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The MEdium Resolution Imaging Spectrometer (MERIS) can be used to monitor vegetation dynamics at regional to global scales. However, the spatial resolutions provided by this sensor (300 or 1200 m) might not be appropriate to monitor fragmented landscapes. This is why the synergistic use of MERIS full resolution (300 m) and a high spatial resolution land use/land cover database (25 m) is studied in this paper. An unmixing-based data fusion approach was used to produce images that have the spectral and temporal resolutions provided by MERIS and a Landsat-like spatial resolution. The central part of The Netherlands was selected to illustrate this approach. Seven MERIS full resolution and one Landsat TM image were available over this area too. The radiometric characteristics of the fused images were evaluated at 25 and at 300 m. After this quantitative quality assessment, the best fused images were used to compute MTCI and MGVI profiles for the main land cover types present in the study area.

Almost all natural surfaces exhibit an individual anisotropic reflectance behaviour due to the contrast between the optical properties of surface elements and background and the geometric surface properties of the observed scene. The resulting bidirectional effects are present in all reflectance data and thus occur as well in various vegetation indices (VI’s) retrieved from multiangular data. No matter whether these effects are considered as noise or as a source of additional information, accurate knowledge about their magnitude is important. This preliminary study is based on data of the spaceborne ESA-mission CHRIS (Compact High Resolution Imaging Spectrometer) onboard PROBA-1 and on ground-based spectrodirectional measurements performed with the dual view field goniometer system FIGOS. The objectives of this study are focused on directional effects in CHRIS and FIGOS reflectance data of a Triticale field as well as on the variability of retrieved vegetation indices for selected view angles in both multiangular datasets.

A program has been written to create image mosaics from ASA_WSS_1P ASAR-WSS level-1 products, providing an overview of the imaged area. The ability to generate WSS mosaics facilitates the study of several WSS product features. This study focuses on their radiometric and geometric characteristics. The incidence angle variation of 16 to 43 degrees across beams SS1 through SS5 creates large differences in the nominal near- and far-range backscatter intensities. Radiometric calibration is applied to the mosaics, taking the range-spreading loss as well as the incidence angle effect into account, normalising for systematic radiometric trends. The five WSS beams acquire data with a substantial overlap (typically several hundred range samples). These overlap regions are of interest because the same ground targets are imaged by two different beams. We calculate the mean radiometric differences between the two beams for each overlap region, and draw conclusions based on the statistics. WSS geolocation accuracy is assessed for the image mosaics as well, using two methods. First, predicted positions of transponders in the Netherlands and Canada are compared to measured positions. Second, terrain geocoding of selected WSS scenes is performed, using DORIS precise state vectors and a digital terrain model (DTM). The geolocation accuracy is estimated using survey points or ground control points (GCPs) derived from topographic maps. The radiometric and geometric investigations confirm a high quality of the level-1 WSS products.

The GonioExp06 field campaign from 19th to 24th of June 2006 is part of a strategic initiative to improve bio-geo-chemo-physical parameter estimation from optical remote sensing data. The focus of the research is on the investigation of the directional fraction of hyperspectral data, emphasising the value adding effect of directional information as supplement to the well known spectral one. Three field goniometric systems (FIGOS, MGS and AIS A gon) of different design, an extensive accompanying measurement campaign and simultaneous multidirectional data takes by Proba/CHRIS (space) and ROSIS (airborne) are combined to create the data base necessar y to investigate the radiative transfer between the surface and the sensors and to contribute improving physical modelling and inversion techniques. The application focus is the “precision” domain in land use (agriculture, forestry, nature conservation).

Leaf area index (LAI) is a key variable for the understanding and modeling of several ecophysiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors such as CHRIS/PROBA. The spaceborne ESA-mission CHRIS/PROBA provides multi-temporal observations of the land surface in the spectral and directional information dimensions. This system represents a rich source of information for Earth observation purposes specifically adapted for monitoring the high dynamic of agricultural crops. For this purpose a radiative transfer model (RTM) is coupled to a canopy structure dynamic model (CSDM). The coupled models are used to exploit the complementary content of the spectral and temporal information dimensions for LAI estimation over a maize canopy. The resulting estimation of the temporal and spatial variation of LAI is improved by integrating multi-temporal CHRIS/PROBA data and ground meteorological observations. Further, the presented method provides the continuous LAI time course over the season, which is required by crop growth and land surface process models.

A multitemporal assessment of chlorophyll-a distribution using MERIS/ENVISAT FR data from 2003 through 2005 shows increased chlorophyll concentrations correlated to high algae bloom during the dry season from July to September. Intra- and interannual distribution patterns were investigated using spectral band ratio approaches. Maximum chlorophyll-a concentrations in Lake Kivu were found along the lake’s shore line, the southern basin, Bukavu Bay and Kabuno Bay. Regional and temporal variations are lowest in the lake’s central northern basin. Increased knowledge on the lake’s regional and temporal chlorophyll-a distribution may help for an improved description of the main features of nutrient cycling.

Sun and sensor geometry cause directional effects in remotely sensed reflectance data which can influence the estimation of biophysical and biochemical variables. Previous studies have indicated that bidirectional measurements contain added information with which the accuracy of derived plant structural parameters can be increased. Because accurate biochemistry mapping is linked to vegetation structure, estimates of nitrogen concentration (CN) and water content (CW) might be indirectly improved with multiangular information. We analyzed data of the spaceborne ESA-mission CHRIS on-board PROBA-1, which provides hyperspectral and directional data. The images were acquired in July 2006 over a forest study site in Switzerland. From each of the five CHRIS images (five different viewing zenith angles) we extracted 60 crown spectra, which correspond to field-sampled trees. Then we developed four-term models by regressing lab-measured CN and CW on two datasets either consisting of original reflectance values (SPEC) or continuum-removed data (BNC). The wavebands used in the regression models were determined with a subset selection algorithm. For the data of all view angle combinations particular models were generated, in total 31 equations were evaluated per spectral dataset by comparing the coefficients of determination (R2) and cross-validated root mean square errors. The results showed that directional information contained in multiangular data improved regression models for CN and CW estimation and lowered RMS errors. Considerable contribution can be achieved with data of a second and third viewing zenith angle. Monodirectional models developed on data of backward scattering angles performed in general better than models based on forward scattering data. Multidirectional models based on combinations of off-nadir data performed best for CN but not for CW. These findings support the potential of multiangular Earth observations for ecological monitoring and modeling studies.

The physically based Modular Inversion & Processing System (MIP) is used in a processing chain for inland water constituent retrieval from MERIS level 1B full resolution data. Reformatting, data import, water/land masking, atmospheric correction and water constituent retrieval are fully automized, in order to allow the efficient analysis of large data quantities in a time series of water quality in Swiss Lakes. To account for the temporal variation of atmosphere and water properties, a set of input parameters is optimized specifically for Lake Constance. The algorithm’s sensitivity to its input parameters is studied, allowing the derivation of a first guess parameterization. Results of successfully processed data are presented and reasons for processing errors are discussed.

Among the several applications that will take advantage of the newly available data from the ALOS PALSAR instrument, considerable interest is in the peculiar features that derive from the penetration and polarimetric capabilities of the system. These capabilities, new for a single spaceborne sensor, need specific software tools for the processing of the different acquisition modes. This paper presents a verification processor, developed under ESA contract, for the generation of polarimetric, interferometric and polarimetric-interferometric geocoded products derived from ALOS PALSAR data. The processor, developed with a modular approach, contains the following main elements: - Phase-preserving fine resolution processor; - Phase-preserving ScanSAR processor; - Interference removal tools; - Polarimetric calibration tools; - Polarimetric analysis tools; - Fine resolution interferometric processor; - ScanSAR interferometric processor; - Polarimetric-interferometric processor; - Geocoding; - Atmospheric modelling tools. The processor architecture is presented; highlights are given on specific modules and algorithms. Early results are shown, in particular of the processing of polarimetric and polarimetric-interferometric data over different test sites.

Accurate geolocation of SAR imagery enables not only precise overlays with other data sources in a common map geometry, but also normalisation for the systematic influence of terrain on image radiometry. We begin by describing our verifications of the geometric behaviour of ENVISAT ASAR products, including all image mode (IM), alternating polarisation (AP), and wide swath (WS) types: IMS, IMP , IMM, IMG, APS, APP , APM, APG, WSM, and WSS. Radar transponders in Canada and Europe are used as easily identifiable targets in radar images to test the accuracy of the nominal timing and state vector annotations accompanying each product. Accuracies achievable using DORIS precise state vectors are also evaluated. In addition to ENVISAT's ASAR, geolocation accuracies achievable using ERS-1/2 and ALOS PALSAR data are demonstrated. Given accurate knowledge of the acquisition geometry of a SAR image from one of the above sensors together with a digital elevation model (DEM) of the area imaged, the process of terrain geocoding is used to transform a diverse set of images into a common reference map geometry. The prerequisite DEM combined with accurate knowledge of the acquisition geometry also enables a radiometric correction, whereby variations in terrain specific to each scene are normalised to a common standard. Thematic interpretation benefits from such pre-processing: we demonstrate improved thematic discriminations using product overlays in a common map geometry where radiometric terrain correction (RTC) has been applied in comparison to typical GTC results.

This paper presents a verification processor, developed under ESA contract, for the generation of polarimetric, interferometric and polarimetric-interferometric geocoded products derived from SAR data obtained from the ALOS PALSAR instrument. The processor, developed with a modular approach, contains the following main elements: - Phase-preserving fine resolution processor; - Phase-preserving ScanSAR processor; - Interference removal tools; - Polarimetric calibration tools; - Polarimetric analysis tools; - Fine resolution interferometric processor; - ScanSAR interferometric processor; - Polarimetric-interferometric processor; - Geocoding; - Atmospheric modelling tools. All of the PALSAR acquisition modes (i.e. stripmap in one, two and full polarisations as well as ScanSAR) are supported both starting from raw and focused data. The processor architecture is presented; highlights are given on modules and algorithms specifically developed for handling the peculiarities of these L-Band SAR data. Examples are shown obtained during the first year of operation of the system, over a number of test sites and of different types of products and acquisition modes.

Monitoring vegetation dynamics is fundamental to improve Earth systems models and to increase our understanding of the terrestrial carbon cycle and the interactions biosphere-climate. Medium spatial resolution sensors, like MERIS, have a great potential to study these dynamics at regional/global scales. However, the spatial resolution provided by MERIS (300m in full resolution mode) might not be appropriate over highly heterogeneous landscapes. This is why the synergistic use of MERIS full resolution (FR) and Landsat TM data is studied in this paper. An unmixing-based data fusion approach was applied to a time series of MERIS FR images acquired over the Netherlands in 2003. The selected data fusion approach uses the linear mixing model and the information derived from Landsat TM imagery acquired in the same year to produce images that have the spectral and temporal resolutions provided by MERIS but with the spatial resolution of Landsat TM. After the fusion, a quantitative assessment of the quality of the fused images was done in order to assess the validity of the proposed methodology and to evaluate the radiometric characteristics of the images. Finally, the time series of fused images was used to compute land cover specific NDVI, MTCI and MGVI profiles.

Due to the specific climate conditions in the dry regions of Africa, an effective integrated water management is of great interest for the national authorities. This paper explains the workflow of the Aquifer Project product ‘Water and Vegetation Monitoring over entire Aquifer’. The large scale water and vegetation monitoring will be accomplished for the transboundary aquifer Iullemeden, which represents one of the major freshwater reservoirs of Africa. By the synergetic use of radar and optical data land cover classification maps for four different times within one season are provided. The changes between these dates as well as the seasonal vegetation and water dynamics were analysed. Furthermore, by inclusion of specific contextual features of irrigated vegetation in the region, an irrigated vegetation map could be derived. The results demonstrate the high potential of Earth Observation data for the decision support for a more efficient water management in Africa.