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High-quality and large-scale image composites are increasingly important for a variety of applications. Yet a number of challenges still exist in the generation of composites with certain desirable qualities such as maintaining the spectral relationship between bands, reduced spatial noise, and consistency across scene boundaries so that large mosaics can be generated. We present a new method for generating pixel-based composite mosaics that achieves these goals. The method, based on a high-dimensional statistic called the ‘geometric median,’ effectively trades a temporal stack of poor quality observations for a single high-quality pixel composite with reduced spatial noise. The method requires no parameters or expert-defined rules. We quantitatively assess its strengths by benchmarking it against two other pixel-based compositing approaches over Tasmania, which is one of the most challenging locations in Australia for obtaining cloud-free imagery.

Global or regional environmental change is of wide concern. Extensive studies have indicated that long-term vegetation cover change is one of the most important factors reflecting environmental change, and normalized difference vegetation index (NDVI) time-series data sets have been widely used in vegetation dynamic change monitoring. However, the significant residual effects and noise levels impede the application of NDVI time-series data in environmental change research. This study develops a novel and robust filter method, i.e., the moving weighted harmonic analysis (MWHA) method, which incorporates a moving support domain to assign the weights for all the points, making the determination of the frequency number much easier. Additionally, a four-step process flow is designed to make the data approach the upper NDVI envelope, so that the actual change in the vegetation can be detected. A total of 487 test pixels selected from SPOT VEGETATION 10-day MVC NDVI time-series data from January 1999 to December 2001 were used to illustrate the effectiveness of the new method by comparing the MWHA results with the results of another four existing methods. Finally, the long-term SPOT VEGETATION 10-day maximum-value compositing (MVC) NDVI time series for China from April 1998 to May 2014 was reconstructed by the use of the proposed method, and a test region in China was utilized to validate the effectiveness of the proposed MWHA method. All the results indicate that the reconstructed high-quality NDVI time series fits the actual growth profile of the vegetation and is suitable for use in further remote sensing applications.

International audience; —Satellite-derived time series of the fraction of absorbed photosynthetically active radiation (fAPAR) are widely used to monitor vegetation dynamics and to detect vegetation anomalies. Several global data sets are available for this purpose. They are produced using different algorithms and/or satellite sensors. This paper compares and analyzes three multitemporal fAPAR data sets derived from SPOT-VEGETATION instrument by explicitly distinguishing between spatial and temporal agreement. The first two data sets are currently used by the Joint Research Centre—Monitoring Agricultural ResourceS Unit (JRC-MARS) for operational yield forecasting and food security assessments. The third time series (named GEOV1) is from a new processing algorithm developed within the European FP7 Geoland2 project. The comparative analysis was conducted for the years 2003 and 2004 over three 10 • × 10 • regions with different eco-climatic characteristics (Niger, Brazil, and France). Our study revealed that GEOV1 fAPAR estimates were systematically higher than those of JRC-MARS. The spatial analysis showed moderate to high agreement between data sets with specific seasonality in the three study regions. The temporal agreement showed spatial (and land cover-related) variability spanning from very low to almost perfect. Large differences were observed in regions and periods with large cloud occurrence where GEOV1 provides more reliable and smooth temporal profiles due to improved cloud screening and longer compositing periods. Other sources of disagreement between data sets were identified in differences in the fAPAR retrieval algorithm definitions. Index Terms—CYCLight, fraction of absorbed photosyntheti-cally active radiation (fAPAR), GEOV1-Geoland2, JRC-FAPAR, multitemporal remote sensing (RS) data, SPOT-VEGETATION (VGT).

This paper presents details of techniques for generating thermal infrared and visible composite images from the cloud-free portions of temporally closely spaced MODerate resolution Imaging Spectroradiometer (MODIS) images, with a focus on studies of landfast sea ice along the East Antarctic coast. Composite image inclusion criteria are based on modified MODIS Earth Observing System cloud mask product results. The compositing process presented places emphasis on retaining maximum spatial resolution while minimizing storage space requirements. Composite images can be produced either as a regular product (e.g., on a ten-day grid), or dynamically (whenever enough information is acquired to produce a new output image). The techniques presented are applicable at any latitude, are available for all MODIS channels at their native resolution, can combine Aqua and Terra images, and can produce maps in any output projection. However, due to the polar orbit of NASA's Terra and Aqua satellites which host the MODIS instrument, more frequent coverage is produced at higher latitudes. Thus, the techniques presented are particularly applicable to polar research. Examples of composite image generation of the landfast sea ice around the Mertz Glacier region, East Antarctica, are included for both winter and summer.

Vegetation indices (VIs) are one of the standard science products available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Earth Observing System (EOS) Terra platform, launched in December 1999. An important requirement of MODIS science products is that they be rigorously validated. In this study, we conducted a site-intensive MODIS VI product validation at the semiarid Jornada Experimental Range, New Mexico, an EOS Land Validation Core Site. Our validation approach involved scaling up independent fine-grained datasets, including ground and airborne radiometry, and high spatial resolution imagery [Enhanced Thematic Mapper Plus (ETM+)], to the coarser MODIS spatial resolutions. The MODIS VIs were evaluated with respect to their radiometric performances, the uncertainties of the compositing methodology, and their capabilities to depict seasonal variations in vegetation. The MODIS Quick Airborne Looks (MQUALS) radiometric package was found useful in up-scaling field in situ measurements to coarser spatial resolutions. Both single-day nadir-view and 16-day composited MODIS reflectances and VIs matched well with the nadir-based atmosphere-free MQUALS observations for all the land cover types found at Jornada, with the root mean squared deviations less than 0.03. The MODIS 16-day composited products also performed well with the single-day nadir-view MODIS data, despite some off-nadir view angles and uncertainties with the cloud mask algorithm. The quality assurance (QA)-based constrained view angle-maximum value composite (CV-MVC) algorithm successfully filtered out much of the cloud and aerosol contaminated observations and helped to minimize view angle-related problems. The MODIS seasonal VI profiles also matched quite well with the other multiple sensor datasets obtained at the finer spatial resolutions. The QA information was found to be crucial in achieving consistent spatial and temporal comparisons of global vegetation conditions and for deriving accurate depictions of important phenological features in multitemporal MODIS data. The results of this validation study over the Jornada Experimental Range demonstrated the accuracy, reliability, and science utility of the MODIS VI products in arid and semiarid areas.

Composited wide field of view satellite data are used for many applications and increasingly for studies of global change. Although several compositing schemes have been suggested, all assume perfect geometric registration, which is not operationally feasible. In this study, models of the satellite imaging, geometric registration, and compositing processes are used to investigate the impact of misregistration upon the position of high contrast edges found in composited satellite data. Simulations are performed with respect to the compositing of advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data. Contrast edges are found to be systematically shifted in maximum and minimum value composites. The degree of shifting increases with the number of orbits that are composited, the degree of misregistration and the view zenith angle. The implications of these findings upon the utility of composited satellite data for change detection are discussed. The shifts may systematically bias estimates of location and area when composited data are used. They may also cause small and/or fragmented features, which are evident in individual orbits to disappear in composited data, precluding the ability to map such features or to detect their occurrence under a change detection scheme.

MC-FUME stands for median composite of fuzzy multispectral estimate. It is the name of a newly developed method for compositing individual reflective channels of the VEGETATION sensor onboard the SPOT-4 platform. MC-FUME is a two-step compositing methodology that uses combined angular and atmospheric corrections of reflectance measurements. The first step is an approximate BRDF correction. Considering the atmospheric influence to be stochastic, the top-of-canopy (TOC) reflectance at a reference geometry is estimated for each pixel by means of an extensive database of model-simulated top-of-atmosphere (TOA) reflectance values. The second step is compositing over a time period. This is done by taking the median of the estimated TOC reflectance values. The method is tested on simulated time series at different latitudes as well as on a time series of NOAA-AVHRR images. Tests performed on the simulated data set prove the ability of the MC-FUME algorithm to correctly reproduce TOC/sub nadir/ values. Moreover, it outperforms classic compositing strategies such as the maximum-value composite of the NDVI (MVC-NDVI) in this respect. Tests performed on AVHRR images show that the angular dependence of the MC-FUME algorithm is strongly reduced with respect to the classic MVC-NDVI method, as is the presence of speckle. This is especially remarkable for the individual reflective channels (RED and NIR). Thus, for individual reflective channels, MC-FUME produces speckle-free composites with reflectance values that are corrected for atmospheric and angular effects, and which therefore are independent of the observation/illumination geometry at the time of measurement.

Microwave radiometers, while traditionally utilized in atmospheric and oceanic studies, can also be used in land surface applications. However, the problem of undesirable atmospheric effects caused by clouds and precipitation must be addressed. In this paper, temporal composite surface brightness images are generated from special sensor microwave/imager (SSM/I) data with the aid of new algorithms to eliminate small-scale distortion caused by clouds or precipitation. Mean, second-highest value, modified maximum average (MMA), and hybrid compositing algorithms are compared. The effectiveness of each algorithm is illustrated through simulation and real data distribution analysis. The results show that the second-highest value algorithm is biased high. MMA provides a more accurate brightness temperature estimate in areas of atmospheric distortion, while the mean is superior in regions with little or no distortion. A hybrid algorithm is developed that is a combination of MMA and mean. It utilizes the strengths of both to create a superior algorithm for regions with varying levels of distortion. Uses of composite images produced by these algorithms include studies of vegetation change, land cover classification, and surface parameter extraction.

The methodology used to store a number of the Moderate Resolution Imaging Spectroradiometer (MODIS) land products is described. The approach has several scientific and data processing advantages over conventional approaches used to store remotely sensed data sets and may be applied to any remote-sensing data set in which the observations are geolocated to subpixel accuracy. The methodology will enable new algorithms to be more accurately developed because important information about the intersection between the sensor observations and the output grid cells are preserved. The methodology will satisfy the very different needs of the MODIS land product generation algorithms, allow sophisticated users to develop their own application-specific MODIS land data sets, and enable efficient processing and reprocessing of MODIS land products. A generic MODIS land gridding and compositing algorithm that takes advantage of the data storage structure and enables the exploitation of multiple observations of the surface more fully than conventional approaches is described. The algorithms are illustrated with simulated MODIS data, and the practical considerations of increased data storage are discussed.

Describes the characteristics and performance of an algorithm designed to remove the effects of cloud contamination from AVHRR composite images over land. Using information in the temporal NDVI profile, the algorithm detects cloud-contaminated pixels, and optionally replaces these with interpolated values of individual AVHRR channels or channel transformations. As tested, the algorithm detects only those effects of residual clouds that result in a temporary decrease of the NDVI. It was found that the algorithm is capable of identifying clouds of varying opacity as well as cloud shadows present in the composites, and that its use appears preferable to compositing over longer periods. The algorithm can be applied without the need for ancillary information, e.g., on expected surface radiance conditions. Although developed for use in biospheric studies in Canada, it could also be applied at other latitudes where the seasonal trajectories of satellite-derived variables exhibit a single maximum or, by extension, a single minimum. >

The objective of this study was to evaluate alternative methods for producing both Normalized Difference Vegetation Index (NDVI) and single-channel composite images of land surfaces from the Advanced Very High Resolution Radiometer (AVHRR). As a goal, it was specified that the composite image should approximate as much as possible a single-date image with a constant, near-nadir geometry. The comparative performance of three single-step [maximum NDVI (MaN), maximum apparent temperature (MaT), maximum difference of channels 2 minus 1 (MaD)] and two two-step [maximum NDVI followed by maximum temperature (MaNMaT) or by minimum scan angle (MaNMiSc)] criteria in creating composite images was evaluated for three land cover types, namely, cropland, coniferous forest, and deciduous forest within a boreal ecosystem. The assessment was carried out using 18 images obtained with various acquisition geometries in mid-summer over a 1000/spl times/1000 km area in Manitoba, Canada. In addition, MaT performance was compared with that of MaN for all Canada during two compositing periods. It was found that among the five criteria tested, MaT and MaNMiSc were the most effective one- and two-step criteria, respectively. MaN preferentially selected off-nadir pixels from the forescatter region, the degree varying with land cover type; the overall NDVI values were higher than for MaT. MaD showed a very strong preference for high backscatter region pixels, regardless of land cover type. Depending on cover type, the NDVI values resulting from Mao were higher or lower than those from a nadir image. Based on ranking of the five techniques using statistics of the differences between composite and reference images, it was found that MaT and MaNMiSc performed similarly, and better than the remaining criteria. Results of the tests show that although statistically reasonable approximations of the reference image could be produced by one or more methods, none of the criteria could consistently yield composites closely resembling the nadir image on a pixel basis, even for a reasonably long compositing period. Therefore, pixel-specific applications of the composites relying on individual channels will likely have to be based on data corrected for bidirectional effects. The results suggest that such corrections are also required for NDVI and, by analogy, for other AVHRR channel combinations. >