Multispectral pansharpening with radiative transfer-based detail-injection modeling for preserving changes in vegetation cover

Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, Sun and satellite heights and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage of a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth's surface from space, that is for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape on different dates highlight that the de-hazing of MS before fusion is beneficial to an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI)

Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use

Fusing ground measurements and satellite-derived products for the construction of climatological maps in atmosphere optics

International audienceClimatological maps (gridded data) of optical parameters of the atmosphere often result from application of numerical models or processing of satellite images. Such maps usually exhibit very large cell sizes, of the order of 1 of arc angle. There is a need to increase their spatial resolution to obtain a cell size closer to the spatial representation obtained by standard meteorological instruments at ground level, i.e. 5' of arc angle. It then permits to fuse ground measurements and gridded data, especially to correct for bias ob- served in gridded data. Taking advantage of the availability of other data sets of relevance to the parameter under concern, though different, a method is proposed for the synthesis of the initial gridded data at a higher resolution by the means of a fusion process. This paper describes the method with an application for the con- struction of worldwide maps of the Linke turbidity factor with cells of 5' in size. This factor characterises the atmospheric optical turbidity under clear skies and is a very important parameter in solar radiation studies

A Geostatistical Data Fusion Technique for Merging Remote Sensing and Ground-Based Observations of Aerosol Optical Thickness

Particles in the atmosphere reflect incoming sunlight, tending to cool the Earth below. Some particles, such as soot, also absorb sunlight, which tens to warm the ambient atmosphere. Aerosol optical depth (AOD) is a measure of the amount of particulate matter in the atmosphere, and is a key input to computer models that simulate and predict Earth's changing climate. The global AOD products from the Multi-angle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS), both of which fly on the NASA Earth Observing System's Terra satellite, provide complementary views of the particles in the atmosphere. Whereas MODIS offers global coverage about four times as frequent as MISR, the multi-angle data makes it possible to separate the surface and atmospheric contributions to the observed top-of-atmosphere radiances, and also to more effectively discriminate particle type. Surface-based AERONET sun photometers retrieve AOD with smaller uncertainties than the satellite instruments, but only at a few fixed locations. So there are clear reasons to combine these data sets in a way that takes advantage of their respective strengths. This paper represents an effort at combining MISR, MODIS and AERONET AOD products over the continental US, using a common spatial statistical technique called kriging. The technique uses the correlation between the satellite data and the "ground-truth" sun photometer observations to assign uncertainty to the satellite data on a region-by-region basis. The larger fraction of the sun photometer variance that is duplicated by the satellite data, the higher the confidence assigned to the satellite data in that region. In the Western and Central US, MISR AOD correlation with AERONET are significantly higher than those with MODIS, likely due to bright surfaces in these regions, which pose greater challenges for the single-view MODIS retrievals. In the east, MODIS correlations are higher, due to more frequent sampling of the varying AOD. These results demonstrate how the MISR and MODIS aerosol products are complementary. The underlying technique also provides one method for combining these products in such a way that takes advantage of the strengths of each, in the places and times when they are maximal, and in addition, yields an estimate of the associated uncertainties in space and time

Using the Data Fusion tecnique for producing tematic maps.

The University of Salerno (Italy) has recently been equipped with a pair of aerials that are able to receive data transmitted from various satellite platforms such as the NOAA and the Terra-1 and Aqua-1 EOS. These satellites are fitted with sensors that pick up information regarding soil and cloud temperature, the humidity level of the atmosphere, the presence of extraneous water, the presence of certain substances such as chlorophyll, the surface colour of the ocean and the presence of plankton with a spatial resolution that at its greatest reaches 250 m. Great use is therefore made of a vast amount of data that concerns our geographical area which allows for a fairly approximate assessment of the state of health (desertification, deforestation, abundance of surface water) and the potential risks (landslides, fires, plant and crop infections or diseases) to which the territory is prone. Nevertheless, the data is supplied with differing precision to the ground and with different pixel dimensions, therefore it is not possible to generate, taking as a starting point the data alone, (thematic map) which, besides furnishing qualitative information, also supplies correct and precise metric information. By using the Data Fusion technique it is possible to project the data supplied by various sensors onto a higher resolution image in order to obtain a representation that allows for a synthesis of all available information

The impact of horizontal heterogeneities, cloud fraction, and liquid water path on warm cloud effective radii from CERES-like Aqua MODIS retrievals

The impact of horizontal heterogeneities, liquid water path (LWP from AMSR-E), and cloud fraction (CF) on MODIS cloud effective radius (<i>r</i>_e), retrieved from the 2.1 μm (<i>r</i>_e2.1) and 3.8 μm (<i>r</i>_e3.8) channels, is investigated for warm clouds over the southeast Pacific. Values of <i>r</i>_e retrieved using the CERES algorithms are averaged at the CERES footprint resolution (&sim;20 km), while heterogeneities (<i>H</i>_&sigma;) are calculated as the ratio between the standard deviation and mean 0.64 μm reflectance. The value of <i>r</i>_e2.1 strongly depends on CF, with magnitudes up to 5 μm larger than those for overcast scenes, whereas <i>r</i>_e3.8 remains insensitive to CF. For cloudy scenes, both <i>r</i>_e2.1 and <i>r</i>_e3.8 increase with <i>H</i>_&sigma; for any given AMSR-E LWP, but <i>r</i>_e2.1 changes more than for <i>r</i>_e3.8. Additionally, <i>r</i>_e3.8–<i>r</i>_e2.1 differences are positive (<1 μm) for homogeneous scenes (<i>H</i>_&sigma; < 0.2) and LWP > 45 gm⁻², and negative (up to −4 μm) for larger <i>H</i>_&sigma;. While <i>r</i>_e3.8–<i>r</i>_e2.1 differences in homogeneous scenes are qualitatively consistent with in situ microphysical observations over the region of study, negative differences – particularly evinced in mean regional maps – are more likely to reflect the dominant bias associated with cloud heterogeneities rather than information about the cloud vertical structure. The consequences for MODIS LWP are also discussed

On the Detection and Monitoring of the Transport of an Asian Dust Storm Using Multi-Sensor Satellite Remote Sensing

Dynamical and physical features of a long range transported dust event originating in China affecting Korea early March 2008 are examined using an integrative multi-sensor and multi-algorithm approach. Aerosol loadings and their size mode were analyzed over both ocean and land surfaces using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), employing standard dark target (DT) and deep blue (DB) algorithms, and the Ångström exponent (AE). Anthropogenic absorbing aerosols and smoke were found to be significant over the Indochina Peninsula, the Philippines and southern China, while a mixture of dust and pollution were predominant over central to northern China, as identified by the AE analysis and the Multi-angle Imaging SpectroRadiometer (MISR) spherecitiy and plume height. Remarkable aerosol absorptions in both the near ultraviolet (UV) and the visible were spread over central, central western and northern China, probably due to aerosol mixtures including desert dust and industrial pollution as well as smoke from biomass burning as evidenced from the Ozone Monitoring Instrument (OMI). Long range transport is validated as dust storm reached up to 4–5 km vertically and a mixed cloud layer was identified over the Yellow Sea as disclosed by the vertical structure of dust aerosols as well as observed aerosols subtypes from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The real time detection and monitoring of the dust outbreak and its subsequent evolution are available through the infrared optical depth index (IODI), developed from the MTSAT-1R geostationary satellite imager

Online Εvaluation of Earth Observation Derived Indicators for Urban Planning and Management

Extensive urbanization and growth of population density have acquired a paramount interest towards a sustainable urban development. Earth Observation (EO) is an important source of information required for urban planning and management. The availability of EO data provides the immense opportunity for urban environmental indicators development easily derived by remote sensors. In this study, the state of the art methods were employed to develop urban planning and management relevant indicators that can be evaluated by using EO data. The importance of this approach lies on providing alternatives for improving urban planning and management, without consuming time and resources in collecting field or archived data. The evaluated urban indicators were integrated into a Web‐based Information System that was developed for online exploitation. The results for three case studies are therefore available online and can be used by urban planners and stakeholders in supporting their planning decisions

Continuity of Landsat Obersvations: Short Term Considerations

As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor\u27s design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community\u27s concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record