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Abstract This study aimed to improve the potential of Analytical Spectral Devices (ASD) hyperspectral and Landsat Operational Land Imager (OLI) data in predicting soil organic matter content (SOMC) in the bare topsoil of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China. The results indicated that the correlation of coefficients (R) between SOMCs and hyperspectral data processed by fractional derivative were significant at the 0.01 level; the number of wave bands increased initially and then decreased when the order increased. The correlation of coefficient peak appeared at the 1.2 order with a value of 0.52. The correlation of coefficients (R) between SOMCs and the optimal remote sensing indexes (the ratio index, RI; difference index, DI; and the normalized difference index, NDI) of peaked at the 1.2 order, with correlation of coefficients (R) values of 0.81, 0.86 and 0.82, respectively. Six SOMC estimation models were created by means of a single band and optimal remote sensing indexes using Gray Relational Analysis-BP Neural Network (GRA-BPNN). This study found that the optimal model was a 1.2 order derivative model, where the lowest root mean square error (RMSE) was 3.26 g/kg, the highest was 0.92, and the residual prediction deviation (RPD) was 2.26. To complete the high accuracy retrieval of SOMCs, based on Landsat OLI operational land images data, more 'hidden' information from the Landsat OLI images were obtained by employing the subsection of spectral band method and the fractional derivative algorithm. Accuracy of the SOMC map was attained by the optimal model of the ground hyperspectral data and the Landsat OLI data, which had low RMSE values of 4.21 g/kg and 4.16 g/kg, respectively. Therefore, we conclude that the SOMC can be estimated and retrieved using a fractional derivative algorithm, the subsection of spectral band method, and the optimal remote sensing index. Highlights • A practical method for band matching from multi-source remote sensing data • A novel method for mining 'hidden' information from remote sensing data • GRA-BPNN is efficient for the estimation of SOMC. [ABSTRACT FROM AUTHOR]

Abstract Reconstruction and evolution of two-dimensional spectra of surface waves in the Gulf of Mexico are derived from airborne sun-glitter imagery. As the proposed method is based on a linear transfer function deduced from the shape of the sunglitter brightness, the absolute wavenumber elevation spectrum does not require any additional assumption or information about sky brightness, wind or wave energy. The detailed description of the airborne image processing method is given. As demonstrated, retrieved spectra agree well with nearby NDBC buoy data, both for spectrum shape, level and energy angular distribution. The 180-degree wave direction ambiguity, inherent to image-derived spectra, is eliminated by using cross-correlation analysis between two consecutive images. A case study corresponding to the spectral evolution with increasing distance from shore in slanting-fetch conditions is then considered. Energy level and peak position transformation are consistent with established approximations and laws of wind-sea development. The technical requirements (flight altitude, image resolution, view angles, etc) and applicability of the suggested methodology are also discussed. These results demonstrate the potential efficiency of high resolution sea state monitoring from drones or light aircrafts using sunglitter imagery. Highlights • Sea surface wave spectra are derived from airborne sunglitter imagery. • A linear transfer function relates sunglitter brightness and wave elevation spectra. • The method is applied to airborne images taken in the Gulf of Mexico. • A case study on the spectral evolution with fetch increasing is considered. • The technical requirements and method applicability are discussed. [ABSTRACT FROM AUTHOR]

Remote sensing of inland and sea waters depends on the quality of the retrieval of the water-leaving radiance from the top-of-atmosphere measurements. The water-leaving radiance can be difficult to observe due to the reflection of direct sunlight on the air-water interface (sunglint) in the direction of the satellite field of view. The viewing geometry of Sentinel-2 satellite ( European Space Agency ) makes it vulnerable to sunglint contamination. In this paper, an original method is proposed to correct Sentinel-2-like imagery for sunglint contamination. The sunglint contribution is first estimated from the shortwave-infrared (SWIR) part of the spectrum and then extrapolated toward the near-infrared and visible bands. The spectral variation of the sunglint signal is thus revisited for a wide spectral range (from 350 to 2500 nm). The bidirectional reflectance distribution function related to the sunglint is shown to vary by > 28% from the SWIR to the blue bands of Sentinel-2. The application of the proposed algorithm on actual Sentinel-2 data demonstrates that sunglint patterns are satisfactorily removed over the entire images whatever the altitude of the observed target. Comparison with in situ data of water-leaving radiances (AERONET-OC) showed that our proposed algorithm significantly improves the correlation between satellite and in situ data by 55% (i.e., from R 2 = 0.56 to R 2 = 0.87). In addition, the discrepancies between satellite and in situ measurements are reduced by 60%. It is also shown that the aerosol data provided by the Copernicus Atmosphere Monitoring Service (CAMS) can be safely used within the proposed algorithm to correct the Sentinel-2-like satellite data for both sunglint and atmospheric radiances. Improvements of the proposed method potentially rely on simultaneous retrievals of the aerosol optical properties. The proposed method is applicable to any satellite sensor which is able to measure in SWIR spectral bands over aquatic environments. [ABSTRACT FROM AUTHOR]

In remote sensing data exploitation, spectral mixture analysis is commonly used to detect land cover materials and their corresponding proportions present in the observed scene. In recent years, high spatial resolution airborne hyperspectral images have shown their potential for deriving accurate land cover maps. In this paper, a new spectral mixture analysis model for mapping urban environments using high spatial resolution airborne hyperspectral data is proposed. First, non-local self-similarity is exploited to partition the scene into groups of similar pixels. The spectral signals of the pixels in each of these groups are assumed to be comprised of the same endmembers, but with different abundance values. Then, the similar pixels in each group are simultaneously unmixed using a jointly sparse spectral mixture analysis method. The proposed method was applied to map land cover in Pavia city, northern Italy, using airborne ROSIS data. An overall classification accuracy of 97.24% was achieved for the Vegetation - Impervious surface - Soil model. Our experimental results demonstrate that the proposed jointly sparse spectral mixture analysis model is well suited for mapping land cover in urban environments using high resolution hyperspectral data. [ABSTRACT FROM AUTHOR]

Remote sensing of sun-induced chlorophyll fluorescence (SIF) is a novel optical tool for the assessment of terrestrial photosynthesis or gross primary production (GPP). Several recent studies have demonstrated the strong link between GPP and space-borne retrievals of SIF at broad scales. However, critical gaps remain between short-term small-scale mechanistic understanding and seasonal global observations. Here, we present a model-based analysis of the relationship between SIF and GPP across scales for diverse vegetation types and a range of meteorological conditions, with the ultimate focus on reproducing the environmental conditions during remote sensing measurements. The coupled fluorescence-photosynthesis model SCOPE is used to simulate GPP and SIF at the both leaf and canopy levels for 13 flux sites. Analyses were conducted to investigate the effects of temporal scaling, canopy structure, overpass time, and spectral domain on the relationship between SIF and GPP. The simulated SIF is highly non-linear with GPP at the leaf level and instantaneous time scale and tends to linearize when scaling to the canopy level and daily to seasonal. These relationships are consistent across a wide range of vegetation types. The relationship between SIF and GPP is primarily driven by absorbed photosynthetically active radiation (APAR), especially at the seasonal scale, although the photosynthetic efficiency also contributes to strengthen the link between them. The linearization of their relationship from leaf to canopy and averaging over time is because the overall conditions of the canopy fall within the range of the linear responses of GPP and SIF to light and the photosynthetic capacity. Our results further show that the top-of-canopy relationships between simulated SIF and GPP have similar linearity regardless of whether we used the morning or midday satellite overpass times. Field measurements confirmed these findings. In addition, the simulated red SIF at 685 nm has a similar relationship with GPP as that of far-red SIF at 740 nm at the canopy level. These findings provide model-based evidence to interpret remotely sensed SIF data and their relationship with GPP. [ABSTRACT FROM AUTHOR]

The screening of sensitive spectral indictors is essential for quantitative diagnosis of ozone (O 3 )-induced stress in plants. Four wheat ( Triticum aestivum L.) cultivars with different degrees of O 3 -tolerance were grown under an elevated O 3 concentration (E-O 3 ) in fully open-air field conditions for two consecutive growth seasons from 2012 to 2013. The aim was to find sensitive hyperspectral indictors for real-time detection of O 3 effects. The results showed that E-O 3 caused a significant decrease in leaf thickness and pigment concentrations, resulting in a change in leaf reflectance. The effects of E-O 3 on both physiological variables and reflectance characteristics were wheat cultivar-specific, with a greater and earlier O 3 effect found in the O 3 -sensitive wheat cultivars than in the O 3 -tolerant cultivars. Spectral indices that were previously developed to detect leaf biological variables were examined, and highly correlated relations were found between the chlorophyll content and the three spectral parameters ND 705 , mND 705 and R 550 , with Pearson r values of 0.896, 0.892 and − 0.872, respectively. When independently determined data from 2013 were used to test the derived equations, the coefficients (R 2 ) of correlation between the measured and estimated chlorophyll were 0.817 ( ND 705 ), 0.833 ( mND 705 ) and 0.776 ( R 550 ); the root mean square errors (RMSE) were 0.227 ( ND 705 ), 0.228 ( mND 705 ) and 0.254 ( R 550 ) (mg kg − 1 ); and the mean relative errors (RE) were 8.7% ( ND 705 ), 8.1% ( mND 705 ) and 9.1% ( R 550 ). Furthermore, O 3 -induced changes in the three optical parameters were in accordance with the leaf chlorophyll responses in wheat. Our study suggested that the reflectance indices mND 705 , ND 705 and R 550 , especially the former two spectral indices that contained information from several bands, could help to support the diagnosis and real-time monitoring of O 3 -induced damage in wheat. To estimate the wheat yield accurately using the selected spectral indices, the filling stage was found to be the best time for measuring canopy reflectance. [ABSTRACT FROM AUTHOR]

Prominent yellow flowers that are present in a Brassica oilseed crop such as canola require careful consideration when selecting a spectral index for yield estimation. This study evaluated spectral indices for multispectral sensors that correlate with the seed yield of Brassica oilseed crops. A small-plot experiment was conducted near Adams, Oregon in which spring canola was grown under varying water regimes and nitrogen treatments to create a wide range in oilseed yield. Plot measurements consisted of canopy reflectance at flowering using a hand-held spectroradiometer and seed yield at physiological maturity. Spectroradiometric measurements were converted to MODIS band equivalent reflectance. Selected indices were computed from spectra obtained with the radiometer and correlated with seed yield. A normalized difference yellowness index (NDYI), computed from the green and blue wavebands, overcame limitations of the normalized difference vegetation index (NDVI) during flowering and best modeled variability in relative yield potential. NDYI was more linear and correlated with county-wide oilseed yield data and MODIS satellite data from North Dakota (r 2 ≤ 0.72) than NDVI (r 2 ≤ 0.66). NDYI only requires wavebands in the visible region of the spectrum and can be applied to any satellite or aerial sensor that has blue and green channels. These findings highlight the benefit of using a spectral index that is sensitive to reproductive growth of vegetation instead of vegetative growth for crops with spectrally prominent reproductive canopy elements. Our results indicate that NDYI is a better indicator of yield potential than NDVI during mid-season development stages, especially peak flowering. [ABSTRACT FROM AUTHOR]

The recently launched WorldView-3 (WV-3) satellite is a high spatial resolution instrument with eight multispectral bands in the visible and near-infrared and an additional eight bands in the short-wave infrared (SWIR). Three of the SWIR bands, including bands 9, 12, and 16 (centered at 1210, 1730, and 2330 nm) respectively overlap with diagnostic absorption features of hydrocarbons (HCs) at 1200, 1700, and 2300 nm. This paper aims to investigate the capability of this superspectral instrument for direct HC detection. For this purpose, we have conducted several simulation experiments using multiple datasets comprising (i) spectral libraries of different HCs measured in the laboratory, (ii) close-range hyperspectral imagery of a well-known tar-sand sample acquired with a sisuCHEMA imaging system, and (iii) far-range ProSpecTIR hyperspectral imagery collected over twelve simulated HC-shows. These datasets were convolved to the spectral resolution of WV-3 and analyzed using a variety of spectral processing techniques. The absorption features of HCs manifest themselves in all cases albeit with varying intensity. The effect of a series of parameters on the detectability of the HCs was also scrutinized; these included background geology, spectral mixing, HC type, endmember set, spatial resolution, noise level, and topography. We demonstrate that the HC absorption feature in WV-3's band 12, accompanied by shoulders sustained at bands 11 and 13 (centered at 1660 and 2165 nm), is resilient enough and persist under various conditions. Potential applications of these finding include hydrocarbon exploration in frontier basins and environmental monitoring. [ABSTRACT FROM AUTHOR]

Several upcoming hyperspectral satellite sensor missions (e.g., the Hyperspectral Infrared Imager and the Environmental Mapping and Analysis Program) will greatly expand the opportunities for researchers to use imaging spectroscopy data for discriminating and mapping plant species and plant functional types (PFTs; defined in this study as combinations of leaf-type, leaf/plant duration and life form). Accurate knowledge of the spatial distribution of dominant plant species and PFTs is highly valuable to many scientific and management goals, including improved parameterization of ecosystem process and climate models, better invasive species distribution monitoring and forecasting, quantification of human and natural disturbance and recovery processes, and evaluations of terrestrial vegetation response to climate change. Most often, species-level discrimination has been achieved using fine spatial resolution (≤ 20 m) airborne imagery, but currently proposed spaceborne imaging spectrometers will have coarser spatial resolution (~ 30 to 60 m). In order to address the impact of coarser spatial resolutions on our ability to spectrally separate species and PFTs, we classified dominant species and PFTs in five contrasting ecosystems over a range of spatial resolutions. Study sites included a temperate broadleaf deciduous forest, a brackish tidal marsh, a mixed conifer/broadleaf montane forest, a temperate rainforest and a Mediterranean climate region encompassing grasslands, oak savanna, oak woodland and shrublands. Data were acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over each site, and spatially aggregated to 20, 40 and 60 m resolutions. Canonical Discriminant Analysis (CDA) was used to classify species and PFTs at each site and across scales with overall accuracies ranging from 61 to 96% for species and 83–100% for PFTs. The results of this study show accuracy increases at coarser resolutions (≥ 20 m) across ecosystems, supporting the use of imaging spectroscopy data at spatial resolutions up to 60 m for the purpose of discriminating among plant species and PFTs. In four of the five study sites, the best accuracies were achieved at 40 m resolution. However, at coarser resolutions, some fine-scale species variation is lost and classes that occur only in small patches cannot be mapped. We also demonstrate that spectral libraries developed from fine spatial resolution imagery can be successfully applied as training data to accurately classify coarser resolution data over multiple ecosystems. [ABSTRACT FROM AUTHOR]

Terrain and landscape complexities can limit the accurate discrimination of land use categories with similar spectral signatures, as well as the accurate detection of land use change in temporal analyses of landscape dynamics. Studies based on misclassified land use data can generate biased parameter estimates and standard errors, inaccurate predictions, and incorrect policy recommendations. To address these challenges and improve the accuracy of land use analyses, we implement a post-classification strategy to detect misclassified land use observations using a latent multinomial logit model. This strategy is tested using both Monte Carlo simulations and a time series dataset based on supervised classification of remotely sensed data corresponding to land use decisions observed in a Mexican coffee growing region during the period 1984–2006. The results indicate that the strategy is useful for identifying land use observations with a high probability of being wrongly classified, even between categories with low discriminative spectral signatures. Reclassification of the land use data, based on the model results, increases the magnitudes of the marginal effects of the analyzed land use drivers in the theoretically expected directions, and in some cases improves the statistical significance of the parameter estimates. [ABSTRACT FROM AUTHOR]

In this paper we present novel automated field spectroscopy systems for collecting unattended, continuous and long-term measurements of plant canopies and, more in general, of Earth's ecosystems. These systems simultaneously collect high and ultra-high resolution spectra in the visible to near-infrared (VNIR) domain employing two spectrometers: i) the first covers the spectral range 400–1000 nm with a 1.0 nm spectral resolution; ii) the second provides a sub-nanometer spectral resolution within the 700–800 nm spectral range. The data collected by the first spectrometer allow retrieval of VNIR reflectance, while the higher spectral resolution data from the second device permit estimation of vegetation Sun-Induced Fluorescence (SIF) in the O 2 –A band. The instruments are constructed by assembling commercial and on-the-shelf optoelectronic devices to facilitate reproduction of the instrument for promoting measurements over different ecosystems. The instrument's optical design, data collection and processing, laboratory and in-field calibration methods are reported and discussed. The high spectral resolution and the rigorous calibration methods enable accurate estimation of SIF in physical units by exploiting almost the same retrieval concept as that of the European Space Agency FLuorescence EXplorer mission. The instruments have been operated in several field campaigns with the aim to show: i) the possibility of continuous and seasonal monitoring of plant growth and activity of an agricultural crop; and ii) the diverse and specific daily course patterns of different types of canopy. The datasets of canopy reflectance, vegetation indices and SIF collected are shown and discussed. [ABSTRACT FROM AUTHOR]

Despite the threats faced by seabirds in both terrestrial and marine habitats, even basic knowledge of the locations of colonies, population sizes and trends is lacking for many remote areas of the world. Recent studies have shown that the guano of Adélie penguins can be identified from Landsat Enhanced Thematic Mapper (ETM) imagery and used to map colonies on coasts around continental Antarctica. Our study highlights a new technique based on the unique spectral signature of guano that can be used to discriminate seabird colonies from background geology and vegetation in a wider range of natural environments, including the vegetated and zoologically-diverse region of the Antarctic Peninsula; moreover, the method was effective for all densely colonial, surface-nesting seabirds. Using Landsat ETM imagery, we correctly identified all known seabird colonies of over 50 pairs in the area of Marguerite Bay. Almost all other areas with a similar spectral signature that were outside known breeding areas were single pixels that were readily distinguishable from genuine colonies. If these were excluded, only 4.1% of pixels appeared to represent unknown breeding or roosting sites, and warrant further investigation. The spatial extent of the guano provided a general guide to the number of individuals present, but further work would be required to determine the accuracy of this method for estimating population size. Spectral profiles of guano collected by satellite and hand-held spectrometers were compared with available data in spectral libraries and did not match with any known geological profile. There may also be potential for discriminating colonies of different species that differ in phenology and show seasonal changes in diet by the carefully-timed acquisition of suitable satellite imagery. We conclude that the remotely-sensed guano signature is a good indicator of the location of seabird breeding or roosting sites, with potentially wide application to other areas of the world. [ABSTRACT FROM AUTHOR]

Abstract: In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objectives of this study are to develop, demonstrate and validate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near real-time at the African continental scale as a first step towards global scale coverage. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. Two different validations were done at the continental scale over Africa: i) The algorithm validation checked the ability of the proposed algorithm to perform as effectively as human interpretation of the image: it showed an accuracy of 96.6% and no commission errors. ii) The product validation was carried out by using an independent dataset derived from high resolution imagery: the continental permanent water surface product showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at the African continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties. [Copyright &y& Elsevier]

Abstract: Ground, airborne and spaceborne data were collected for a 450ha prescribed fire implemented on 18 October 2011 at the Henry W. Coe State Park in California. The integration of various data elements allowed near-coincident active fire retrievals to be estimated. The Autonomous Modular Sensor-Wildfire (AMS) airborne multispectral imaging system was used as a bridge between ground and spaceborne data sets providing high-quality reference information to support satellite fire retrieval error analyses and fire emissions estimates. We found excellent agreement between peak fire radiant heat flux data (<1% error) derived from near-coincident ground radiometers and AMS. Both MODIS and GOES imager active fire products were negatively influenced by the presence of thick smoke, which was misclassified as cloud by their algorithms, leading to the omission of fire pixels beneath the smoke, and resulting in the underestimation of their retrieved fire radiative power (FRP) values for the burn plot, compared to the reference airborne data. Agreement between airborne and spaceborne FRP data improved significantly after correction for omission errors and atmospheric attenuation, resulting in as low as 5% difference between Aqua/MODIS and AMS. Use of in situ fuel and fire energy estimates in combination with a collection of AMS, MODIS, and GOES FRP retrievals provided a fuel consumption factor of 0.261kgMJ−1, total energy release of 14.5e+06MJ, and total fuel consumption of 3.8e+06kg. Fire emissions were calculated using two separate techniques, resulting in as low as 15% difference for various species. [Copyright &y& Elsevier]

Abstract: Land Surface Temperature and Emissivity (LST&E) data determine the amount of net longwave radiation emitted from the Earth's surface and are therefore critical variables for studying a variety of surface–atmosphere processes over land such as evapotranspiration, land cover change, and surface composition. Because emissivity is an intrinsic property of the surface, multispectral thermal infrared emissivity data have the potential for enhancing our ability to monitor landscape changes in environmentally sensitive zones beyond what is currently possible from standard practices used today. The most common of these practices is the use of visible to short-wave infrared data, in particular the Normalized Difference Vegetation Index (NDVI). Two algorithms are currently used to generate the LST&E products from MODIS data, but studies have identified several issues with both these algorithms that limit their usefulness for land cover change detection. These issues have been recently addressed by applying the ASTER Temperature Emissivity Separation (TES) algorithm to MODIS thermal infrared data to generate LST and a dynamically varying multispectral emissivity product for bands 29, 31, and 32 at 1-km resolution. The new product (MOD21) will be released with MODIS Collection 6 during fall 2013. This study demonstrates the utility of the dynamic MOD21 multispectral emissivity product to detect land cover changes over a broad range of different Earth surface domains including land degradation in dryland regions, snow melt characteristics on glaciers and ice sheets, extreme ecosystem disturbances, and agricultural activities. The MOD21 spectral emissivity provided increased sensitivity to land cover change in a more consistent manner than is currently possible with other emissivity products and, depending on the application, standard visible to near infrared (VNIR) data. The results suggest that synergistic use of thermal and VNIR data will help us to better identify and understand changes in the Earth surface system, and reduce uncertainties in estimating their magnitudes and trends. [Copyright &y& Elsevier]

Abstract: The Suomi National Polar-orbiting Partnership (SNPP) sensors operationally measure a broad spectrum from microwave to ultraviolet wavelengths for generating 30 satellite products. The wide swath of the SNPP Visible Infrared Imaging Radiometer Suite (VIIRS) observed a historic event: 3 typhoons that all hit China mainland within 5days. The Community Radiative Transfer Model (CRTM) provides critical supports to the SNPP instrumental validation and verification efforts. For example, the CRTM helped to verify image striping in the Advanced Technology Microwave Sounder (ATMS) upper atmosphere channels. The CRTM has also been used to characterize the ATMS radiometric bias and has led to the development of a complementary cloud screening method. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) 6h analysis data as inputs to the CRTM, we can statistically quantify the spectral bias for each field of view (FOV) of the Cross-track Infrared Sounder (CrIS). The CRTM is also a very useful tool for cross-sensor verifications. Using the double difference method, it can remove the biases caused by slight differences in the spectral response and geometric angles between two instruments. The CRTM helps our understanding on radiometric and spectral calibrations. It is the CRTM simulations that enable us to determine the root cause of the VIIRS shortwave infrared band image striping during daytime. The CRTM is operationally used at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) for weather forecasting and monitoring satellite radiance biases and standard deviation. This study also demonstrated the CRTM capability for Ozone Mapping and Profiler Suite (OMPS) radiance simulations. The first result showed a good agreement between the measurement and simulation. The CRTM for OMPS limb sensing, and Clouds and the Earth's Radiant Energy System (CERES) shortwave radiation and long-wave radiation flux simulation capability need to be extended. [Copyright &y& Elsevier]

Abstract: The thermal infrared (TIR) wavelength region has proved highly useful for remotely extracting important parameters of volcanic activity, such as the composition, texture, and temperature of either the surface or gas/aerosol emissions. However, each of these characteristics can vary within the area of one pixel of a remote sensing dataset, which ultimately affects the accuracy of the retrieval of these characteristics. For example, where multiple temperatures occur in a particular pixel, the derived emissivity spectrum and pixel-integrated brightness temperature for that pixel are inaccurate. We present a new approach for deconvolving thermally-mixed pixels in a day/night pair of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) scenes over Kilauea volcano, acquired during an active effusive phase in October 2006. The thermal deconvolution algorithm identifies thermally-mixed pixels and determines the multiple temperature components and their area, using data from the higher spatial resolution short wave infrared (SWIR) channels of ASTER. The effects of thermal mixing on the emissivity retrievals were quantified using a spectral deconvolution approach comparing the original to the thermally deconvolved data. The root mean squared (RMS) error improved slightly from 0.879 to 0.813, whereas the compositional end-members changed more dramatically (e.g., glass decreased from 70.2% to 49.3% and the vesicularity increased from 0.7% to 16.3%). The results provide more accurate temperature and emissivity data derived from ASTER data over thermally-elevated surfaces such as volcanoes and fires. This approach also serves as rapid means for accurately identifying sub-pixel temperatures, commonly obscured in low to medium spatial resolution orbital datasets. Moreover, it minimizes processing time, allowing critical information to be quickly disseminated. [Copyright &y& Elsevier]

Abstract: Microphytobenthic (MPB) biofilms are taxonomically mixed microbial assemblages (e.g. cyanobacteria, chlorophyta and diatoms) responsible for important estuarine ecosystem functions (e.g. nutrient fluxes, sediment cohesion, and primary productivity). MPB biofilms exhibit high spatial–temporal variation leading to high variability in collected data and requiring high sampling replication. Thus there is an increasing interest in developing remote sensing tools to quantify and describe MPB biofilms. The objectives of the current work were to investigate the effect of MPB taxonomic diversity on biofilm spectral reflectance signatures and to investigate the possibility of using second derivative analysis to estimate MPB chlorophyll a (as a proxy for biomass) and pigment composition in monospecific and mixed species biofilms. Two diatoms, Amphora coffeaeformis (Agardh) Kützing and Cylindrotheca closterium (Ehrenberg) W. Smith; one green algae, Dunaliella tertiolecta and one cyanobacteria, Spirulina platensis (Gomont) Geitler were used to construct artificial MPB biofilms on glass fiber filters. Biofilms with a range of different MPB concentrations and different taxonomic groups were constructed, their spectral reflectances were measured with a ASD FieldSpec 3FR spectroradiometer and pigments were extracted and measured by HPLC. The best relationships between pigment concentration and second derivative spectra (δδ) obtained with monospecific biofilms were: δδ548 for fucoxanthin, δδ590 for zeaxanthin, δδ650 for chlorophyll b and δδ675 for chlorophyll a. The relationship between δδ590 and zeaxanthin was probably the result of covariation with phycocyanin or phycoeritrin. δδ590 and δδ650 were strongly affected by the presence of other taxonomic groups in the mixed species biofilms and underestimated the respective pigments, while δδ548 and δδ675 could be used to estimate directly fucoxanthin and chlorophyll a in taxonomically mixed biofilms. The relationship between δδ675 and chlorophyll a was strongly affected by the presence of a second derivative double peak caused by chlorophyll fluorescence in the red region and was highly dependent on the smoothing amount applied to the original reflectance spectra. An alternative was proposed using the area of the second derivative peaks between 660 and 690nm (A[δδRED]). This index showed very good relationships with MPB chlorophyll a concentration and was almost insensitive to the amount of smoothing applied to the original spectra. [Copyright &y& Elsevier]

Abstract: This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD) was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We tested whether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was done with Fixed Rank Kriging (FRK) to represent the medium and long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 =0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 =0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale. [Copyright &y& Elsevier]

Abstract: Carbon dioxide is emitted from the combustion of fossil fuels and is an important contributor to anthropogenic climate change. Multiple current and planned satellite missions are designed to quantify atmospheric carbon dioxide concentrations on a global scale, but most of these sensors do not have the spatial resolution necessary to resolve point sources such as fossil fuel power plants. Airborne imaging spectrometer data, such as those from the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS), can have multiple, contiguous bands covering shortwave infrared (SWIR) absorption features produced by carbon dioxide. Therefore, high spatial resolution data from AVIRIS-like sensors may offer a means for detecting plumes and retrieving carbon dioxide concentrations for point source emissions. The objectives of this study include modeling minimum carbon dioxide anomalies detectable in AVIRIS data under different conditions and applying a Cluster-Tuned Matched Filter for detection of carbon dioxide plumes in simulated data and in AVIRIS images acquired over power plants. Radiative transfer simulations were used to model the residual radiance produced by increased absorption by carbon dioxide as concentration was elevated above background levels within a 0–500m layer. Carbon dioxide anomalies, surface reflectance, water vapor concentration, solar zenith angle, sensor height, and aerosol scattering were varied in simulation sets and the resulting residual radiance spectra were compared against noise equivalent delta radiance (NEdL) for the “classic” and “next generation” AVIRIS instruments. Sensitivity to carbon dioxide anomalies improved with increased surface reflectance and declined with increased water vapor concentration, solar zenith angle, sensor height, and aerosol scattering. Zero to 500m concentration anomalies as low as 100 parts per milion by volume (ppm) for AVIRIS C and 25ppm for AVIRIS NG produced residual radiance values that exceeded SWIR NEdL. Carbon dioxide concentrations modeled for a generic power plant emissions scenario using a plume dispersion model were combined with randomly-generated reflectance spectra to create simulated images with varying surface reflectance and NEdL. For these simulated images, true positive and false positive detection rates improved as background reflectance increased and as NEdL decreased. Apparent plumes were detected in all four AVIRIS C images acquired over power plants, although the characteristics of the plumes varied according to solar-plume-sensor geometry. Improvements in modeling may allow retrieval of plume concentration, providing a valuable means for quantifying point source emissions and a basis for comparison with column concentrations retrieved from in situ measurements and coarse resolution satellite data. [Copyright &y& Elsevier]

Abstract: West Nile (WNV) is now established in the continental United States with new human cases occurring annually in most states. Mosquitoes in the genus Culex are the primary vectors and exploit urban stagnant water and swimming pools as larval habitats. Public health surveys to monitor unmaintained pools typically rely on visual inspections of aerial imagery. This work demonstrates automated analysis of airborne imaging spectroscopy to assist Culex monitoring campaigns. We analyze an overflight of Fresno County, CA by the Airborne Visible Infrared Imaging Spectrometer instrument (AVIRIS), and compare the spectral information with a concurrent ground survey of swimming pools. Matched filter detection strategies reliably detect pools against a cluttered urban background. We also evaluate remotely sensed spectral markers of ecosystem characteristics related to larval colonization. We find that commonly used chlorophyll signatures accurately predict the probability of pool colonization by Culex larvae. These results suggest that AVIRIS spectral data provide sufficient information to remotely identify pools at risk for Culex colonization. [Copyright &y& Elsevier]

Abstract: Spectral mixture analysis (SMA) has been widely employed in analyzing urban environments, especially for estimating urban impervious surface distribution at the subpixel level. When implementing SMA, endmember selection is considered an important step, and an inappropriate endmember set could severely affect the accuracy of fractional land covers. Although different degrees of success have been achieved in existing SMA approaches, the paradox of endmember selection is still unsolved: theoretically “purest” endmembers that can be selected with relative ease do not always yield optimal results, while the selection of the most “representative” endmembers is very difficult with simple SMA. To address this problem, we propose a spatially adaptive SMA (SASMA) technique to automatically extract and synthesize the “most representative” endmembers for SMA through considering both between-class and within-class variations. In particular, we developed a classification tree method to automatically extract endmember candidates through incorporating spectral and spatial information. In addition, to mitigate the effect of within-class variation, we employed synthetic spectra of neighboring endmember candidates in a local search window as the most “representative” endmember signature. Results indicate that SASMA performs well in estimating subpixel impervious surface distribution with relatively high precision (mean absolute error of 8.50%, root mean square error of 15.25%, and R2 of 0.701) and small bias (systematic error of −0.93%). [Copyright &y& Elsevier]

Abstract: Spatiotemporal dimensionality refers to the continuum of spatial and temporal patterns in an image time series. Time-Space characterization refers to a way of representing this continuum of patterns as combinations of spatial and temporal constituents — with a minimum of assumptions about the forms of the patterns. Patterns can be related to processes through modeling. By combining characterization and modeling, two complementary analytical tools can be used together so that each resolves a key limitation of the other. This study describes a straightforward extension of Principal Component Analysis and Spectral Mixture Analysis to multitemporal imagery and illustrates how characterization of the dimensionality and eigenstructure of the data can inform modeling of the processes represented in the data. The relationships among spatiotemporal processes can be represented as combinations of temporal endmembers in a temporal feature space where the dimensions represent different components of the temporal patterns present in the data. The topology of the feature space and the processes being modeled together inform the selection of temporal endmembers and the structure of the model chosen to represent the processes. The dimensionality revealed by the characterization can also provide a partial solution to the problem of endmember variability. The characterization and modeling process is illustrated with the vegetation phenology of the Ganges–Brahmaputra delta using a MODIS vegetation index time series. Additional applications and limitations of Time-Space characterization and mixture modeling are further illustrated by comparing the eigenstructures and temporal feature spaces of Landsat vegetation fraction and DMSP-OLS night light time series. [Copyright &y& Elsevier]

Abstract: Forest canopy impairs the detection of snow underneath and optical interpretation methods tend to underestimate the snow coverage. The effect of measurement conditions on snow reflectance is considerably well understood, but the sensitivity of scene reflectance (when forest canopy present) to these properties is less examined. To investigate scene reflectance of a boreal forest, an Analytical Spectral Devices (ASD) Field Spec Pro JR was installed at the top of a 30-metre-high mast in Sodankylä, Northern Finland. Two sites – sparse forest and a forest opening – were monitored. The data consists of average reflectance spectra in the range 350–2500nm, as well as of simultaneously acquired digital images. The Moderate Resolution Imaging Spectroradiometer (MODIS) is widely used for the monitoring of snow‐covered area (SCA). Therefore, ASD spectra were resampled to the spectral characteristics of MODIS. Time series of normalised difference snow index (NDSI), normalised difference vegetation index (NDVI) and the reflectance at selected wavelengths were investigated in order to understand their behaviour in a snow-covered forest in spring. In order to discriminate between the effects of illumination conditions and the actual snow characteristics, reflectance spectra were measured both under direct and diffuse illumination. A specific focus was on the behaviour of NDSI, which is typically used in SCA retrieval – e.g. in MODIS snow mapping algorithms by NASA''s Goddard Space Flight Center (GSFC). We further modelled the scene reflectance as a function of surface reflectances considering areal proportions of Scots Pines, shadowed snow and directly illuminated snow. In the forest opening, NDSI was found to be relatively insensitive to illumination conditions, while in the forest stand it showed stronger dependence. This indicates that NDSI-based snow algorithms may provide a lower accuracy over forested areas. We demonstrated this by comparing the mast-observed indices and reflectances with the corresponding thresholds applied in MODIS snow mapping. The current thresholds led to underestimated snow cover in our test forest. It was also noticed that over the forest stand, variations in snow reflectance induced particularly great variation in the scene reflectance, even though visible snow-covered ground accounted for only half of the observed area as a result of the blocking effect of the trees. Comparison between the mast-borne and the modelled scene reflectance suggest that the surface reflectance observations could offer a feasible means to predict scene reflectance characteristics of snow-covered boreal forests observed by space-borne instruments. [Copyright &y& Elsevier]

Abstract: Airborne laser scanning (ALS) data with sparse point densities are increasingly used for forest growing stock estimations. The area-level point distributions derived by ALS are not considered informative on tree species, however, and the required information is typically produced using an additional data source, such as spectral images. We developed new volumetric and structural features (so called alpha shape metrics), hypothesizing that these could produce additional information on the species‐size variation as compared to the previously used ALS and image features. These metrics were tested in the prediction of species-specific, plot-level volume using the Most Similar Neighbor imputation method and a data set consisting of altogether 426 training and 142 validation field plots. The considered forest area was dominated by Scots pine, while Norway spruce and deciduous trees formed the other two species groups to be distinguished. The developed metrics improved the species-specific estimates by 13–30 or 2–4 percentage points compared to features based on ALS data alone or a combination of the ALS and image features, respectively. The metrics had a higher importance when the reference data insufficiently covered the species‐size variation within the area. Although the estimates produced using a combination of the ALS and image data had a superior accuracy compared to those produced by the ALS data alone, the results indicate that species-specific estimates may be further improved by developing computational features based on ALS data. [Copyright &y& Elsevier]

Abstract: Satellite-based remote monitoring programs of regional lake water quality largely have relied on Landsat Thematic Mapper (TM) owing to its long image archive, moderate spatial resolution (30m), and wide sensitivity in the visible portion of the electromagnetic spectrum, despite some notable limitations such as temporal resolution (i.e., 16days), data pre-processing requirements to improve data quality, and aging satellites. Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on Aqua/Terra platforms compensate for these shortcomings, although at the expense of spatial resolution. We developed and evaluated a remote monitoring protocol for water clarity of large lakes using MODIS 500m data and compared MODIS utility to Landsat-based methods. MODIS images captured during May–September 2001, 2004 and 2010 were analyzed with linear regression to identify the relationship between lake water clarity and satellite-measured surface reflectance. Correlations were strong (R²=0.72–0.94) throughout the study period; however, they were the most consistent in August, reflecting seasonally unstable lake conditions and inter-annual differences in algal productivity during the other months. The utility of MODIS data in remote water quality estimation lies in intra-annual monitoring of lake water clarity in inaccessible, large lakes, whereas Landsat is more appropriate for inter-annual, regional trend analyses of lakes ≥8ha. Model accuracy is improved when ancillary variables are included to reflect seasonal lake dynamics and weather patterns that influence lake clarity. The identification of landscape-scale drivers of regional water quality is a useful way to supplement satellite-based remote monitoring programs relying on spectral data alone. [Copyright &y& Elsevier]

Abstract: Considerable interest has been given to forming an international collaboration to develop a virtual moderate spatial resolution land observation constellation through aggregation of data sets from comparable national observatories such as the US Landsat, the Indian ResourceSat and related systems. This study explores the complementarity of India''s ResourceSat-1 Advanced Wide Field Sensor (AWiFS) with the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The analysis focuses on the comparative radiometry, geometry, and spectral properties of the two sensors. Two applied assessments of these data are also explored to examine the strengths and limitations of these alternate sources of moderate resolution land imagery with specific application domains. There are significant technical differences in these imaging systems including spectral band response, pixel dimensions, swath width, and radiometric resolution which produce differences in observation data sets. None of these differences was found to strongly limit comparable analyses in agricultural and forestry applications. Overall, we found that the AWiFS and Landsat TM/ETM+ imagery are comparable and in some ways complementary, particularly with respect to temporal repeat frequency. We have found that there are limits to our understanding of the AWiFS performance, for example, multi-camera design and stability of radiometric calibration over time, that leave some uncertainty that has been better addressed for Landsat through the Image Assessment System and related cross-sensor calibration studies. Such work still needs to be undertaken for AWiFS and similar observatories that may play roles in the Global Earth Observation System of Systems Land Surface Imaging Constellation. [Copyright &y& Elsevier]

Abstract: The aim of this study is to explore the capability of hyperspectral remote sensing to map areas affected by Red Dust (RD) pollution on soil and river water. The chosen study area contains residues from the extraction of aluminum from bauxite in a mud pond (i.e. the red mud), which contain high concentrations of contaminants such as heavy metals, so that special precaution must be taken when disposing to avoid RD pollution of land surface and river water resources. We perform laboratory analysis and in situ measurements of RD polluted samples to recognize the dominant minerals and identify the optical characteristics of the samples and their spectral features. Both an unsupervised methodology and a shape-based spectral analysis technique, using the significant RD reflectance spectral features, were applied to airborne hyperspectral remote sensing data to map the RD distribution on bare soils. Whereas, to assess a tool for the detection of RD in river waters a semi-analytical model for the radiative transfer in water bodies was used. The results of the spectral shape-based analysis and of the semi-analytical model used for this study were consistent with ground truth data and hence support the application of hyperspectral technologies for a rapid detection and mapping of industrial wastes, such as the RD. This information is suitable to support the development of effective intervention policies and monitoring programs. [Copyright &y& Elsevier]

Abstract: Rapid fire severity assessments are essential for timely post-fire rehabilitation responses. The Landsat differenced Normalized Burn Ratio (dNBR) has become the standard spectral index for assessing fire severity. The dNBR is generally calculated using the first available cloud-free post-fire image and a pre-fire image. This bi-temporal image differencing can be problematic due to image-to-image differences in illumination and phenology for example. We have developed an alternative index based on single date short-wave infrared (SWIR) and mid infrared (MIR) reflectance. In contrast with the (d)NBR, the SWIR–MIR index (SMI) is robust against scattering caused by smoke plumes over active fires allowing fire severity assessments to be generated when the area is still obscured by smoke. The SMI was generated using MODIS/ASTER (MASTER) airborne simulator data acquired over the recent 2011 Wallow fire in Arizona, USA. Simulation experiments showed that the SMI is more sensitive to char fractional cover than the NBR. We performed a regression analysis in which 92 Geo Composite Burn Index (GeoCBI) field plots of severity were randomly assigned to two equal halves, a training and a validation dataset, during ten separate trials. The SMI performed better to estimate GeoCBI values than the NBR (SMI R2 =0.69±0.06 and NBR R2 =0.60±0.06). The dNBR demonstrated slightly better performance than the SMI (dNBR R2 =0.71±0.05), however this difference was not significant at the 95% confidence level. Although no spaceborne sensors with pixel sizes smaller than 100m currently offer the possibility of a SWIR–MIR band combination, the airborne results illustrate the potential of this band combination for the remote sensing of post-fire effects. Such data will become available with the advent of the next generation satellite sensors, such as the planned spaceborne Hyperspectral Infrared Imager (HyspIRI). [Copyright &y& Elsevier]

Abstract: The TROPOspheric Monitoring Instrument (TROPOMI) will be part of ESA''s Sentinel-5 Precursor satellite platform scheduled for launch in late 2014. One of TROPOMI''s primary goals will be to accurately monitor methane (CH4) concentrations in the Earth''s atmosphere by measuring spectra of sunlight backscattered by the Earth''s surface and atmosphere in the shortwave-infrared (SWIR) spectral range. Absorption by CH4 along the lightpath allows for the spectroscopic retrieval of its atmospheric abundance given accurate knowledge of the lightpath. Scattering by aerosol and cirrus particles, however, can modify the lightpath substantially causing retrieval errors if not appropriately taken into account. We propose and evaluate a method to minimise these errors by simultaneously retrieving CH4 total column concentrations and scattering properties of the atmosphere. The method exploits observations by TROPOMI''s SWIR and near-infrared (NIR) channels and is based on a parameterisation of particle amount, height distribution, and microphysical properties. Retrieval performance is tested for a trial ensemble of simulated observations that cover a realistic range of aerosol and cirrus loaded scenes. For more than 90% of the trials, residual scattering induced CH4 errors are less than 1%. Retrievals with residual errors larger than 1% and non-convergent retrievals typically occur for difficult scenes with high particle load or very small surface albedo. [Copyright &y& Elsevier]

Abstract: Mapping requires a meaningful generalization of information. For vegetation maps, classification is frequently used to generalize the species composition of (semi-)natural plant assemblages. As an alternative to classification, ordination methods aim to extract major floristic gradients describing the prevailing compositional variation in a floristic data set as metric variables. This ability has been used previously to derive gradient maps of homogeneous landscapes that show plant species composition in continuous fields. In the present study, gradient mapping was used in a more heterogeneous landscape with intricate environmental gradients and higher variation in vegetation physiognomy. Since established ordination methods may have difficulties to cope with the highly variable plant species composition, we tested the novel method Isometric Feature Mapping (Isomap) against conventional methods (Detrended Correspondence Analysis and Nonmetric Multidimensional Scaling). The resulting floristic gradients were related to hyperspectral imagery (HyMap) using partial least squares regression (PLSR) and subsequently mapped. Prediction uncertainties are provided as additional map. Isomap was able to preserve 74% of the original variation inherent to the floristic data set in a three-dimensional solution. This was considerably more than the established techniques achieved. The PLSR models for the floristic gradients extracted with Isomap showed model fits ranging from R²=0.59 to R²=0.73 in calibration and from R²=0.55 to R²=0.69 in tenfold cross-validation. The resulting gradient map provides detailed information on compositional vegetation patterns. [Copyright &y& Elsevier]

Abstract: We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf. [Copyright &y& Elsevier]

Abstract: Aerial survey provides the user with great flexibility in terms of the geometry of sensing and the timing of measurements, but mosaicking individual aerial images to produce an extensive coverage remains a problem. Empirical methods based on normalising individual images to a common standard are used widely to create visually acceptable mosaics. However, the effect of these methods on quantitative estimation of land surface properties is unknown. An existing method for atmospherically correcting an aerial image mosaic involves fitting a regression model using pixels from the overlapping edges of adjacent flightlines. Here, we demonstrate a new method of atmospherically correcting an aerial image mosaic, based on use of an additional orthogonal flightline. The two methods were compared by using the two image mosaics to calculate vegetation indices (NDVI, SAVI, ARVI), which were then used to predict leaf area index, which was known in detail from ground survey. The second method was found to have lower uncertainty for all three vegetation indices tested. ARVI was found to be the most robust of the three when applied across multiple flightlines, regardless of the method of atmospheric correction. [Copyright &y& Elsevier]

Abstract: Directional microwave scattering, as investigated in this study, is characterized by a strong and narrow scattering pattern that varies strongly for only minor aspect angle changes. As was noted already in the past directional scattering is relevant for applications and cannot just be ignored. The overall objective of our work was to better understand directional scattering. Suited space- and airborne SAR data over several agricultural sites together with related in-situ information were collected for this purpose. Directional scattering was identified by comparison of backscattering acquired with only slightly different aspect angles as available from ERS–ENVISAT pairs with significantly different Doppler Centroids or by comparing azimuth spectrum sub-bands with slightly different Doppler Centroid. Major progress achieved in this work includes the much improved experimental evidence available and significant improvements in the understanding of the scatter phenomenon through the developed scatter model. Good progress was also made in the detection of directional scattering. [Copyright &y& Elsevier]

Abstract: Desert spring ecosystems provide water resources essential for sustaining wildlife, plants, and humans inhabiting arid regions of the world. Disturbance processes in desert spring ecosystems are likely important but have not been well studied. Documentation of historic wildfires in these often remote areas has been inconsistent and proxy records are often not available. Remote sensing methods have been used in other environments to gain information about fires that have occurred over recent decades, but these methods have not been tested in desert spring environments. The differenced normalized burn ratio (dNBR) is the most commonly used method for delineating fire perimeters and burn severity mosaics, although another method, differenced linear spectral unmixing (dSMA), may produce more accurate results in heterogeneous desert spring ecosystems due to its ability to detect changes at the sub-pixel scale. This study compared dNBR and dSMA using field observations of burn presence and fire severity for two recent wildfires. The dNBR method outperformed dSMA, but required some post-processing manipulation to reduce errors of commission. The dNBR classification correctly indentified burned areas with 86% accuracy (3% omission error, 19% commission error) and classified fire severity with 76% accuracy. Misclassification errors were most common in dune and mesquite bosque/meadow land cover types (mean misclassification rate=36%). Nine of the fifteen wildfires reported to have occurred in the study site were successfully identified, with five of the unidentified fires having reported sizes of less than one hectare. Additional refinement of remote sensing methods is necessary to better distinguish small (<5ha) burned areas from areas of change resulting from soil moisture fluctuation and other short-term shifts in background conditions. [Copyright &y& Elsevier]

Abstract: Over the past decade, the role of multiangle remote sensing has been central to the development of algorithms for the retrieval of global land surface properties including models of the bidirectional reflectance distribution function (BRDF), albedo, land cover/dynamics, burned area extent, as well as other key surface biophysical quantities impacted by the anisotropic reflectance characteristics of vegetation. In this study, a new retrieval strategy for fine-to-moderate resolution multiangle observations was developed, based on the operational sequence used to retrieve the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 reflectance and BRDF/albedo products. The algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model to provide estimates of intrinsic albedo (i.e., directional-hemispherical reflectance and bihemispherical reflectance), model parameters describing the BRDF, and extensive quality assurance information. The new retrieval strategy was applied to NASA''s Cloud Absorption Radiometer (CAR) data acquired during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC) over the well-instrumented Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site in Oklahoma, USA. For the case analyzed, we obtained ~1.6million individual surface bidirectional reflectance factor (BRF) retrievals, from nadir to 75° off-nadir, and at spatial resolutions ranging from 3m to 500m. This unique dataset was used to examine the interaction of the spatial and angular characteristics of a mixed agricultural landscape; and provided the basis for detailed assessments of: (1) the use of a land cover type-specific a priori knowledge in kernel-driven BRDF model inversions; (2) the interaction between surface reflectance anisotropy and instrument spatial resolution; and (3) the uncertainties that arise when sub-pixel differences in the BRDF are aggregated to a moderate resolution satellite pixel. Results offer empirical evidence concerning the influence of scale and spatial heterogeneity in kernel-driven BRDF models; providing potential new insights into the behavior and characteristics of different surface radiative properties related to land/use cover change and vegetation structure. [Copyright &y& Elsevier]

Abstract: The Neyriz ophiolite occurs along the Zagros suture zone in SW Iran, and is part of a 3000-km obduction belt thrusting over the edge of the Arabian continent during the late Cretaceous. This complex typically consists of altered dunites and peridotites, layered and massive gabbros, sheeted dykes and pillow lavas, and a thick sequence of radiolarites. Reflectance and emittance spectra of Neyriz ophiolite rock samples were measured in the laboratory and their spectra were used as endmembers in a spectral feature fitting (SFF) algorithm. Laboratory spectral reflectance measurements of field samples showed that in the visible through shortwave infrared (VNIR–SWIR) wavelength region the ultramafic and gabbroic rocks are characterized by ferrous-iron and Fe, Mgal features, and the pillow lavas and radiolarites are characterized by spectral features of ferric-iron and Alaboratory spectral emittance spectra also revealed a wide wavelength range of Sil features for the ophiolite rock units. After continuum removal of the spectra, the SFF classification method was applied to the VNIR+SWIR 9-band stack, and to the 11-band data set of SWIR and TIR data sets of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, using field spectra as training sets for evaluating the potential of these data sets in discriminating ophiolite rock units. Output results were compared with the geological map of the area and field observations, and were assessed by the use of confusion matrices. The assessment showed, in terms of kappa coefficient, that the SFF classification method with continuum removal applied to the SWIR data achieved excellent results, which were distinctively better than those obtained using VNIR+SWIR data and TIR data alone. [Copyright &y& Elsevier]

Abstract: Leaf spectroscopy may be useful for tropical species discrimination, but few studies have provided an understanding of the spectral separability of species or how leaf spectroscopy scales to the canopy level relevant to mapping. Here we report on a study to classify humid tropical forest canopy species using field-measured leaf optical properties with leaf and canopy radiative transfer models. The experimental dataset included 188 canopy species collected in humid tropical forests of Hawaii. The leaf optical model PROSPECT-5 was used to simulate the leaf spectra of each species, which was used to train a classifier based on Linear Discriminant Analysis, and a canopy radiative transfer model 4SAIL2 to scale leaf measurements to the canopy level. The relationship linking classification accuracy at the leaf level to biodiversity showed an asymptotic trend reaching a maximum error of 47% when applied to the entire 188 species experimental dataset, and 56% when a simulated dataset showing amplified within-species spectral variability was used, suggesting uniqueness of the spectral signature for a significant proportion of species under study. The maximum error in canopy-level species classification was higher than leaf-level classification: 55% when canopy structure was held constant, and 64% with varying and unknown canopy structure. However, when classifying fewer species at a time, errors dropped considerably; for example, 20 species can be classified to 82–88% accuracy. These results highlight the potential of imaging spectroscopy to provide species discrimination in high-diversity, humid tropical forests. [Copyright &y& Elsevier]

Abstract: Thermal remote sensing studies of actively burning wildfires are usually based on the detection of Planckian energy emissions in the MIR (3–5μm), LWIR (8–14μm) and/or SWIR (1.0–2.5μm) spectral regions. However, vegetation also contains a series of trace elements which present unique narrowband spectral emission lines in the visible and near infrared wavelength range when the biomass is heated to high temperatures during the process of flaming combustion. These spectral lines can be discriminated by detector systems that are less costly than the longer wavelength, actively cooled instruments more typically used in EO-based active fire studies. The main trace element resulting in the appearance of spectral emission lines appears to be potassium (K), with features at 766.5nm and 769.9nm. Here we study K-emission line spectral signature in laboratory scale fires using a field spectrometer, at a series of moderately-sized woodland and shrubland fires using airborne imagery from a new compact hyperspectral imager (HYPER–SIM.GA) operating at a relatively fine spectral sampling interval (1.2nm), and at large open wildfires using the EO-1 satellite''s Hyperion sensor. We derive a metric based on band differencing of the spectral signal both close to and outside of the K-line region in order to quantify the magnitude of the K-emission signature, and find that variations in this metric appear to track quite well with the commonly used measures of fire radiometric temperature and fire radiative power (FRP). We find that substantial flaming activity is required to generate a potassium emission signature, but that once present this can be detected using airborne remote sensing even through a substantial smoke layer that apparently obscures fire across the remainder of the VIS spectral range. Being specific to flaming combustion, detection of the K-emission line signature could prove useful in refining estimates of the gases released in open wildfires, since trace gas emission factors can vary substantially between flaming and smouldering stages. Finally, we demonstrate the first identification of the K-emission line signature from space using the EO-1 Hyperion instrument, but find it detectable only in certain instances. We conclude that a finer spectral and spatial resolution than that offered by Hyperion is required for improved detection performance. Nevertheless, our results point to the potential effectiveness of airborne and spaceborne K-emission signature detection as a complement to the more common thermal remote sensing approaches to wildfire detection and analysis. Sensors targeting this application should consider careful placement of the measurement wavelengths around the location of the K-line wavelengths, in part to minimise influences from the nearby oxygen A-band features. [Copyright &y& Elsevier]

Abstract: The use of satellites to monitor the color of the ocean requires effective removal of the atmospheric signal. This can be performed by extrapolating the aerosol optical properties in the visible from the near-infrared (NIR) spectral region assuming that the seawater is totally absorbant in this latter part of the spectrum. However, the non-negligible water-leaving radiance in the NIR which is characteristic of turbid waters may lead to an overestimate of the atmospheric radiance in the whole visible spectrum with increasing severity at shorter wavelengths. This may result in significant errors, if not complete failure, of various algorithms for the retrieval of chlorophyll-a concentration, inherent optical properties and biogeochemical parameters of surface waters. This paper presents results of an inter-comparison study of three methods that compensate for NIR water-leaving radiances and that are based on very different hypothesis: 1) the standard SeaWiFS algorithm (Stumpf et al., 2003; Bailey et al., 2010) based on a bio-optical model and an iterative process; 2) the algorithm developed by Ruddick et al. (2000) based on the spatial homogeneity of the NIR ratios of the aerosol and water-leaving radiances; and 3) the algorithm of Kuchinke et al. (2009) based on a fully coupled atmosphere–ocean spectral optimization inversion. They are compared using normalized water-leaving radiance nLw in the visible. The reference source for comparison is ground-based measurements from three AERONET-Ocean Color sites, one in the Adriatic Sea and two in the East Coast of USA. Based on the matchup exercise, the best overall estimates of the nLw are obtained with the latest SeaWiFS standard algorithm version with relative error varying from 14.97% to 35.27% for λ=490nm and λ=670nm respectively. The least accurate estimates are given by the algorithm of Ruddick, the relative errors being between 16.36% and 42.92% for λ=490nm and λ=412nm, respectively. The algorithm of Kuchinke appears to be the most accurate algorithm at 412nm (30.02%), 510 (15.54%) and 670nm (32.32%) using its default optimization and bio-optical model coefficient settings. Similar conclusions are obtained for the aerosol optical properties (aerosol optical thickness τ(865) and the Ångström exponent, α(510, 865)). Those parameters are retrieved more accurately with the SeaWiFS standard algorithm (relative error of 33% and 54.15% for τ(865) and α(510, 865)). A detailed analysis of the hypotheses of the methods is given for explaining the differences between the algorithms. The determination of the aerosol parameters is critical for the algorithm of Ruddick et al. (2000) while the bio-optical model is critical for the algorithm of Stumpf et al. (2003) utilized in the standard SeaWiFS atmospheric correction and both aerosol and bio-optical model for the coupled atmospheric–ocean algorithm of Kuchinke. The Kuchinke algorithm presents model aerosol-size distributions that differ from real aerosol-size distribution pertaining to the measurements. In conclusion, the results show that for the given atmospheric and oceanic conditions of this study, the SeaWiFS atmospheric correction algorithm is most appropriate for estimating the marine and aerosol parameters in the given turbid waters regions. [Copyright &y& Elsevier]

Abstract: Discrete changes in forest abundance, distribution, and productivity are readily detectable using a number of remotely sensed data sources; however, continuous changes such as growth and succession processes are more difficult to monitor. In this research we explore the potential of spectral trajectories generated from a 35-year (1973–2008) time-series of Landsat imagery to characterize change processes in a dynamic forest environment in northwestern Alberta, Canada. We propose a method of hierarchical spatio-temporal segmentation that enables the characterization of change processes that are spatially diffuse and temporally imprecise. Calibrated imagery from Landsat sensors are radiometrically normalized and two metrics derived from the Tasseled Cap Transformation components, greenness and brightness, are used to generate the Tasseled Cap Angle (TCA). The TCA is a measure of the proportion of vegetation to non-vegetation (the occupation state), and its derivative, the Process Indicator (PI), is a measure of change in this proportion through time. These indices condense information from the visible and near-infrared wavelengths, and facilitate lengthy time series analysis of forest landscape change using data from all Landsat sensors. A combination of the original TCA and its derivative sequence are input to a three level hierarchical segmentation process with the highest and lowest levels defining homogeneous objects at the initial and final date, and the intermediate level identifying trajectories with similar change processes. The development through time of the TCA and PI are described, and the spatial and temporal associations of processes are statistically assessed using the Moran''s Index. A full range of change types were identified on the landscape, from stand replacing disturbances to more subtle growth and succession processes. Results indicate that the study area is in a constant state of change, and maintains a high average proportion of vegetation to non-vegetation. The amount of total landscape modified per decade increased from 18% and 14% in the 1970s and 1980s respectively, to more than 30% and 33% in the 1990s and 2000s. On average, the proportion of vegetation to non-vegetation was increasing prior to 1981, decreasing between 1981 and 1997, and increasing post-1997. There was a high degree of spatial autocorrelation amongst change processes, with a maximum Moran''s I of 0.79 in 1973; landscape change became more spatially disperse and widespread after 1981. Temporal correlation of change processes was observed locally, with the period 1990–1995 having the most persistent change. [Copyright &y& Elsevier]

Abstract: A high resolution urban thermal sharpener (HUTS) was developed that increases the resolution of thermal infrared (TIR) data to that of visible and near infrared (VNIR) data by fitting the relationship between radiometric surface temperature, normalized difference vegetation index (NDVI) and surface albedo (α). HUTS was applied to TIR data aggregated to 90m to represent a satellite acquired dataset and validated against the measured 10m data from an aircraft over San Juan, Puerto Rico. HUTS sharpening reduced the root mean square error of surface temperature at the high resolution by 17% compared to no sharpening and outperformed other sharpening methods. HUTS is proposed as a useful tool to study urban meteorology and climatology at the microscale using ASTER satellite data. [Copyright &y& Elsevier]

Abstract: The composite nature of remotely sensed spectral information often masks diagnostic spectral features and hampers the detailed identification and mapping of targeted constituents of the earth''s surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent spectral components or spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative mixture analysis cycle, (ii) the selection of a subset of stable spectral features, (iii) the spectral weighting of bands, (iv) spectral signal transformations and (v) the use of radiative transfer models in a mixture analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA. [Copyright &y& Elsevier]

Abstract: This paper assessed the incorporation of road structural information in the classification process of impervious surface areas. A multi-process classification model was adopted and it consisted of an a priori classifier and an a posteriori classifier. The role of the a priori classifier was to classify the relatively simple portions of the image. This partial classification acted as the basis for the production of linear features using an iterative Radon transform. Spatial statistics derived from the linear features led to road structural intermediate inputs (RSIIs) (for example, distance to the closest segment endpoint). RSIIs were integrated with spectral information on the remaining unclassified pixels and an assessment was done to evaluate whether they would improve a binary impervious classification task. The experimental results on a 2006 Landsat ETM+ image suggested that classification accuracy improved by 8.4% for the portion of the dataset classified with the a posteriori classifier and led to an improvement of 3.2% over the entire dataset. In addition, a more challenging and wide-reaching hypothesis was tested, namely whether RSIIs could completely replace spectral information in portions of the image instead of complementing it. Exclusive use of RSIIs matched or improved classification accuracy obtained solely from spectral information, even when more than half of the validation dataset was forwarded to the a posteriori classifier. This finding offers an important contribution to the remote sensing community, since the proposed methodology handles the missing spectral information problem through exclusive analysis of the given degraded image; no external information, such as spectral information from other times and/or vector data, is needed. [Copyright &y& Elsevier]

Abstract: The absorption feature approach was used in CHRIS multiangular hyperspectral data in order to investigate its potential for ecosystem remote sensing. For that purpose, CHRIS images in mode 1 were acquired throughout a two-year period for a Mediterranean ecosystem dominated by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident in situ Leaf spectra and ecophysiological measurements (Leaf Area Index, leaf pigment content and leaf water potential) were conducted. After data preprocessing, absorption feature information was calculated for both CHRIS and Leaf spectra for the whole spectrum. Three common characteristic absorption features within the spectral areas 450–550nm, 550–750nm and 900–1000nm were detected. Each spectral area was then examined separately and four characteristic parameters were calculated that described the pattern, magnitude and position of the maximum absorption. Correlations between CHRIS and Leaf spectra for each date and viewing angle (VA) were then conducted. All correlations, either on full continuum removed spectra or on spectral areas, showed high coefficients of determination, especially (i) in higher observation angles (VA +55), (ii) during the wet season and (iii) in strong absorptions such as the “red absorption”. Subsequently, correlations between CHRIS and Leaf absorption parameters of selected spectral areas with field-measured ecophysiological parameters were examined. Ecophysiological parameters proved to be highly correlated to CHRIS and Leaf absorption parameters in magnitude and/or pattern of the absorption feature and less in wavelength of the maximum absorption. CHRIS VAs +/− 36 showed the highest correlations although the type of relation, linear or nonlinear, was not conclusive. Finally, a first comparison between narrowband spectral indices and absorption features in correlations with ecophysiological parameters showed that both methods provide significant and comparable results, with oblique angles showing best performance. However, ecophysiological parameters are generally better predicted linearly by narrowband spectral indices issued from CHRIS, with most significant differences appearing on pigments absorbing mainly within 450–550nm. [Copyright &y& Elsevier]

Abstract: The gravimetric water content (GWC, %), a commonly used measure of leaf water content, describes the ratio of water to dry matter for each individual leaf. To date, the relationship between spectral reflectance and GWC in leaves is poorly understood due to the confounding effects of unpredictably varying water and dry matter ratios on spectral response. Few studies have attempted to estimate GWC from leaf reflectance spectra, particularly for a variety of species. This paper investigates the spectroscopic estimation of leaf GWC using continuous wavelet analysis applied to the reflectance spectra (350–2500nm) of 265 leaf samples from 47 species observed in tropical forests of Panama. A continuous wavelet transform was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength and scale. Linear relationships were built between wavelet power and GWC expressed as a function of dry mass (LWCD) and fresh mass (LWCF) in order to identify wavelet features (coefficients) that are most sensitive to changes in GWC. The derived wavelet features were then compared to three established spectral indices used to estimate GWC across a wide range of species. Eight wavelet features observed between 1300 and 2500nm provided strong correlations with LWCD, though correlations between spectral indices and leaf GWC were poor. In particular, two features captured amplitude variations in the broad shape of the reflectance spectra and three features captured variations in the shape and depth of dry matter (e.g., protein, lignin, cellulose) absorptions centered near 1730 and 2100nm. The eight wavelet features used to predict LWCD and LWCF were not significantly different; however, predictive models used to determine LWCD and LWCF differed. The most accurate estimates of LWCD and LWCF obtained from a single wavelet feature showed root mean square errors (RMSEs) of 28.34% (R 2 =0.62) and 4.86% (R 2 =0.69), respectively. Models using a combination of features resulted in a noticeable improvement predicting LWCD and LWCF with RMSEs of 26.04% (R 2 =0.71) and 4.34% (R 2 =0.75), respectively. These results provide new insights into the role of dry matter absorption features in the shortwave infrared (SWIR) spectral region for the accurate spectral estimation of LWCD and LWCF. This emerging spectral analytical approach can be applied to other complex datasets including a broad range of species, and may be adapted to estimate basic leaf biochemical elements such as nitrogen, chlorophyll, cellulose, and lignin. [Copyright &y& Elsevier]

Abstract: To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer''s and user''s accuracies for most species ranging from >52–95.4 and >63–87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer''s (+5.1–11.6%) and user''s (+8.4–18.8%) accuracies. McNemar''s tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant (p <0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology. [Copyright &y& Elsevier]

Abstract: The land cover fractions (LCFs) and spectral reflectance of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare soil were measured at 58 sites in semi-arid and arid regions of Mongolia in the summers of 2005 and 2006. These data sets allowed a detailed assessment of the impact of measurement geometry as represented by the solar zenith angle θ s, sensor view zenith angle θ v and azimuth view angle ϕ in the estimation of LCF values by means of the spectral unmixing model (SUM). The bidirectional distribution function (BRDF) was fitted to the reflectance data and then used to produce reflectance at various measurement geometries. LCFs from these reflectance data for a given combination of θ s, θ v, and ϕ were compared with visually determined LCFs. It was found that θ s in the range of 30–45° produced a better agreement of LCFs. For θ v, the agreement is not very sensitive to the choice of angle for the range 30–70°, although θ v =50° showed a slightly better performance. The azimuth view angle does not have strong influences to the LCF estimation, except for the case of ϕ =180° (view toward the sun), which does not allow precise fitting of BRDF function over a tall vegetation site. Overall, this study verified the results of earlier studies obtained mostly for the American continents that SUM is capable of producing LCF estimates accurately and also found that its accuracy was, in general, much better than that by the more traditional approach of the supervised classification method (SCM) applied to images of a digital camera. [ABSTRACT FROM AUTHOR]

Abstract: The NASA Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODIS-Aqua) provides a viable data stream for operational water quality monitoring of Chesapeake Bay. Marine geophysical products from MODIS-Aqua depend on the efficacy of the atmospheric correction process, which can be problematic in coastal environments. The operational atmospheric correction algorithm for MODIS-Aqua requires an assumption of negligible near-infrared water-leaving radiance, nL w(NIR). This assumption progressively degrades with increasing turbidity and, as such, methods exist to account for non-negligible nL w(NIR) within the atmospheric correction process or to use alternate radiometric bands where the assumption is satisfied, such as those positioned within shortwave infrared (SWIR) region of the spectrum. We evaluated a decade-long time-series of nL w(λ) from MODIS-Aqua in Chesapeake Bay derived using NIR and SWIR bands for atmospheric correction. Low signal-to-noise ratios (SNR) for the SWIR bands of MODIS-Aqua added noise errors to the derived radiances, which produced broad, flat frequency distributions of nL w(λ) relative to those produced using the NIR bands. The SWIR approach produced an increased number of negative nL w(λ) and decreased sample size relative to the NIR approach. Revised vicarious calibration and regional tuning of the scheme to switch between the NIR and SWIR approaches may improve retrievals in Chesapeake Bay, however, poor SNR values for the MODIS-Aqua SWIR bands remain the primary deficiency of the SWIR-based atmospheric correction approach. [ABSTRACT FROM AUTHOR]

Abstract: The ground data used as a reference in the validation of land cover change products are often not an ideal gold standard but degraded by error. The effects of ground reference data error on the accuracy of land cover change detection and the accuracy of estimates of the extent of change were evaluated. Twelve data sets were simulated to allow the exploration of the impacts of a spectrum of ground data imperfections on the estimation of the producer''s and user''s accuracy of change as well as of change extent. Simulated data were used since this ensured that the actual properties of the data were known and to exclude effects due to other sources of ground reference data error; although the impacts of simulated reference data error on two real confusion matrices are also illustrated. The imperfections evaluated ranged from the inclusion of small amounts of known error into the ground reference data through to the extreme situation in which ground data were absent. The results show that even small amounts of error in the ground reference data can introduce large error into studies of land cover change by remote sensing and reinforce the desire to avoid the expression ground truth as this might imply that the data are a gold standard reference. The effect of reference data imperfections was dependent on the degree of association between the errors in the cross-tabulated data sets. For example, in the scenarios investigated, a 10% error in the reference data set introduced an underestimation of the producer''s accuracy of 18.5% if the errors were independent but an over-estimation of the producer''s accuracy of 12.3% if the errors were correlated. The magnitude of the mis-estimation of the producer''s accuracy was also a function of the amount of change and greatest at low levels of change. The amount of land cover change estimated also varied greatly as a function of ground reference data error. Some possible methods to reduce or even remove the impacts of ground reference data error were illustrated. These ranged from simple algebraic means to estimate the actual values of accuracy and change extent if the imperfections were known through to a latent class analysis that allowed the assessment of classification accuracy and estimation of change extent without the use of ground reference data if the underlying model is defined appropriately. [Copyright &y& Elsevier]