites. The study also demonstrates the utility of in-situ field sampling (e.g. tram systems) and hyperspectral aircraft imagery for proper interpretation of satellite data taken at coarse spatial scales.