Bidirectional reflectance of coral growth-forms

In situ spectral reflectance measurements of substrates in a coral reef are often obtained by viewing a substrate at nadir. However, it is likely that off-nadir oblique viewing would show different spectral characteristics for most coral reef substrates and provide valuable information on structural properties. To understand the relationship between substrate structure and spectral response, this study examined the bidirectional reflectance distribution function (BRDF) of various growth-forms of hard corals and algae, as well as rock, rubble, and sand. BRDF measurements were collected on Heron Reef, the Great Barrier Reef, Australia, during the spring (October to November) of 2010, using a visible and near-infrared (VNIR) spectroradiometer attached to a goniometer. The measurements were made in the same five view zenith angles as the PROBA-1 Compact High Resolution Imaging Spectrometer (CHRIS) satellite (+55°, +36°, 0°, ?36°, ?55°) in the solar principal plane (SPP). A correction algorithm was used to remove both water column and water surface effects. Uncorrected measurements for sand covered with benthic microalgae appeared to have BRDF effects, but when corrected showed an essentially diffuse spectral response. Corrected measurements for branching corals showed BRDF effects dependent on branch spacing, branch length, and colour. The results indicate that spectral reflectance does vary with view angle for complex substrates such as caespitose corals, macroalgae, and irregular beach-rock and to a lesser extent for digitate corals and rippled sand and that the morphology of the coral and the shadowing between branches determines total spectral response. It is concluded that BRDF information can provide additional discriminating features for some coral reef substrates, particularly in the wavelength range of 550–650 nm.     

Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis

The dynamics of foliar chlorophyll concentrations have considerable significance for plant-environment interactions, ecosystem functioning and crop growth. Hyperspectral remote sensing has a valuable role in the monitoring of such dynamics. This study focussed upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Leaf-scale radiative transfer models were used to generate very large spectral data sets with which to develop and rigorously test refinements to the approach and compare it with existing spectral indices. The results demonstrated that by decomposing leaf spectra, the resultant wavelet coefficients can be used to generate accurate predictions of chlorophyll concentration, despite wide variations in the range of other biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and a range of spectral indices. The paper discusses the possibilities for further refining the wavelet approach and for extending the technique to the sensing of a variety of vegetation properties at a range of spatial scales.     

Variability of high-resolution crop reflectance spectra

Abstract

High-spectral-resolution reflectance spectra from ground and helicopter measurements of agricultural crops and soils were analyzed to determine spectral variability in the visible and near-infrared (0 4-2 38 μm), using a procedure previously applied to thermal infrared emittance spectra of the atmosphere and to reflectance spectra of soil samples. Five spectral basis functions were sufficient to describe separately the ground and helicopter data, six or, at most, seven are sufficient to describe the pooled data. Thus, five to seven relatively broad band measurements, together with basis functions developed in this analysis, are sufficient to describe the variability of both data sets, to within differences that are probably associated with the measurement process and instrument noise.     

Feasibility of neural network approach in spectral mixture analysis of reflectance spectra

In the present work, we perform spectral mixture analysis using Chi‐square minimization (χ² minimization) procedure and test the feasibility of applying an inverse technique, neural network (NN) approach, for the spectral unmixing. The training of NN is carried out using the Levenberg–Marquardt algorithm (LM) with the initial weights for training being chosen randomly. The experiments are performed in the laboratory by mixing young, matured and dead leaves of a sequoia tree in various proportions and reflectance spectra of these mixtures are recorded. The proportions are chosen to model a few near‐real situations like different kinds of vegetation in a forest (by mixing young leaves and matured leaves) and trees damaged in a forest fire or affected by certain virus (by mixing matured and dead leaves) and a combination of all these (by mixing young, matured and dead leaves). The spectral mixture analysis employing χ² minimization and the inverse procedure utilizing NN with two hidden layers yielded consistent results in accordance with the proportion of each kind of leaf.     

Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing

Coral reef benthic communities are mosaics of individual bottom-types that are distinguished by their taxonomic composition and functional roles in the ecosystem. Knowledge of community structure is essential to understanding many reef processes. To develop techniques for identification and mapping of reef bottom-types using remote sensing, we measured 13,100 in situ optical reflectance spectra (400-700 nm, 1-nm intervals) of 12 basic reef bottom-types in the Atlantic, Pacific, and Indian Oceans: fleshy (1) brown, (2) green, and (3) red algae; non-fleshy (4) encrusting calcareous and (5) turf algae; (6) bleached, (7) blue, and (8) brown hermatypic coral; (9) soft/gorgonian coral; (10) seagrass; (11) terrigenous mud; and (12) carbonate sand. Each bottom-type exhibits characteristic spectral reflectance features that are conservative across biogeographic regions. Most notable are the brightness of carbonate sand and local extrema near 570 nm in blue (minimum) and brown (maximum) corals. Classification function analyses for the 12 bottom-types achieve mean accuracies of 83%, 76%, and 71% for full-spectrum data (301-wavelength), 52-wavelength, and 14-wavelength subsets, respectively. The distinguishing spectral features for the 12 bottom-types exist in well-defined, narrow (10-20 nm) wavelength ranges and are ubiquitous throughout the world. We reason that spectral reflectance features arise primarily as a result of spectral absorption processes. Radiative transfer modeling shows that in typically clear coral reef waters, dark substrates such as corals have a depth-of-detection limit on the order of 10-20 m. Our results provide the foundation for design of a sensor with the purpose of assessing the global status of coral reefs.     

Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea

During spring and summer 2004, intensive field bio-optical campaigns were conducted in the eastern English Channel and southern North Sea to assess the mechanisms regulating the ocean color variability in a complex coastal environment. The bio-optical properties of the sampled waters span a wide range of variability, due to the various biogeochemical and physical processes occurring in this area. In-water hyperspectral remote sensing reflectances (R_rs) were acquired simultaneously with measurements of optically significant parameters at 93 stations. An empirical orthogonal function (EOF) analysis indicates that 74% of the total variance of R_rs is partly explained by particulate backscattering (b_bp), while particulate and dissolved absorption only explain 15% of the ocean color variability. These results confirm, for the first time from in situ backscattering measurements, previous studies performed in other coastal environments. Whereas the amplitude factors of the first EOF mode are well correlated (r = 0.75) with the particulate backscattering coefficient (b_bp), the highest correlation (r = 0.83) is found with the particulate backscattering ratio (b_bp/b_p). This result highlights the fundamental role of the nature of the bulk particulate assemblage in the ocean color variability.An unsupervised hierarchical cluster analysis applied to our data set of normalized R_rs spectra, leads to five spectrally distinct classes. We show that the class-specific mean R_rs spectra significantly differ from one another by their bio-optical properties. Three classes particularly stand out: one class corresponds to a Phaeocystis globosa bloom situation, whereas the two others are associated with water masses dominated by mineral and non-living particles, respectively. Among the different bio-optical parameters, the particulate backscattering ratio, the chlorophyll concentration, and the particulate organic carbon to chlorophyll ratio, are the most class-specific ones. These different results are very encouraging for the inversion of bio-optical parameters from class-specific algorithms.     

Computer simulation of the skin reflectance spectra

The reflectance spectra of the human skin in visible and near-infrared (NIR) spectral region have been calculated using the Monte Carlo technique, and the specular and internal reflection on the medium surface is taken into account. Skin is represented as a complex inhomogeneous multi-layered highly scattering and absorbing medium. The model takes into account variations in spatial distribution of blood, index of blood oxygen saturation, volume fraction of water and chromophores content. The simulation of the skin tissues optical properties and skin reflectance spectra are discussed. Comparison of the results of simulation and in vivo experimental results are given.     

Interference of salt and moisture on soil reflectance spectra

This article portrays the effects of salt and moisture on soil reflectance spectra and their consequent influences on the estimation of soil salinity and soil moisture contents (SMC). It is amid to demonstrate and discuss how the interference of salt and moisture, as soil constituents, on soil spectra can affect the estimation of either soil salinity or SMC when spectral variabilities are used as predictive variables. To achieve this objective, a data set was obtained from a test area where soil salinity and SMC were largely varied. Furthermore, the Inverted Gaussian (IG) modelling approach, which has been successfully used for the estimation of soil salinity under laboratory conditions and for the estimation of SMC for non-saline soil, is used in this study. Using the IG function, the near-infrared (NIR) and the shortwave infrared (SWIR) regions of the salt-affected soil spectra, with various amount of moisture, were fitted to an IG curve. Parameters of the fitted curve such as functional depth, distance to the inflection point and area under the curve were then used as predictors in a multi-regression analysis to quantify the effect of soil salinity and SMC on soil spectra. The results suggest that a combination of salt and moisture in soil causes anomalies and therefore variations in neither salt nor moisture contents can be modelled accurately on the basis of quantification of soil reflectance. These results suggest that further studies are required to determine a set of calibrating coefficients that can be used to eliminate the background spectral effects caused by either soil salinity or SMC.     

Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra

A Portable Infrared Mineral Analyzer II (PIMA II) field spectrometer was used to measure infrared reflectance spectra (1·3-2·5 μm) of split drill core at 1 cm intervals in both the along-core and cross-core directions. These data were formatted into an image cube similar to that acquired by an imaging spectrometer with 600 spectral channels, and multi-spectral and hyperspectral analysis techniques were used for analysis. Colour images and enhancements provided visual displays of the spectral information, while real-time digital extraction of individual spectra allowed identification of minerals. Absorption band-depth mapping and spectral classification were used to map the spatial distribution of specific minerals in the core. Linear spectral unmixing provided estimated mineral abundances. Analysis results demonstrate that multi-spectral and hyperspectral image analysis methods can be used to produce detailed mineralogical maps of drill core. They suggest that the concepts and analytical techniques developed for analysis of hyperspectral image data can be applied to field and laboratory spectra in a variety of disciplines, and raise the question of the use of hyperspectral scanners in the laboratory.     

Rock type classification of drill core using continuous wavelet analysis applied to thermal infrared reflectance spectra

This study investigates a core logging methodology to map rock type using thermal infrared reflectance (TIR) spectra (500–4000 cm^–1 or 2.5–20.0 μm) for 74 samples encompassing 11 rock types exposed in various mines of the Sudbury Basin, Canada. A continuous wavelet transform (CWT) was used to represent the original reflectance spectra as a suite of wavelets, each capturing spectral features of different scales with the low-scale components containing mineral spectral features and the high-scale components capturing the overall continuum. Classification was driven by the use of endmember spectra and the spectral angle mapper (SAM). Modelling and validation suites were developed and the mapping accuracy evaluated iteratively for random data splits. The results were compared for reflectance and wavelets of low components of power and significance. We found that the variability amongst measurements observed for varying orientation of a sample or due to variable surface roughness can be greatly minimized with the use of low-scale components, thus improving rock type classification. The average accuracy computed for the 11 rock types is highest for the low-scale component of power (72%) data as opposed to the reflectance data (55%). The highest average accuracy per rock type is obtained using the low-scale components (average value of 82%) for seven rock units that are relatively texturally homogeneous and of uniform modal mineralogy. Lower accuracy values are observed for rock units that display pronounced textural heterogeneity at the scale of observation, or variability in modal mineralogy, or are spectrally similar to other rock types.     

Salinity modelling by inverted Gaussian parameters of soil reflectance spectra

This paper presents an approach to estimate soil salinity through modelling of soil spectra using an inverted Gaussian (IG) function. The approach is tested on experimental datasets including measurements of soil physicochemical properties and their spectral reflectance which are obtained under controlled laboratory conditions. The near-infrared (NIR) and shortwave infrared (SWIR) region of the salt-affected soil spectra were fitted to an inverted Gaussian curve. Parameters of the fitted curve, such as functional depth, distance to the inflection point and area under curve, were then used as predictors in regression analysis to estimate soil salinity levels. The results suggest a successful estimation of salinity levels, especially, for soil samples treated with epsomite and bischofite solutions. Amongst the calculated IG parameters, the area under fitted curve resulted in the most accurate estimations. The results demonstrate the relative utility of high spectral resolution data for estimating soil salinity under laboratory controlled conditions.     

Potential signatures of heavy metal complexes in lichen reflectance spectra

Lichens are sensitive to atmospheric pollutants emitted from anthropogenic activities and are thus effective biomonitors. A variety of heavy metals, such as nickel (Ni), iron (Fe), lead (Pb), copper (Cu), and cadmium (Cd), can be emitted by metal smelters. The purpose of this study was twofold: (1) to measure the spectral reflectance properties (350–2500 nm) of expected heavy metal complexes in lichens (oxalates and sulphides); and (2) to determine whether these complexes contribute features to reflectance spectra of lichens from the vicinity of a heavy metal smelter. Some metal oxalate spectra are characterized by crystal field transition absorption bands in the 500–1300 nm region, which are specific to the particular metal cation they contain and its oxidation state. The 1900–2500 nm region exhibits multiple absorption bands attributable to the oxalate molecule. The metal sulphide reflectance spectra are characterized by generally low reflectance and few if any strong or diagnostic spectral features; those that are found can be related to a specific cation and its oxidation state. These spectra were used to determine whether reflectance spectra of a diverse suite of lichens collected downwind of a smelter showed spectral evidence indicative of heavy metal oxalates or sulphides. The lichen spectra, coupled with the oxalate and sulphide spectra and independently determined heavy metal concentration, failed to reveal spectral features that could be unambiguously related to heavy metal complexes. This was likely due to a number of causes: lichen reflectance spectra have absorption bands that overlap those of oxalates; oxalate and sulphide concentrations may have been too low to allow for their unambiguous identification, and lichen spectra are naturally diverse in the region below 1300 nm. There were no strong or significant linear trends between metal concentrations and distance from the smelter (coefficient of determination (R²) values <0.05), or between absorption band depths in the lichen spectra and distance from the smelter (R² values <0.06). This was likely due to the inclusion of multiple lichen species in the analysis, which may interact with airborne pollutants in different ways, and microenvironmental effects.     

Effects of lichens on the reflectance spectra of granitic rock surfaces

The objective of this study was to determine the effect of lichen cover on the spectral reflectance characteristics of granitic rocks. Rock samples collected from bedrock exposures and talus slopes were granodiorite, tonalite, granite, and quartz-diorite. The lichens found on these rocks varied in color, i.e., black, dark brown, medium gray, olive-green, yellow-green, green, orange, and orange-red. The visible and near infrared reflectance spectra (400–1100 nm) of the rock and lichen samples show that lichens can affect the rock's spectra. These changes are dependent on the reflectance contrast between the rock surface and the lichen covering.     

Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef,Australia

Studies investigating the spectral reflectance of coral reef benthos and substrates have focused on the measurement of pure endmembers, where the entire field of view (FOV) of a spectrometer is focused on a single benthos or substrate type. At the spatial scales of the current satellite sensors, the heterogeneity of coral reefs even at a sub-metre scale means that many individual image pixels will be made up of a mixture of benthos and substrate types. If pure endmember spectra are used as training data for image classification, there is a spatial discrepancy, because many pixels will have a mixed endmember spectral reflectance signature. This study investigated the spectral reflectance of coral reef benthos and substrates at a spatial scale directly linked to the pixel size of high spatial resolution imaging systems, by incorporating multiple benthos and substrate types into the spectrometer FOV in situ. A total of 334 spectral reflectance signatures were measured of 19 assemblages of the coral reef benthos and substrate types. The spectra were analysed for separability using first derivative values, and a discrimination decision tree was designed to identify the assemblages. Using the decision tree, it was possible to identify 15 assemblages with a mean overall classification accuracy of 62.6%.     

Influence of desert varnish on the reflectance of gossans in the context of Landsat TM data,southern Red Sea Hills,Sudan

Laboratory spectra have been used for studying the characteristics of desert varnish in the context of Landsat TM data in order to establish its influence on the reflectance of gossans and their discrimination. Supported by limited chemical analysis, reflectance values from selected rock samples have been processed using statistical methods, and spectral plots produced and visually interpreted. Spectral analysis shows that paucity and Mn/Fe proportion in desert varnish, lightness or darkness of the rock, its mineral composition, and the wavelength under consideration control the effect of desert varnish on Landsat TM data. In general, overall reflectance of rocks is reduced by up to 70% in the VIS parts and about 20% in the SWIR parts of the electromagnetic spectrum due to desert varnish. For acidic rocks this effect seems to be intense, whereas mafic rocks are less influenced. The latter generally display spectral crossovers partly due to Mn/Fe content. In the VIS region absorption bands turn to featureless spectra due to desert varnish, whereas in the SWIR spectra are generally subdued but preserved. This has apparently little consequence on the discrimination of gossans from other rock types on composites constituted by bands from the VIS, IR and SWIR parts.     

A novel restoration approach for vegetation reflectance spectra at noisy bands using the principal component analysis method

ABSTRACT

Due to the signal-to-noise ratio (SNR) of sensors, as well as atmospheric absorption and illumination conditions, etc., hyperspectral data at some bands are of poor quality. Data restoration for noisy bands is important for many remote sensing applications. In this paper, we present a novel data-driven Principal Component Analysis (PCA) approach for restoring leaf reflectance spectra at noisy bands using the spectra at effective bands. The technique decomposes the leaf reflectance spectra into their principal components (PCs), selects the leading PCs that describe the most variance in the data, and restores the data from these components. First, the first 10 PCs were determined from a training dataset simulated by the leaf optical properties model (PROSPECT-5) that contained 99.998% of the total information in the 3636 training samples. Then, the performance of the PCA method for restoration of the reflectance at noisy bands was investigated using the ANGERS leaf optical properties dataset; the results showed the spectral root mean squared error (RMSE) is in the range 6.46 × 10^?4 to 6.44 × 10^?2, which is about 3 ? 34 times more accurate than the stepwise regression method and partial least squares method (PLSR) for the ANGERS dataset. The results also showed that if the noisy bands are far away from the effective bands, the accuracy of the restored leaf reflectance spectra will decrease. Thirdly, the reliability of the restored reflectance spectra for retrieving leaf biochemical contents was assessed using the ANGERS dataset and leaf optical properties dataset established by the Beijing Academy of Agriculture and Forestry Sciences (BAAFS). Three water-sensitive vegetation indices ? normalized difference water index (NDWI), normalized difference infrared index (NDII) and Datt water index (DWI), derived from the restored leaf spectra ? were employed to retrieve the equivalent water thickness (EWT). The results showed that the leaf water content can be accurately retrieved from the restored leaf reflectance spectra. In addition, the PCA method to restore vegetation spectral reflectance can be applied on canopy level as well. The results showed that the spectral root mean squared error (RMSE) is in the range 8.22 × 10^?4 to 1.87 × 10^?2. The performance of the restored canopy spectra was investigated according to the results of retrieving canopy equivalent water thickness (CEWT) using the five spectral indices NDWI, NDWI₁₃₇₀, NDWI₁₈₉₀, NDII and DWI. The results indicated that the restored canopy spectra can be used for retrieving CEWT reliably and improve the accuracy of retrieval compared to the results using original canopy reflectance spectra.     

A multi-angle spectrometer for automatic measurement of plant canopy reflectance spectra

The paper describes the design and operation of a multi-angle spectrometer (MAS) for automatic measurement of near-field spectral reflectances of plant canopies at hourly intervals. A novel feature of the instrument is a rotating periscope connected to a spectrometer via a fiber optic cable. Canopy reflectances are calculated for multiple view azimuths, at a single zenith angle from measurements of spectrometer dark current, incoming solar irradiance and reflected radiances. Spectral measurements are made between 300 and 1150 nm wavelength at a band-to-band spacing of 3 nm, and a bandwidth (full-width, half maximum) of 10 nm. Preliminary data analysis showed that the canopy reflectance model of Kuusk [Kuusk, A. (1995). A fast, invertible canopy reflectance model. Remote Sensing of Environment 51, 342-350] reproduced the observed large differences in visible and near-infrared (NIR) reflectances, but the model was unable to predict quantitatively the observed variations in the measured reflectance spectra with azimuth, particularly in the NIR. Discrepancies between model and measurements are likely due to the inhomogeneous nature of the forest canopy in contrast to the assumption of a uniformly absorbing turbid medium in the model. Measurements using the MAS can be used to investigate directional dependences of reflectance indices and for testing BRDF models used to separate geometrical and plant physiological contributions to the reflectance signals. The MAS provides continuous sampling of reflectance indices which can be compared with canopy properties such as chlorophyll content and photosynthetic capacity.     

Effects of heavy metals on the absorbance and reflectance spectra of plants

Abstract.

Published work on the reflectance of vegetation growing over soil mineralizations is reviewed. Experimental work was carried out on several species grown in a glasshouse and was extended to a pilot field study. In the laboratory studies, the most general effects of Cd, Cu, Pb or Zn were growth inhibition. A detailed study of the leaf pigments of pea plants showed that the chl a/chl b ratio (chl=chlorophyll) decreased under conditions of Cd or Cu stress but showed little effect with Pb or Zn. However, the absorption spectra of chloroplast pigments were not found to show any wavelength shifts with metal treatments, indicating that new spectral forms of chlorophyll were not produced as a stress response. A decrease in the total chlorophyll content of leaf tissue (fresh weight basis) was correlated with an increase in visible-wavelength leaf reflectance (R) of pea plants. R at infrared wavelengths of 0.85 μm, 1.65 μm and 2.20 μm decreased in metal-treated plants, compared with controls. Although experiments with other species, and a review of published literature, indicate that reflectance effects are dependent on species, phase of growth cycle and environment, the existence of correlations between R at certain wavelengths and the metal concentrations to which roots are exposed was confirmed using oak trees growing naturally in the area of a copper-arsenic mineralization in south-west England. Metal (Cu or As) concentrations in the soil were strongly negatively correlated (p > 99 per cent) with R at 1.65 μm and 2.20 μm, and positively correlated (p > 95 per cent) with R at 0.660 μm, in close agreement with the experiments on pea plants. The inclusion of the relevant infrared bands on Earth resource survey instruments is likely to enhance their usefulness for detecting heavy metal stress in plants.     

Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)

In this paper the possibility of predicting salt concentrations in soils from measured reflectance spectra is studied using partial least squares regression (PLSR) and artificial neural network (ANN). Performance of these two adaptive methods has been compared in order to examine linear and non-linear relationship between soil reflectance and salt concentration.Experiment-, field- and image-scale data sets were prepared consisting of soil EC measurements (dependent variable) and their corresponding reflectance spectra (independent variables). For each data set, PLSR and ANN predictive models of soil salinity were developed based on soil reflectance data. The predictive accuracies of PLSR and ANN models were assessed against independent validation data sets not included in the calibration or training phase.The results of PLSR analyses suggest that an accurate to good prediction of EC can be made based on models developed from experiment-scale data (R² > 0.81 and RPD (ratio of prediction to deviation) > 2.1) for soil samples salinized by bischofite and epsomite minerals. For field-scale data sets, the PLSR predictive models provided approximate quantitative EC estimations (R² = 0.8 and RPD = 2.2) for grids 1 and 6 and poor estimations for grids 2, 3, 4 and 5. The salinity predictions from image-scale data sets by PLSR models were very reliable to good (R² between 0.86 and 0.94 and RPD values between 2.6 and 4.1) except for sub-image 2 (R² = 0.61 and RPD = 1.2).The ANN models from experiment-scale data set revealed similar network performances for training, validation and test data sets indicating a good network generalization for samples salinized by bischofite and epsomite minerals. The RPD and the R² between reference measurements and ANN outputs of theses models suggest an accurate to good prediction of soil salinity (R² > 0.92 and RPD > 2.3). For the field-scale data set, prediction accuracy is relatively poor (0.69 > R² > 0.42). The ANN predictive models estimating soil salinity from image-scale data sets indicate a good prediction (R² > 0.86 and RPD > 2.5) except for sub-image 2 (R² = 0.6 and RPD = 1.2).The results of this study show that both methods have a great potential for estimating and mapping soil salinity. Performance indexes from both methods suggest large similarity between the two approaches with PLSR advantages. This indicates that the relation between soil salinity and soil reflectance can be approximated by a linear function.     

A global analysis of urban reflectance

A comparative Spectral Mixture Analysis (SMA) of Landsat 7 Enhanced Thematic Mapper (ETM+) imagery for a collection of 28 urban areas worldwide provides a physical basis for a spectral characterization of urban reflectance properties. These urban areas have similar mixing space topologies and can be represented by three‐component linear mixture models in both scene‐specific and global composite mixing spaces. The results of the analysis indicate that the reflectance of these cities can be accurately described as linear combinations of High Albedo, Dark and Vegetation spectral endmembers within a two‐dimensional mixing space containing over 90% of the variance in the observed reflectance. Only two of the 28 cities had greater than 10% median RMS misfit to the three‐endmember linear model. The relative proportions of these endmembers vary considerably among different cities and within individual cities but in all cases the reflectance of the urban core lies near the dark end of a mixing line between the High Albedo and Dark endmembers. The most consistent spectral characteristic of the urban mosaic is spectral heterogeneity at scales of 10–20?m. In spite of their heterogeneity, built‐up areas do occupy distinct regions of the spectral mixing space. This localization in mixing space allows spectrally mixed pixels in built‐up areas to be discriminated from undeveloped land cover types. This provides a basis for mapping the spatial extent of human settlements using broadband optical satellite imagery collected over the past 30 years.