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The penetration ability of Light Detection and Ranging (LiDAR) pulses into vegetation cover makes it a valuable tool in forest inventory. Extraction of a Digital Terrain Model (DTM) using LiDAR data is a challenging topic, especially in steep and complex terrains with forest canopy. This paper presents some approaches for ground filtering and interpolating the point cloud to generate DTM in forested terrains. Interpolating the points in dense forests that have high attitude variation is very difficult and make the popular interpolation methods unsatisfactory. This paper proposed a modified Invasive Weed Optimization (IWO) method for finding the optimized coefficients of the polynomial interpolation method. This method had a good performance with 0.210 mm RMSE value in the forested terrains. The interpolated point cloud was used as the input of the proposed ground filtering method for detecting the non-ground pixels. The proposed ground filtering method was structured with two main sections including, iterative geodesic morphology and scan labeling. In the iterative geodesic morphology, some geometric and structural parameters were introduced to investigate the quality of extracted points in each iteration. The scan labeling searched the data pixel by pixel in four directions and labeled the pixels with the high slope value in all directions. The non-ground pixels were obtained by integrating the result of iterative geodesic and scan labeling. Assessment of the ground filtering results using the International Society for Photogrammetry and Remote Sensing (ISPRS) showed 3.92% total error compared to the other reported algorithms. This demonstrated the ability of the proposed approach in recognition of the non-ground pixels. The extracted pixels were removed and the DTM was generated by filling the gaps using the proposed IWO and polynomial interpolation. Some forested regions with various characteristics such as sparse and dense trees on the hills and steep slopes were utilized to evaluate the accuracy of the generated DTM. The computed RMSE in the test areas was 0.463 m, on average, which was acceptable for the complex and forested terrains. [ABSTRACT FROM AUTHOR]

The FLuorescence EXplorer (FLEX) satellite mission, selected as ESA's 8th Earth Explorer, has been designed for the measurement of sun-induced fluorescence ( F ) spectra emitted by plants. This will be accomplished through a multi-sensor approach by placing it in a common orbit in tandem with the Sentinel-3 (S3) mission, which will have two optical sensors on board, OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) to complement FLEX. These S3 instruments will be used in combination with the imaging spectrometers on board FLEX to provide data useful for atmospheric correction of FLEX data. However, a fully synergetic approach, i.e. by exploiting the spectral and directional information from all tandem mission instruments together, is an attractive alternative which is explored in this paper. By employing all combined top-of-atmosphere (TOA) spectral radiance data, one can (i) characterize the relevant optical properties of the atmosphere, (ii) retrieve biophysical canopy properties including the associated reflectance anisotropy, and (iii) retrieve a more accurate and consistent canopy F . Regarding retrieval methods, Fraunhofer Line Depth (FLD) and Spectral Fitting (SF) are well-known techniques applied to hyperspectral data. Both methods depend on a high spectral resolution and assume a Lambertian (isotropic) canopy reflectance. However, most vegetation canopies are non-Lambertian. This implies that, in particular when ignoring the anisotropic surface reflection, substantial retrieval errors can occur due to the interaction between atmospheric absorption bands and surface reflectance anisotropy. In this paper, a novel method based on spectral radiative transfer (RT) modeling is proposed, in which coupled RT models are used to simulate TOA radiance spectra. These are then matched with ‘measured’ spectra in order to retrieve surface fluorescence, along with a suite of biophysical parameters, by model inversion through optimization. By applying coupled RT models of the soil-leaf-canopy and the surface-atmosphere systems, TOA radiance spectra can be simulated for all optical sensors of this tandem mission. In this way, complex effects due to surface reflectance anisotropy and the spectral sampling by the various instruments, which are difficult to compensate for in the end products, are properly taken into account by their incorporation in the forward modeling. Next, by model inversion of TOA radiance data via optimization, the most accurate F retrievals can be achieved in a consistent manner, along with important canopy level biophysical parameters that may help interpret the F spectrum, such as chlorophyll content and leaf area index (LAI). The potential of this approach has been explored in a numerical experiment, and the results are presented in this paper. We find that, with the assumed well-characterized and plausible FLEX/S3 instrument performances, the simultaneous retrieval of biophysical canopy parameters and F spectra would be possible with a remarkable accuracy, provided the correct atmospheric characterization is available. [ABSTRACT FROM AUTHOR]

Reliable detection of clouds is a critical pre-processing step in optical satellite based remote sensing. Currently, most methods are based on classifying invidual pixels from their spectral signatures, therefore they do not incorporate the spatial patterns. This often leads to misclassifications of highly reflective surfaces, such as human made structures or snow/ice. Multi-temporal methods can be used to alleviate this problem, but these methods introduce new problems, such as the need of a cloud-free image of the scene. In this paper, we introduce the Remote Sensing Network (RS-Net), a deep learning model for detection of clouds in optical satellite imagery, based on the U-net architecture. The model is trained and evaluated using the Landsat 8 Biome and SPARCS datasets, and it shows state-of-the-art performance, especially over biomes with hardly distinguishable scenery, such as clouds over snowy and icy regions. In particular, the performance of the model that uses only the RGB bands is significantly improved, showing promising results for cloud detection with smaller satellites with limited multi-spectral capabilities. Furthermore, we show how training the RS-Net models on data from an existing cloud masking method, which are treated as noisy data, leads to increased performance compared to the original method. This is validated by using the Fmask algorithm to annotate the Landsat 8 datasets, and then use these annotations as training data for regularized RS-Net models, which then show improved performance compared to the Fmask algorithm. Finally, the classification time of a full Landsat 8 product is 18.0 ± 2.4 s for the largest RS-Net model, thereby making it suitable for production environments. • This algorithm show high performance, even when limited to RGB bands. • The processing time is low due to GPU based calculations. • Cloud detection performance over snow/ice is significantly improved. • This algorithm can be trained on the output of earlier cloud detection algorithms. [ABSTRACT FROM AUTHOR]

Satellite data are increasingly used to provide observation-based estimates of the effects of aerosols on climate. The Aerosol-cci project, part of the European Space Agency's Climate Change Initiative (CCI), was designed to provide essential climate variables for aerosols from satellite data. Eight algorithms, developed for the retrieval of aerosol properties using data from AATSR (4), MERIS (3) and POLDER, were evaluated to determine their suitability for climate studies. The primary result from each of these algorithms is the aerosol optical depth (AOD) at several wavelengths, together with the Ångström exponent (AE) which describes the spectral variation of the AOD for a given wavelength pair. Other aerosol parameters which are possibly retrieved from satellite observations are not considered in this paper. The AOD and AE (AE only for Level 2) were evaluated against independent collocated observations from the ground-based AERONET sun photometer network and against “reference” satellite data provided by MODIS and MISR. Tools used for the evaluation were developed for daily products as produced by the retrieval with a spatial resolution of 10 × 10 km 2 (Level 2) and daily or monthly aggregates (Level 3). These tools include statistics for L2 and L3 products compared with AERONET, as well as scoring based on spatial and temporal correlations. In this paper we describe their use in a round robin (RR) evaluation of four months of data, one month for each season in 2008. The amount of data was restricted to only four months because of the large effort made to improve the algorithms, and to evaluate the improvement and current status, before larger data sets will be processed. Evaluation criteria are discussed. Results presented show the current status of the European aerosol algorithms in comparison to both AERONET and MODIS and MISR data. The comparison leads to a preliminary conclusion that the scores are similar, including those for the references, but the coverage of AATSR needs to be enhanced and further improvements are possible for most algorithms. None of the algorithms, including the references, outperforms all others everywhere. AATSR data can be used for the retrieval of AOD and AE over land and ocean. PARASOL and one of the MERIS algorithms have been evaluated over ocean only and both algorithms provide good results. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is an important technique for remote sensing image exploitation. It aims to decompose a mixed pixel into a collection of spectrally pure components (called endmembers ), and their corresponding proportions (called fractional abundances ). In recent years, many studies have revealed that unmixing using spectral information alone does not sufficiently incorporate the spatial information in the remotely sensed hyperspectral image, as the pixels are treated as isolated entities without taking into account the existing local correlation among them. To address this issue, several spatial preprocessing methods have been developed to include spatial information in the spectral unmixing process. In this paper, we present a new spatial preprocessing method which presents several advantages over existing methods. The proposed method is derived from the Simple Linear Iterative Clustering (SLIC) method, which adapts the global search scope of the clustering into local regions. As a result, the spatial correlation and the spectral similarity are intrinsically incorporated at the clustering step, which results in O ( N ) computational complexity of the clustering procedure with N being the number of pixels in the image. First, a regional clustering is iteratively performed by using spatial and spectral information simultaneously. The obtained result is a set of clustered partitions that exhibit both spectral similarity and spatial correlation. Then, for each partition we select a subset of candidate pixels with high spectral purity. Finally, the obtained candidate pixels are gathered together and fed to a spectral-based endmember extraction method to extract the final endmembers and their corresponding fractional abundances. Our newly developed method naturally integrates the spatial and the spectral information to retain the most relevant endmember candidates. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the proposed method can obtain accurate unmixing results with less than 0.5% of the number of pixels used by other state-of-the-art methods. This confirms the advantages of integrating spatial and spectral information for hyperspectral unmixing purposes. [ABSTRACT FROM AUTHOR]

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the sensitivity of L-band multi-parameter retrievals across the range of angular measurements available from the SMOS (Soil Moisture and Ocean Salinity) mission. The SMOS core algorithm was used to evaluate two-parameter retrieval scenarios including soil moisture and one of either i) vegetation water content, ii) surface roughness, iii) vegetation temperature, or iv) surface soil temperature. For all pairs a range of parameter value combinations were compiled to run the model in forward mode. Subsequently, the resulting angular brightness temperature simulations with two unknown parameters were compared against the brightness temperature response derived from reference simulations using data from the National Airborne Field Experiment 2005 (NAFE'05) in Australia. This paper showed that the two-parameter retrieval accuracy of soil moisture is strongly affected by the surface moisture conditions, the polarization of the brightness temperature data, and the choice of the secondary ancillary parameter to be retrieved. The synthetic analysis demonstrated a tendency for better retrievals from dual-polarized data at large incidence angles (40–50°). Validation with airborne brightness temperature observations at L-band did not demonstrate such a strong angular dependency, although it confirmed that the simultaneous retrieval of soil moisture and vegetation properties is not preferable as opposed to i) soil moisture and surface roughness or ii) soil moisture and surface soil temperature, especially under dry moisture conditions. [Copyright &y& Elsevier]

In typical surface energy balance (SEB) models such as surface energy balance for land (SEBAL) and mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC), calibration of sensible heat flux ( H ) requires identification of endmember (i.e. hot and cold) pixels. Such pixel selection is typically done manually, which makes it labor intensive to apply SEBAL or METRIC over large spatial areas or long time series. In this paper, we introduce a new automated approach that uses an exhaustive search algorithm (ESA) to identify endmember pixels for use in these models. The fully automated models were applied on 134 near cloud-free Landsat images with each image covering one of four flux measurement sites covering a distinct land cover type in humid Florida or relatively drier Oklahoma. Observed land surface temperatures ( T s ) at automatically identified hot and cold pixels were within 0.25% of manually identified pixels (both coefficient of determination, R 2 , and Nash-Sutcliffe efficiency, NSE, ≥ 0.98, and root mean squared error, RMSE, ≤ 1.31 K). The new fully automated model performed better and demonstrated better consistency than an existing semi-automated method that used a statistical approach to subset coldest and hottest pixels within an image. Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.94 and RMSE ≤ 0.35 mm day − 1 ). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed automated pixel selection approach greatly reduces time demands (e.g. approximately one image per hour vs. hundreds of image per hour) for applying SEBAL and METRIC and allows for their more widespread and frequent use. This automation can also reduce potential bias introduced by an inexperienced operator and extends the domain of the models to new users. [ABSTRACT FROM AUTHOR]

RGB pseudo composite imagery is a robust and fast processing technique for satellite observations to provide near real-time synthesized weather information to forecasters. The technique consists of superposing three images resulting from a linear combination of multi-spectral observations and corresponding them to red, green and blue. A new algorithm for the detection of snow during daytime has recently been developed for the Suomi-NPP VIIRS instruments. The algorithm uses five VIIRS bands including that centered at 2.25 μm to enhance snow detection and that centered at 1.24 μm to highlight the temporal variability of the snow cover. Furthermore, this paper shows the capability of the algorithm to detect snow under thin cloud conditions. This product is already provided in near real-time to forecasters of the French national weather service, Météo-France. [ABSTRACT FROM AUTHOR]

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately 10% of the observations) the following metrics were obtained: RMSD (root mean square difference) of 0.016 m 3 /m 3 and correlation of 0.83. The PALS soil moisture was also compared to SMAP and in situ soil moisture at the 36-km scale, which is the SMAP grid size for the standard product. PALS and SMAP soil moistures were found to be very similar owing to the close match of the brightness temperature measurements and the use of a common soil moisture retrieval algorithm. Spatial heterogeneity, which was identified using the high-resolution PALS soil moisture and the intensive ground sampling, also contributed to differences between the soil moisture estimates. In general, discrepancies found between the L-band soil moisture estimates and the 5-cm depth in situ measurements require methodologies to mitigate the impact on their interpretations in soil moisture validation and algorithm development. Specifically, the metrics computed for the SMAP radiometer-based soil moisture product over WGEW will include errors resulting from rainfall, particularly during the monsoon season when the spatial distribution of soil moisture is especially heterogeneous. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16days of MODIS data gridded to 1km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products. [Copyright &y& Elsevier]

In a 1988 study, a generalized non-linear SST algorithm was derived from the radiative transfer equation. An empirical approximation to this algorithm provided the simplified NLSST algorithm for computing sea surface temperatures with satellite radiometer data. It has remained a favorite of the remote sensing community for the past 25 years. However, a 1998 study demonstrated that the functional form of the generalized non-linear algorithm, which is here designated the GNLSST, is very much data dependent. In this paper we explore the various functional forms that the GNLSST may take. Rather than using actual satellite data, we model the effects of atmospheric absorption by water vapor, under saturation conditions, on the satellite split-window temperature difference, ΔT, in the 11–13 μm infra-red spectral region. The parameters used in the GNLSST algorithm are adjusted to demonstrate the various forms that the GNLSST can take. One key finding is that the familiar algorithms used for measuring SST, such as the MCSST, the NLSST and the QSST are all special solutions to the more general GNLSST algorithm. Additionally, the GNLSST may take other forms, which are described graphically. The simplifying assumptions which yield the NLSST solution represent an approximation to reality when working with actual satellite data. It is demonstrated that minor modifications to the regression procedures, which are used to derive the NLSST with satellite split-window data, may reduce the temperature errors resulting from these approximations. Alternative forms of the GNLSST algorithm may be useful for users of regional split-window satellite data. [ABSTRACT FROM AUTHOR]

Changes of permafrost not only affect regional and global water circulation, carbon deposit and climate warming, but also influence ground ecological, geophysical, and biogeochemical processes in cold region. Monitoring surface deformation over permafrost will help to understand the dynamics of these processes, assess climate changes, and prevent potential damages to engineering structures underlain by permafrost. This article employs an improved small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor the surface deformation over the Danxiong-Yangbajing area in the southern Qinghai-Tibet Plateau (QTP), with emphasis on climatic factors modeling. A new deformation model considering both the external (mainly climatic) and internal factors (such as tectonic activities and thermal character of frozen soil) is constructed and introduced into the SBAS-InSAR to estimate the surface deformation over permafrost. The results show that the peak-to-peak annual deformation over natural surface is 3.6–5.0 cm, while that along the Qinghai-Tibet Highway (QTH) and the Qinghai-Tibet Railway (QTR) is 2.8–3.7 cm. We also find that the surface deformation over frozen soil is negatively correlated to air temperature and precipitation, with the correlation being − 0.80 to − 0.45 and − 0.95 to − 0.75, respectively. The root mean squares (RMSs) of the residual (un-modeled) deformation got by the proposed method are much smaller than that by the existing methods. We also successfully examine a time lag of about 60 days between the retrieved maximal subsidence and the highest air temperature in warm seasons (from May to September). After compensating for this time lag, the RMSs of the residual deformation reduce significantly and the correlation between the deformation and the temperature rises considerably, varying from − 0.95 to − 0.65. The method presented in this paper is more suitable to model and analyze the surface deformation in permafrost areas. [ABSTRACT FROM AUTHOR]

Abstract: Availability of free, high quality Landsat data portends a new era in remote sensing change detection. Using dense (~annual) Landsat time series (LTS), we can now characterize vegetation change over large areas at an annual time step and at the spatial grain of anthropogenic disturbance. Additionally, we expect more accurate detection of subtle disturbances and improved characterization in terms of both timing and intensity. For Landsat change detection in this new era of dense LTS, new detection algorithms are required, and new approaches are needed to calibrate those algorithms and to examine the veracity of their output. This paper addresses that need by presenting a new tool called TimeSync for syncing algorithm and human interpretations of LTS. The tool consists of four components: (1) a chip window within which an area of user-defined size around an area of interest (i.e., plot) is displayed as a time series of image chips which are viewed simultaneously, (2) a trajectory window within which the plot spectral properties are displayed as a trajectory of Landsat band reflectance or index through time in any band or index desired, (3) a Google Earth window where a recent high-resolution image of the plot and its neighborhood can be viewed for context, and (4) an Access database where observations about the LTS for the plot of interest are entered. In this paper, we describe how to use TimeSync to collect data over forested plots in Oregon and Washington, USA, examine the data collected with it, and then compare those data with the output from a new LTS algorithm, LandTrendr, described in a companion paper (Kennedy et al., 2010). For any given plot, both TimeSync and LandTrendr partitioned its spectral trajectory into linear sequential segments. Depending on the direction of spectral change associated with any given segment in a trajectory, the segment was assigned a label of disturbance, recovery, or stable. Each segment was associated with a start and end vertex which describe its duration. We explore a variety of ways to summarize the trajectory data and compare those summaries derived from both TimeSync and LandTrendr. One comparison, involving start vertex date and segment label, provides a direct linkage to existing change detection validation approaches that rely on contingency (error) matrices and kappa statistics. All other comparisons are unique to this study, and provide a rich set of means by which to examine algorithm veracity. One of the strengths of TimeSync is its flexibility with respect to sample design, particularly the ability to sample an area of interest with statistical validity through space and time. This is in comparison to the use of existing reference data (e.g., field or airphoto data), which, at best, exist for only parts of the area of interest, for only specific time periods, or are restricted thematically. The extant data, even though biased in their representation, can be used to ascertain the veracity of TimeSync interpretation of change. We demonstrate that process here, learning that what we cannot see with TimeSync are those changes that are not expressed in the forest canopy (e.g., pre-commercial harvest or understory burning) and that these extant reference datasets have numerous omissions that render them less than desirable for representing truth. [Copyright &y& Elsevier]

Abstract: This paper presents a new algorithm for the detection of oil spill from SAR intensity images. The proposed algorithm combines the marked point process, Bayesian inference and Markov Chain Monte Carlo (MCMC) technique. In this paper, the candidates of oil spills or dark spots in a SAR intensity image are characterized by a Poisson marked point process. The marked point process is formed by a group of random points (as a point process modelling the locations of oil spills) and a set of parameters including geometric parameters of windows centred at the random points and gamma distribution parameters (as the marks attaching to each point). As a result, the candidates of oil spills are represented by a group of windows, in which the intensities of pixels follow independent and identical gamma distribution with lower mean than that for the identical gamma distribution of the pixels out of windows. Following the Bayesian paradigm, the posterior distribution, which characterizes the locations and statistical distributions of oil spills, can be obtained up to a normalizing constant. In order to simulate from the posterior distribution and to estimate the parameters of the posterior distribution, the Revisable Jump MCMC (RJMCMC) algorithm is used. The optimal locations and sizes of dark spots are obtained by a maximum a posteriori (MAP) algorithm. The proposed approach is tested using Radarsat-1 SAR images with oil spills indicated by human analysts. The results show that the proposed approach works well and is very promising. [Copyright &y& Elsevier]

Abstract: With the constant proliferation of computational power, our ability to develop hybrid classifiers has improved. Hybrid classifiers integrate results from multiple algorithms and often improve classification accuracy. In this paper, a hybrid classification framework was used to evaluate two research hypotheses: i) can manipulated results from prior classifiers (“intermediate inputs” (IIs)) improve classification accuracy in subsequent classification steps. and ii) is there an optimal dataset proportion for creation and usage of intermediate inputs. These additional intermediate inputs were based on spatial and texture statistics calculated on a partially classified image. The implementation of intermediate inputs on an impervious surface classification task using a 2001 Landsat ETM+ image from central New York was demonstrated. The results suggested that there was an average accuracy improvement of 3.6% (maximum 6.6%) by using intermediate inputs. These improvements were proved statistically significant by a Z-test and tended to increase as classification difficulty increased. The experiments in this paper also showed that there was an optimal point that balanced the number of pixels and pixel classification accuracy from prior steps used to produce intermediate inputs. Additionally, some traditional problems such as separation of impervious surfaces and soil were successfully tackled through intermediate inputs. The concept of the intermediate inputs may easily apply to other sensors and/or ground features. [Copyright &y& Elsevier]

Abstract: A retrieval algorithm, of total suspended matter (TSM) concentration in the Yellow Sea (YS) and East China Sea (ECS) was developed using observations made in the 2003 Spring and Autumn cruises over the YS and the ECS. Analysis of the in-situ backscattering coefficients of the suspended particles (b bp) indicates that the accuracy becomes worse when the concentration of TSM (C TSM) is higher than 20mg/l. The accuracy of the b bp is improved by using a bio-optical model in which b bp is optimized with a non-linear least-square Levenberg–Marquardt method. The remote sensing reflectance (R rs) is obtained by means of the optimization. The optimized R rs for waters with C TSM higher than 20mg/l, together with the measured R rs for waters with C TSM lower than 20mg/l, are used to establish the relationships between R rs(748), R rs(869) and R rs(645), which are used in the iterative method for atmospheric correction. Two atmospheric correction algorithms are switched according to the water turbidity. The shortwave infrared wavelengths (SWIR) method is used for waters with high-turbidity, and the iterative method is used otherwise. Results of the atmospheric correction were then applied to the Tassan model modified in this paper to compute the C TSM. Comparison between the retrieval results from MODIS imagery and the in-situ measurements indicates that the algorithms described in this paper can provide a reliable estimation of the C TSM distributions in the YS and ECS. [Copyright &y& Elsevier]

Abstract: An algorithm for the derivation of atmospheric parameters and surface reflectance data from MEdium Resolution Imaging Specrometer Instrument (MERIS) on board ENVIronmental SATellite (ENVISAT) images has been developed. Geo-rectified aerosol optical thickness (AOT), columnar water vapor (CWV) and spectral surface reflectance maps are generated from MERIS Level-1b data over land. The algorithm has been implemented so that AOT, CWV and reflectance products are provided on an operational manner, making no use of ancillary parameters apart from those attached to MERIS products. For this reason, it has been named Self-Contained Atmospheric Parameters Estimation from MERIS data (SCAPE-M). The fundamental basis of the algorithm and applicable error figures are presented in the first part of this paper. In particular, errors of ±0.03, ±4% and ±8% have been estimated for AOT, CWV and surface reflectance retrievals, respectively, by means of a sensitivity analysis based on a synthetic data set simulated under a usual MERIS scene configuration over land targets. The assumption of a fixed aerosol model, the coarse spatial resolution of the AOT product and the neglection of surface reflectance directional effects were also identified as limitations of SCAPE-M. Validation results are detailed in the second part of the paper. Comparison of SCAPE-M AOT retrievals with data from AErosol RObotic NETwork (AERONET) stations showed an average Root Mean Square Error (RMSE) of 0.05, and an average correlation coefficient R 2 of about 0.7–0.8. R 2 values grew up to more than 0.9 in the case of CWV after comparison with the same stations. A good correlation is also found with the MERIS Level-2 ESA CWV product. Retrieved surface reflectance maps have been successfully compared with reflectance data derived from the Compact High Resolution Imaging Spectrometer (CHRIS) on board the PRoject for On-Board Autonomy (PROBA) in the first place. Reflectance retrievals have also been compared with reflectance data derived from MERIS images by the Bremen AErosol Retrieval (BAER) method. A good correlation in the red and near-infrared bands was found, although a considerably higher proportion of pixels was successfully processed by SCAPE-M. [Copyright &y& Elsevier]

This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary information. Several k-Nearest Neighbors (k-NN) algorithm configurations were tested in two study areas in Italy belonging to Mediterranean and Alpine ecosystems. Field data were acquired by the regional forest inventory and forest management plans, and satellite images are from Landsat 5 TM and Landsat 7 ETM+. The paper describes the data used, the methodologies adopted and the results achieved in terms of pixel level accuracy of forest growing stock volume estimates. The results show that several factors affect estimation accuracy when using the k-NN method. For the two test areas a total of 3500 different configurations of the k-NN algorithm were systematically tested by changing the number and type of spectral and ancillary input variables, type of multidimensional distance measures, number of nearest neighbors and methods for spectral feature extraction using the leave-one-out (LOO) procedure. The best k-NN configurations were then used for pixel level estimation; the accuracy was estimated with a bootstrapping procedure; and the results were compared to estimates obtained using parametric regression methods implemented on the same data set. The best k-NN growing stock volume pixel level estimates in the Alpine area have a Root Mean Square Error (RMSE) ranging between 74 and 96 m3 ha−1 (respectively, 22% and 28% of the mean measured value) and between 106 and 135 m3 ha−1 (respectively, 44% and 63% of the mean measured value) in the Mediterranean area. On the whole, the results cast a promising light on the use of non-parametric techniques for forest attribute estimation and mapping with accuracy high enough to support forest planning activities in such complex landscapes. The results of the LOO analyses also highlight the importance of a local empirical optimization phase of the k-NN procedure before defining the best algorithm configuration. In the tests performed the pixel level accuracy increased, depending on the k-NN configuration, as much as 100%. [Copyright &y& Elsevier]

This paper presents a new method developed for the atmospheric correction of the images that will be acquired by the Venμs satellite after its launch expected in early 2010. Every two days, the Venμs mission will provide 10 m resolution images of 50 sites, in 12 narrow spectral bands ranging from 415 nm to 910 nm. The sun-synchronous Venμs orbit will have a 2-day repeat cycle, and the images of a given site will always be acquired from the same place, at the same local hour, with constant observation angles. Thanks to these characteristics, the directional effects will be considerably reduced since only the solar angles will slowly vary with time. The algorithm that will be implemented for the atmospheric correction of Venμs data is being developed using both radiative transfer simulations and the actual data acquired by the Formosat-2 satellite. Because of its one-day sun-synchronous repeat cycle, Formosat-2 acquires images with a sun-viewing geometry close to the one Venμs will offer. With this geometry, reflectance time series are free from directional effects on the short term, a feature which reduces the number of unknowns to retrieve. The atmospheric corrections algorithm exploits this feature and the two following assumptions: [–] Aerosol optical properties vary quickly with time but slowly with location. [–] Surface reflectances vary quickly with location but slowly with time. Consequently, the top of atmosphere reflectance short term variations (10 to 15 days) are mainly due to the variations of aerosol optical properties, and it is thus possible to use these variations to characterise the atmospheric aerosols and to retrieve surface reflectances. This paper first describes the aerosol inversion method we developed and its results when applied to simulations. In the second part, we show the first tests of the method against three data sets acquired by Formosat-2 images with constant observation angles. Aeronet sun photometers measurements were available on all sites. Formosat-2 estimates of optical thickness compare favourably with Aeronet in situ measurements, leading to a noticeable improvement of the smoothness of time series of surface reflectances after atmospheric correction. [Copyright &y& Elsevier]

Reliable information about the geographic distribution and abundance of major plant functional types (PFTs) around the world is increasingly needed for global change research. Using remote sensing techniques to map PFTs is a relatively recent field of research. This paper presents a method to map PFTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a multisource evidential reasoning (ER) algorithm. The method first utilizes a suite of improved and standard MODIS products to generate evidence measures for each PFT class. The multiple lines of evidence computed from input data are then combined using Dempster''s Rule of combination. Finally, a decision rule based on maximum support is used to make classification decisions. The proposed method was tested over the states of Illinois, Indiana, Iowa, and North Dakota, USA where crops dominate. The Cropland Data Layer (CDL) data provided by the United States Department of Agriculture were employed to validate our new PFT maps and the current MODIS PFT product. Our preliminary results suggest that multisource data fusion is a promising approach to improve the mapping of PFTs. For several major PFT classes such as crop, trees, and grass and shrub, the PFT maps generated with the ER method provide greater spatial details compared to the MODIS PFT. The overall accuracies increased for all the four states, with the biggest improvement occurring in Iowa from 51% (MODIS) to 64% (ER). The overall kappa statistic also increased for all the four states, with the biggest improvement occurring in Iowa from 0.03 (MODIS) to 0.38 (ER). The paper concludes with a discussion of several methodological issues pertaining to the further improvement of the ER approach. [Copyright &y& Elsevier]

Abstract: Evapotranspiration (ET) is a key process in land surface–atmosphere studies. It mainly depends on water availability and incoming solar radiation and then reflects the interactions between surface water processes and climate. In this paper, a methodology for retrieving ET from low spatial resolution remote sensing data is presented. It is based on the evaporative fraction concept, and it has been applied to Advanced Very High Resolution Radiometer (AHVRR) data acquired over the Iberian Peninsula. The methodology does not require other data than the data provided by the satellite and may be applied to areas with almost spatially constant atmospheric conditions and which include wet and dry sub-areas. The comparison with high resolution ET estimation shows a root mean square error (RMSE) of 1.4 mm d−1 which is in agreement with the sensitivity analysis of the method. Finally, the methodology has been applied to temporal NOAA–AVHRR images acquired from 1997 to 2002 in order to analyze the seasonal evolution of the daily ET. The temporal study of the ET values realized in this paper shows that the highest ET values are associated with the higher development crops, while the lowest values are related with lower development or null crop. As a conclusion, it is shown that the ET values obtained with the proposed model evolve according to the variations presented in parameters such as surface temperature or vegetation index. [Copyright &y& Elsevier]

This paper focuses on the high accuracy geo-location and co-registration performance operationally obtained for the Multi-angle Imaging SpectroRadiometer (MISR) data via a robust production algorithm. The MISR instrument (which is part of the payload for NASA''s Terra spacecraft) continuously acquires systematic, global, multi-angle imagery in reflected sunlight since it was launched in December 1999, with the objective of supporting ecology and climate studies. The moderate resolution data need to be autonomously geo-rectified prior to being used in subsequent scientific retrievals. This is particularly critical to the unique cloud height/wind retrievals which require sub-pixel co-registration accuracies on a global basis. In order to address this problem, the MISR ground data processing system is based on a fully photogrammetric approach and is complemented with a quality monitoring system used to verify performance globally and over desired time periods. The geo-rectification requirements were developed from a series of data quality investigations based partially on sensitivity requirements for the cloud height/wind retrieval algorithm. An update to the nominal production algorithm has been implemented, and the final operational results are presented in this paper. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new way of detecting and monitoring flooding through the Autonomous Sciencecraft Experiment (ASE) [Chien, S. T., Debban, C., Yen, R., Sherwood, R. Castano, B., & Cichy, A. G. et al. (2001). ASC Science Study Report, available from http://ASE.jpl.nasa.gov], which is part of the Space Technology 6 effort under NASA''s New Millennium Program. Recent autonomy experiments conducted on Earth Observing 1 (EO-1) using the ASE flight software have demonstrated the ability of several science algorithms to successfully classify key features including flood-induced changes, in hyperspectral images captured by the EO-1 Hyperion instrument. Furthermore, onboard science analysis on the classified images has been performed, and then used to modify an operational plan without interaction from the ground (Sherwood, R., Chien, S., Tran, D., Cichy, B., Castano, R., Davies, A., et al. (2004). Preliminary results of the autonomous sciencecraft experiment. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT). These algorithms are used to downlink science data only when change occurs, and to detect features of scientific interests such as flooding, volcanic eruptions, and the formation and breakup of sea ice. The purpose of this paper is to demonstrate the success of ASE and its implications on detecting, mapping, and monitoring transient processes such as flooding autonomously from space. Mapping of water inundation and its change through time is part of our focus in studying transient processes from space. In 2004, hyperspectral data were acquired from the Hyperion instrument for target areas around the world that have a high potential for flooding to develop and test floodwater classifiers. In addition, classifier thresholds were determined from both normal flows and possible flood conditions. The paper introduces the development, testing, and success of the ASE software in detecting and reacting to flooding in near real-time. ASE is now operational and flight-tested, and, thus, ready to use for space-borne reconnaissance. Successful demonstration of the floodwater classifiers includes the capture of a rare flooding event of the Australian Diamantina River during ground testing in February 2004, and the detection of flood-related changes along the Brahmaputra River in Bangladesh and the Yukon River in Alaska during onboard testing on EO-1 in 2005. Both of these detections led to triggered responses onboard the spacecraft, which included acquiring additional Hyperion scenes. These results pave the way for future smart reconnaissance missions of transient processes on Earth and beyond. It is hoped that ASE will become a default in future missions to increase the science return by introducing spacecraft autonomy for detection and monitoring of science events, which otherwise would be discovered too late or altogether missed. [Copyright &y& Elsevier]

Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the ESA's Sentinel 3 (S3) satellite, accurate LST retrieval methodologies exploiting the synergy between OLCI and SLSTR instruments can be developed. In this paper we propose a candidate methodology for retrieving LST from data acquired with the forthcoming S3 instruments. The LST algorithm is based on the Split-Window (SW) technique with an explicit dependence on surface emissivity, in contrast to the AATSR level 2 algorithm with emissivity dependence embedded in the algorithm coefficients. Performance of the methodology is assessed by using MERIS/AATSR pairs (instruments with similar characteristics to OLCI and SLSTR, respectively). LST retrievals using different datasets of input emissivity are validated against in situ data measured along one year (2011) in five test sites and intercompared to the standard AATSR level 2 products. Validation results show that LST is retrieved with the proposed SW algorithm typically with RMSE below 2 K, providing slightly better results than the AATSR level 2 product. The main advantage of the proposed algorithm is that it allows for improvements in input emissivities to be directly translated into improved LST retrievals. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05° from satellite observations, to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05–0.1°, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~ 0.04 K in global area coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods. [ABSTRACT FROM AUTHOR]

Longwave radiation (5–100 μm) is a critical component of the Earth's radiation budget. Most of the existing satellite-based retrieval algorithms are valid only for flat surfaces without accounting for topographic effects. This causes significant errors. Meanwhile, the fixed spatial resolution of remote sensing data makes it difficult to link the satellite-derived longwave radiation to different land models running on various scales. These deficiencies result in an urgent need for topographic modeling and spatial scaling studies of longwave radiation. In this paper, a longwave topographic radiation model (LWTRM) is proposed that quantifies all possible radiation-affecting factors over rugged terrain. For driving the LWTRM, a hybrid method for simultaneously deriving multiple components of longwave radiation from MODIS data is suggested based on artificial neuron networks (ANN) and the radiative transfer simulation. Topographically corrected longwave radiation is then derived by coupling the ANN outputs and LWTRM. Based on this, a general upscaling strategy for longwave radiation is presented. The results demonstrate that: (1) both the proposed LWTRM and the upscaling strategy are rather effective and work well over rugged areas; (2) the ANN-based retrieval method can produce longwave radiation with better accuracy(RMSE < 23 W/m 2 , bias < 9 W/m 2 ). More importantly, it can simultaneously derive multiple components of longwave radiation in a consistent manner; (3) over mountainous areas, the radiation cannot be accurately characterized in terms of either spatial distribution or specific values if topographic effects are neglected, for instance, the induced error can reach up to 100 W/m 2 for the longwave net flux; and (4) the topographic effects cannot be ignored below spatial scale of approximately 5 km in the selected study area. [ABSTRACT FROM AUTHOR]

In this study, we used the Disaggregation based on Physical And Theoretical scale Change (DISPATCH) algorithm under very wet soil conditions in Western Canada for the disaggregation of coarse resolution 40-km soil moisture derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The algorithm relies on the Soil Evaporative Efficiency (SEE), which was estimated using the 1-km resolution data from the MODerate resolution Imaging Spectoradiometer (MODIS). The study aimed to: (i) evaluate DISPATCH under wet soil conditions, (ii) test the linearity/non-linearity of the relationship between soil moisture and SEE, and (iii) propose a more robust procedure to calibrate the SEE model under very wet soil conditions. The disaggregated soil moisture values were compared to 0–5 cm in situ measurements and the soil moisture derived from the L-MEB (L-band Microwave Emission of the Biosphere) model from airborne brightness temperature at 1.4 GHz collected during the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) field campaign. The results show a correlation between 0.7 and 0.8 and bias values ~ 0 m 3 /m 3 . The DISPATCH algorithm shows better disaggregation results under very wet soil conditions when a non-linear relationship is considered between SEE and soil moisture instead of a linear model. This is mainly due to the small variability of surface temperature inside the area covered by the SMOS pixel under very wet soil conditions, and the difficulty in accurately estimating the maximum soil temperature ( Ts max ), which is a driving factor for SEE. A sensitivity analysis was conducted and it shows that the linear model performs well only if Ts max can be determined more accurately. The possibility to determine Ts max using high resolution MODIS data over a larger area than the SMOS pixel is examined and discussed in the paper. [ABSTRACT FROM AUTHOR]

The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM + imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a powerful tool for monitoring sea states in terms of the significant wave height (SWH). Regarding the specific wave mode, to date, the previous empirical models for estimating SWH from SAR data rely on single polarization. In the emerging deep learning era, few published quad-polarized SAR SWH retrieval algorithms have been based on machine learning technique, and whether quad-polarimetry improves the skill of wave height estimation remains a question. Here we propose a deep residual convolutional neural network-based SAR SWH retrieval algorithm in quad-polarization. By collocating WaveWatch III sea state hindcasts and all available archives of quad-polarized Chinese Gaofen-3 SAR imagettes in wave mode, a database with approximately 30,000 matchups was employed to establish our deeply-learned network. The GaoFen-3 significant wave height retrievals were validated against the hindcast dataset independent of training along with altimeter observations. The result of good consistency in terms of a root mean square error of 0.32 m (under sea state conditions of approximately 0.5–7.0 m) outperforms the existing Gaofen-3 wave height retrieval algorithms. Additionally, this paper introduces a discussion about the contribution of polarizations by comparing SWH derived from single-, dual- and quad-polarized deep convolutional neural networks. Single-polarized Gaofen-3 SAR data are found to be sufficient to provide accurate estimates compared to quad-polarization via a deep learning model under moderate sea conditions. Exploitation of SAR quad-polarimetry information will improve SAR wave height retrievals under high sea conditions. • We propose residual networks for estimating wave heights from Gaofen-3. • Our approach outperformed the existing Gaofen-3 wave height retrieval methods. • Single polarization is sufficient for accurately estimating moderate wave heights. • Quad-polarized data enhance the SAR wave estimating capabilities for high states. [ABSTRACT FROM AUTHOR]

Abstract: Spectral unmixing is the process of decomposing the spectral signature of a mixed pixel into a set of endmembers and their corresponding abundances. Endmembers are spectra of the pure materials present in the image and abundances at each pixel represent the percentage of each endmember that is present in the pixel. Many spectral unmixing techniques treat a pixel as independent of its neighbors, therefore, only spectral characteristics of the image are used to address the spectral unmixing problem. However, a number of recent studies have found that spatial autocorrelation provides useful information for spectral unmixing. Combining spatial information with its spectral counterpart can lead to improvements in the unmixing results. In this paper, the unmixing methods that incorporate spatial information are termed spatial spectral unmixing, whereas those exploiting only spectral information are referred to as spectral-only unmixing. We summarize the available spatial spectral unmixing methods according to the following three categories: 1) endmember extraction, 2) selection of endmember combinations, and 3) abundance estimation. An experiment-based comparison between representative spatial spectral and spectral-only unmixing methods is also presented in order to demonstrate the advantages of spatial spectral methods. Furthermore, considerations and suggestions of the incorporation of spatial information are provided. With this review, we hope to bring spatial spectral unmixing to the attention of the remote sensing community and stimulate new research initiatives to integrate both spatial and spectral information for unmixing purposes. [Copyright &y& Elsevier]

Abstract: Estimating primary production at large spatial scales is key to our understanding of the global carbon cycle. Algorithms to estimate primary production are well established and have been used in many studies with success. One of the key parameters in these algorithms is the chlorophyll-normalised production rate under light saturation (referred to as the light saturation parameter or the assimilation number). It is known to depend on temperature, light history and nutrient conditions, but assigning a magnitude to it at particular space-time points is difficult. In this paper, we explore two models to estimate the assimilation number at the global scale from remotely-sensed data that combine methods to estimate the carbon-to-chlorophyll ratio and the maximum growth rate of phytoplankton. The inputs to the algorithms are the surface concentration of chlorophyll, sea-surface temperature, photosynthetically-active radiation at the surface of the sea, sea surface nutrient concentration and mixed-layer depth. A large database of in situ estimates of the assimilation number is used to develop the models and provide elements of validation. The comparisons with in situ observations are promising and global maps of assimilation number are produced. [Copyright &y& Elsevier]

Abstract: Satellite-based passive microwave remote sensing has been shown to be a valuable tool in mapping and monitoring global soil moisture. The Advanced Microwave Scanning Radiometer on the Aqua platform (AMSR-E) has made significant contributions to this application. As the result of agency and individual initiatives, several approaches for the retrieval of soil moisture from AMSR-E have been proposed and implemented. Although the majority of these are based on the same Radiative Transfer Equation, studies have shown that the resulting soil moisture estimates can differ significantly. A primary goal of this investigation is to understand these differences and develop a suitable approach to potentially improve the algorithm currently used by NASA in producing its operational soil moisture product. In order to achieve this goal, the theoretical basis of several alternative soil moisture retrieval algorithms are examined. Analysis has focused on five established approaches: the operational algorithm adopted by NASA, which is referred to as the Normalized Polarization Difference algorithm, the Single Channel Algorithm, the Land Parameter Retrieval Model, the University of Montana soil moisture algorithm, and the HydroAlgo Artificial Neural Network algorithm. Previous comparisons of these algorithms in the literature have typically focused on the retrieved soil moisture products, and employed different metrics and data sets, and have resulted in differing conclusions. In this investigation we attempt to provide a more thorough understanding of the fundamental differences between the algorithms and how these differences affect their performance in terms of range of soil moisture provided. The comparative overview presented in the paper is based on the operating versions of the source codes of the individual algorithms. Analysis has indicated that the differences between algorithms lie in the specific parameterizations and assumptions of each algorithm. The comparative overview of the theoretical basis of the approaches is linked to differences found in the soil moisture retrievals, leading to suggestions for improvements and increased reliability in these algorithms. [Copyright &y& Elsevier]

Historically, change detection reviews have examined and categorized algorithms based on their techniques for the remote sensing community. Here, we synthesize urban land change algorithms by the types of information they provide to a diverse and growing set of user communities. Two goals of the paper are first to synthesize past and current change detection studies to examine urban land change to help users of remote sensing algorithms understand and navigate the vast variety of available methods, and second to identify gaps in knowledge for the urban remote sensing community. We analyzed 644 peer-reviewed research papers published in English-language journals and conducted a systematic review of urban land change algorithms. All papers included in our study focus on urban land change ; studies that concentrate on single-date urban classification or mapping for one point in time without an explicit urban land change component are not included in this analysis. The review showed five key results and knowledge gaps. First, most urban change detection algorithms are being developed and applied for only a few regions, with 75% of studies focused on high or upper-middle-income countries and the majority of these on China or the United States. This suggests a major gap in geographic coverage as well as the need for more studies on cities in low and lower-middle-income countries. Second, the results show that 41% of the algorithms have been developed or applied for cities of over 5 million inhabitants. This focus on large cities is problematic given that only 11% of the world's urban population lives in cities with populations greater than 5 million and that most future urban growth will occur in small- and medium-sized towns and cities with populations of fewer than 1 million people. Third, our analysis shows that 62% of the studies use three or fewer time points to measure urban land change with an average study length of 17 years. Since rapidly growing urban areas are highly dynamic, this suggests that existing algorithms using only a few time points are likely missing urban transitions that can only be captured with high temporal frequency analysis. Fourth, we find that urban expansion is the most commonly monitored type of urban land change. Comparatively fewer studies characterized intra-urban change or three-dimensional structural change. Fifth and finally, the results show that an overwhelming majority—87%—of all studies identify only one urban class, highlighting a need for more studies that distinguish intra-urban variation and differentiate multiple urban classes. Our analysis shows that it is very difficult—nearly impossible—to compare across algorithms. Thus, for users of urban land change information, it is difficult to navigate the literature and know which algorithms are most appropriate for a particular use. Taken together, this points to the need for improved reproducibility, replicability, and comparability of studies in order to help harmonize urban land change information across regions and countries. This is especially important given the growing user communities of urban land change products and analysis, especially from policy and practice. • We conduct a systematic review of the urban land change algorithm literature. • Most studies monitor urban change at low temporal frequencies. • The majority of urban land change is characterized using only one urban class. • Over 75% of studies focus on high or upper-middle level income countries. • Key knowledge gaps include in geographic coverage, city size, and land transitions. [ABSTRACT FROM AUTHOR]

Abstract: Improving regional estimates of actual evapotranspiration (λΕ) in water-limited regions located at climatic transition zones is critical. This study assesses an λΕ model (PT-JPL model) based on downscaling potential evapotranspiration according to multiple stresses at daily time-scale in two of these regions using MSG–SEVIRI (surface temperature and albedo) and MODIS products (NDVI, LAI and fPAR ). An open woody savanna in the Sahel (Mali) and a Mediterranean grassland (Spain) were selected as test sites with Eddy Covariance data used for evaluation. The PT-JPL model was modified to run at a daily time step and the outputs from eight algorithms differing in the input variables and also in the formulation of the biophysical constraints (stresses) were compared with the λΕ from the Eddy Covariance. Model outputs were also compared with other modeling studies at similar global dryland ecosystems. The novelty of this paper is the computation of a key model parameter, the soil moisture constraint, relying on the concept of apparent thermal inertia (fSM-ATI ) computed with surface temperature and albedo observations. Our results showed that fSM-ATI from both in-situ and satellite data produced satisfactory results for λΕ at the Sahelian savanna, comparable to parameterizations using field-measured Soil Water Content (SWC) with r2 greater than 0.80. In the Mediterranean grasslands however, with much lower daily λE values, model results were not as good as in the Sahel (r2 =0.57–0.31) but still better than reported values from more complex models applied at the site such as the Two Source Model (TSM) or the Penman–Monteith Leuning model (PML). PT-JPL-daily model with a soil moisture constraint based on apparent thermal inertia, fSM-ATI offers great potential for regionalization as no field-calibrations are required and water vapor deficit estimates, required in the original version, are not necessary, being air temperature and the available energy (Rn-G) the only input variables required, apart from routinely available satellite products. [Copyright &y& Elsevier]

Abstract: Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space. However, retrieving LST is still a challenging task since the LST retrieval problem is ill-posed. This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data. A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements. The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites. The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST. Directions for future research to improve the accuracy of satellite-derived LST are then suggested. [Copyright &y& Elsevier]

Abstract: A gridded population dataset was produced for Pakistan by developing an algorithm that distributed population either on the basis of per-pixel built-up area fraction or the per-pixel value of a weighted population likelihood layer. Per-pixel built-up area fraction was calculated using a classification and regression trees (CART) methodology integrating high- and medium-resolution satellite imagery. The likelihood layer was produced by weighting different geospatial layers according to their effect on the likelihood of population being found in the particular pixel. The geospatial layers integrated into the likelihood layer were: 1) proximity to remotely sensed built-up pixels, 2) density of settlement points in a fixed kernel, 3) slope, 4) elevation, and 5) heterogeneity of landcover types found within a search radius. The method for weighting these layers varied according to settlement patterns found in the provinces of Pakistan. Differences in zonal population estimates generated from the 100-meter gridded population layer resulting from this study, Oak Ridge National Laboratory''s LandScan (2002), and CIESIN''s Gridded Population of the World and Global Rural Urban Mapping Project (GPW and GRUMP) are examined. Population estimates for small areas produced using this paper''s method were found to differ from census counts to a lesser degree than those produced using LandScan, GPW, or GRUMP. The root mean square error (RMSE) for small area population estimates for this method, LandScan, GPW, and GRUMP were 31,089, 48,001, 100,260, and 72,071, respectively. [Copyright &y& Elsevier]

Abstract: The Wildfire Automated Biomass Burning Algorithm (WF-ABBA) is a state-of-the-art algorithm for geostationary wildfire detection whose results have been increasingly used in a range of environmental applications. At present, the WF-ABBA validation activities and, in general, fire product validation methodologies are at a markedly less advanced stage than the algorithm itself. Particularly, little is known about detection timeliness, despite the value of such information for assessing the potential of geostationary observations to improve tactical decision making of first responders. This paper contributes to reducing this gap in two ways. Firstly, we describe a new methodology that is suitable for evaluating geostationary satellite wildfire detection in terms of incidents with regard to both timeliness and reliability. This methodology utilizes available official multi-agency wildfire reporting information and multitemporal Landsat imagery. Secondly, we apply the proposed validation method to temporally filtered GOES-West WF-ABBA (ver. 6.1) detections for the 2006 fire season over the State of California and present incident-wise and pixel-wise performance information. The results indicate highly reliable pixel-wise performance of WF-ABBA, with about 75% of fire pixels (or more) corresponding to actual recorded active wildfires. A substantial portion of wildfires were detected during their first hour of activity, and a few incidents—even before the initial reports from conventional sources. Although the WF-ABBA performs best at what it was designed for: consistently re-detecting (monitoring) active fires, we believe there is an additional potential for automated detection from current geostationary data to reduce wildfire ignition latencies in the Western U.S. Our results can serve as a guideline for algorithm developers and users of the WF-ABBA fire product. [Copyright &y& Elsevier]

Abstract: Steep terrain affects optical satellite images through variations it creates in both irradiance and bidirectional reflectance distribution function (BRDF) effects. To obtain the corrected land surface reflectance and detect land surface change through time series analysis over rugged surfaces, it is necessary to remove or reduce the topographic effects. In this paper a physics-based BRDF and atmospheric correction model that handles both flat and inclined surfaces in conjunction with a 1-second SRTM (Shuttle Radar Topographic Mission) derived Digital Surface Model (DSM) product was applied to 8 Landsat scenes covering different seasons and terrain types in eastern Australia. Visual assessment showed that the algorithm removed much of the topographic effect and detected deep shadows in all 8 images. An indirect validation based on the change in correlation between the data and terrain slope showed that the correlation coefficient between the surface reflectance factor and the cosine of the incident (sun) angle reduced dramatically after the topographic correction algorithm was applied. The correlation coefficient typically reduced from 0.80–0.70 to 0.05 in areas of significant relief. It was also shown how the terrain corrected surface reflectance can provide suitable input data for multi-temporal land cover classification in areas of high relief based on spectral signatures and spectral albedo, while the products based only on BRDF and atmospheric correction cannot. To provide comparison with previous work and to validate the proposed algorithm, two empirical methods based on the C-correction were used as well as the established SCS-method to provide benchmarks. The proposed method was found to achieve the same measures of shade reduction without empirical regression. [Copyright &y& Elsevier]

Abstract: The East China Sea (ECS) is well known for its high concentration of total suspended matter (TSM). Some regions of the ECS have concentrations higher than 5000gm−3, exceeding the valid ranges of many TSM remote sensing algorithms. To overcome the limitation of the existing algorithms, a new TSM model, the “complex proxy TSM model” (CPTSM), is developed in this study. The model is established on the basis of a complex proxy of remote sensing reflectance. The proxy is designed to convert the non-linear relationship between reflectance and TSM to a quasi-linear function over the entire range of TSM concentrations. This proxy is deduced from four indices defined by combinations of the reflectance at different bands. The four indices take advantage of the different relationships between the band combinations of the reflectance and total TSM concentrations. The band selections and model parameters are based on correlation coefficients and regression analysis between the indices and TSM. The results show that the correlation coefficient of 0.912 between the proxy and TSM is higher than that between any individual index and TSM. To validate the CPTSM model, TSM, turbidity, and reflectance data were collected in the ECS during 4 cruises in 2006 and 2007. The actual TSM concentration was measured by weighing the samples collected on filter papers. Turbidity was measured by a Seapoint Turbidity Meter. The turbidity data with values higher than 750FTU were re-calibrated using an empirical equation. All turbidity values were converted to TSM concentrations using a linear equation. The in situ reflectance was measured using the above-water method at 459 stations and the in-water method at 146 stations. A total of 87 pairs of reflectance measured by both methods were used for inter-comparison with a relative difference of 4.5%. The reflectance values were used to retrieve TSM concentrations using the CPTSM model. A comparison with in situ measurements gave a mean relative error of 23%. Applying the CPTSM model to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data and analyzing the errors from a match-up dataset of SeaWiFS and in situ data, we found that the average relative error was 24.5%. We propose to use the CPTSM model to map TSM concentrations from satellite data in the ECS. [Copyright &y& Elsevier]

Abstract: Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by ecosystems and is an important component of the terrestrial carbon cycle. Empirical light use efficiency (LUE) models and process-based Farquhar, von Caemmerer, and Berry (FvCB) photosynthetic models are widely used for GPP estimation. In this paper, the MODIS GPP algorithm using the LUE approach and the Boreal Ecosystem Productivity Simulator (BEPS) based on the FvCB model in which a sunlit and shaded leaf separation scheme is evaluated against GPP values derived from eddy-covariance (EC) measurements in a variety of ecosystems. Although the total GPP values simulated using these two models agree within 89% when they are averaged for the conterminous U.S., there are systematic differences between them in terms of their spatial and temporal distribution patterns. The spatial distribution of MODIS GPP therefore differs substantially from that produced by BEPS. These differences may be due to an inherent problem of the LUE modeling approach. When a constant maximum LUE value is used for a biome type, this simplification cannot properly handle the contribution of shaded leaves to the total canopy-level GPP. When GPP is modeled by BEPS as the sum of sunlit and shaded leaf GPP, the problem is minimized, i.e., at the low end, the relative contribution of shaded leaves to GPP is small and at the high end, the relative contribution of shaded leaves is large. Compared with monthly and annual GPP derived from eddy covariance data at 40 tower sites in North America, BEPS performed better than the MODIS GPP algorithm. The difference between MODIS and BEPS GPP widens as with the fraction of shaded leaves increases. The simpler LUE modeling approach should therefore be further improved to reduce this bias issue for effective estimation of regional and temporal GPP distributions. [Copyright &y& Elsevier]

Abstract: Given the pace and scale of urban expansion in many parts of the globe, urban environments are playing an increasingly important role in daily quality-of-life issues, ecological processes, climate, material flows, and land transformations. Remote sensing has emerged as a powerful tool to monitor rates and patterns of urban expansion, but many early challenges – such as distinguishing new urban land from bare ground – remain unsolved. To deal with the high temporal and spatial variability as well as complex, multi-signature classes within settlements, this paper presents a new approach that exploits multi-seasonal information in dense time stacks of Landsat imagery using a multi-date composite change detection technique. The central premise of the approach is that lands within/near urban areas have distinct temporal trajectories both before and after change occurs, and that these lead to characteristic temporal signatures in several spectral regions. The method relies on a supervised classification that exploits training data of stable/changed areas interpreted from Google Earth images, and a ‘brute force’ approach of providing all available Landsat data as input, including scenes with data gaps due to the Scan Line Corrector (SLC) problem. Three classification algorithms (maximum likelihood, boosted decision trees, and support vector machines) were tested for their ability to monitor expansion across five time periods (1988–1995, 1996–2000, 2001–2003, 2004–2006, 2007–2009) in three study areas that differ in size, eco-climatic conditions, and rates/patterns of development. Both the decision trees and support vector machines outperformed the maximum likelihood classifier (overall accuracy of 90–93%, compared to 65%), but the decision trees were superior at handling missing data. Adding transformed features such as band metrics to the Landsat data stack increased accuracy 1–4%, while experiments with a reduced number of features (designed to mimic noisy or missing data) led to a drop in accuracy of 1–9%. The methodology also proved particularly effective for monitoring peri-urbanization outside the urban core, capturing >98% of village settlements. [Copyright &y& Elsevier]

Abstract: NOAA will soon use the new Visible Infrared Imager Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) as its primary polar-orbiting satellite imager. Employing a near real-time processing system, NOAA will generate a series of Environmental Data Records (EDRs) from VIIRS data. For example, the VIIRS Land Surface Temperature (LST) EDR will estimate the surface skin temperature over all global land areas and provide key information for monitoring Earth surface energy and water fluxes. Because both VIIRS and its processing algorithms are new, NOAA is conducting a rigorous calibration and validation program to understand and improve product quality. This paper presents a new validation methodology to estimate the quantitative uncertainty in the LST EDR, and contribute to improving the retrieval algorithm. It employs a physically-based approach to scaling up point LST measurements currently made operationally at many field and weather stations around the world. The scaling method consists of the merging information collected at different spatial resolutions within a land surface model to fully characterize large area (km×km scale) satellite products. The approach can be used to explore scaling issues over terrestrial surfaces spanning a large range of climate regimes and land cover types, including forests and mixed vegetated areas. The methodology was tested successfully with NASA/MODIS data, indicating an absolute error for MODIS LST products of 2.0K at a mixed agricultural site (Bondville, IL) when accounting for scaling, and higher than 3K without scaling. The VIIRS LST EDR requires a 1.5K measurement accuracy and 2.5K measurement precision. Ultimately, this validation approach should lead to an accurate and continuously-assessed VIIRS LST product suitable to support weather forecast, hydrological applications, or climate studies. It is readily adaptable to other moderate resolution satellite systems. [Copyright &y& Elsevier]

Abstract: Validation is an important task in the development of satellite remote sensing products. Strategies for validation vary depending on the nature of the products. The validation process of the flood and standing water product (FSW) for the Geostationary Operational Environmental Satellite - R series (GOES-R) is presented in this paper. A major challenge in the validation of the FSW product is the lack of ground truth flood maps and similar reference products from other satellite systems and other sources. To overcome this limitation, a two-level validation scheme for the FSW product is developed using the Moderate-resolution Imaging Spectroradiometer (MODIS) data as a proxy. In the first level, gauging station data collected by the U.S. Geological Survey (USGS) are employed as ground truth flood point information on local scales to verify the effectiveness of the proposed algorithm for flood detection. Gauging station data collected during 34 flood cases that occurred in 2010 and 2011 in the continental US were validated and assessed according to the rate of correct detection. Results showed that 79.71% of flooding stations were accurately detected from the MODIS 1km images by the proposed FSW algorithm. In the second level of the validation, FSW detection results using the proposed algorithm were compared to the reference flood maps, which were generated by a supervised support vector machine (SVM) classification followed by human interpretation and editing. Flood detection accuracies for three major flood events occurred in Asia and Australia in 2010 were evaluated. Confusion matrices were employed as the accuracy measurement for the second level of the validation. Commission errors for the three flood cases were 6.75%, 13.45% and 21.45%, respectively. Omission errors of flood pixels varied between 9.58% and 19.61%. The validation results suggest that the employed FSW algorithm is capable of producing flood and standing water maps in an operational environment, and it meets the required accuracy and execution time of the product. [Copyright &y& Elsevier]

Abstract: This paper presents new methods for an automated analysis of the double InterTropical Convergence Zone (dITCZ) phenomena on a daily time scale over the east Pacific. Long-term Geostationary Operational Environmental Satellite (GOES) visible and infrared data are used to spatially identify and segment the convection zones over the east Pacific basin on both sides of the equator and to track the temporal variability of the ITCZ, specifically to identify cases of dITCZs, northern or southern ITCZ, or non-presence events. For the segmentation approach, image processing techniques are developed to extract information about the spatial features of the ITCZ in both hemispheres for each satellite image. These features serve as input to a temporal classification algorithm that is based on a combination of hidden semi Markov model (HsMM) and support vector machine (SVM) methods. The performance of the proposed method is competitive with human experts and the methodology can thus be used to conduct an in-depth analysis of the dITCZ. Such an analysis could provide precise information for refining existing weather and climate models over the sparsely observed east Pacific where the dITCZ is greatly over-represented in most models. [Copyright &y& Elsevier]

Abstract: In this paper we compare three different methodologies to retrieve land surface emissivity (LSE) over urban areas: the NDVI thresholds method, the temperature and emissivity separation (TES) algorithm and the temperature independent spectral indices (TISI) algorithm. The methodologies were applied to the Airborne Hyperspectral Scanner (AHS) imagery acquired during the Dual-use European Security IR Experiment 2008 (DESIREX 2008) experimental campaign over the city of Madrid (Spain). The images have a spatial resolution of 4m. The retrieved values are compared to in situ data measured during the campaign at 4 sites. Results show a good performance of the TISI and the TES algorithms over urban surfaces, while the NDVI threshold method does not appear to be capable to distinguish between different artificial surfaces. The land surface temperature (LST) was retrieved with a split window algorithm, using the three different LSE products. Differences of up to 3K were realized in some surfaces when different LSE maps were used. Over artificial urban surfaces TES and TISI showed better agreement with in-situ data than NDVI. Finally, the TES is the algorithm that best reproduces the LST over an urban area, without the requirement of high temporal resolution of the sensor. [Copyright &y& Elsevier]

Abstract: This paper summarizes the implementation of a physically based algorithm for the retrieval of vegetation green Leaf Area Index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsat-observed surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites. [Copyright &y& Elsevier]

Abstract: An important component of satellite-based soil moisture algorithm development and validation is the comparison of coincident remote sensing and in situ observations that are typically provided by intensive field campaigns. The planned NASA Soil Moisture Active Passive (SMAP) mission has unique requirements compared to previous soil moisture satellite programs because both active and passive microwave observations are needed. The primary source of these combined observations has been an aircraft-based SMAP simulator called PALS (Passive and Active L-band System). This paper presents an overview of the field experiment data collected using PALS that spans 10years. Data from the various campaigns were merged to form a single data set. Analyses showed that the data set contains an extensive range of soil moisture values collected under a variety of conditions and that the quality of both the PALS and ground truth data meets the needs of SMAP algorithm development and validation. The study suggests that the data set should be expanded in order to achieve globally representative land cover diversity and that more observations under dense vegetation conditions and longer time series would be desirable. [Copyright &y& Elsevier]

Abstract: Time series of the Normalized Difference Vegetation Index (NDVI) derived from satellite observations provide important information on the state of terrestrial vegetation over a wide range of spatiotemporal scales. For understanding long-term changes in terrestrial ecosystems (post-1981), data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board the satellites of National Oceanic and Atmospheric Administration (NOAA) series is a unique source of information. In this paper, we describe a new processing methodology for a comprehensive AVHRR data set at 1km spatial resolution acquired over Canada, the northern United States and Greenland post-1981. The methodology incorporates a pre-processing algorithm, Canadian AVHRR Processing System (CAPS), recently developed by the Canada Centre of Remote Sensing (CCRS), which enables highly accurate geolocation and ortho-rectification at efficiency rates of >90%. Once image navigation is completed, our approach consists of five key steps: first, two clear-sky composites for each 10day interval are generated from the forward or backward scattering hemisphere; second, AVHRR Channel 1 and 2 reflectances are normalized to the AVHRR/3 on board NOAA-17 to account for differences in the spectral response function among the AVHRR sensors; third, atmospheric correction is performed using the Simplified Method for Atmospheric correction (SMAC) algorithm, using standard meteorological data sets (water vapor, surface level air pressure, ozone); fourth, NDVI is calculated based on atmospherically corrected Channel 1 and 2 reflectances; and finally, the NDVI is adjusted for directional effects based on the Ross-Thick Li-Sparse Bidirectional Reflectance Distribution Function (BRDF) model. The processed NDVI data are compared to an equivalent spatially and temporally overlapping MODIS NDVI data set from 2001 to 2005 for validation. Results at continental scale indicate that time series of MODIS and AVHRR were similar for a wide range of biomes and generalized ecoregions. Analysis stratified by land cover indicated that the correlation was strongest for homogeneous land cover types, such as cropland, when compared to structurally more diverse classes, such as deciduous broadleaf forests. The comparison of the NDVI at the local scale at seven sites of the Fluxnet Canada Research Network resulted in the correlation coefficient r=0.95. Given confidence in the processing approach, this NDVI data set can be a valuable source of information for climate and vegetation-related studies over Canada and the northern United States. [Copyright &y& Elsevier]

Abstract: Simulation of future satellite images can be applied in order to validate the general mission concept and to test the performance of advanced multi-sensor algorithms for the retrieval of surface parameters. This paper describes the radiative transfer modeling part of a so-called Land Scene Generator (LSG) that was developed to simulate images of the sensors OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) on board of the Sentinel-3 mission. Features of this mission are its wide spectral coverage (optical and thermal domains) and its wide imaging swath, which imposes particular requirements on the simulator in dealing with atmospheric effects over both spectral domains and with angular effects caused by variations in surface bi-directional reflectance distribution function (BRDF) and atmospheric scattering. In the simulator, radiative transfer models for the combination vegetation-soil and for water are coupled to atmospheric parameters derived from MODTRAN runs in order to calculate top-of-atmosphere radiances. For this, four-stream radiative transfer theory is applied to allow simulation of BRDF effects, topography effects, adjacency effects, as well as its uniform application over the optical and thermal spectral domains. [Copyright &y& Elsevier]