A method for the radiometric correction of satellite imagery over mountainous terrain has been developed to remove atmospheric and topographic effects. The algorithm accounts for horizontally varying atmospheric conditions and also includes the height dependence of the atmospheric radiance and transmittance functions to simulate the simplified properties of a three-dimensional atmosphere. A database has been compiled that contains the results of radiative transfer calculations (atmospheric transmittance, path radiance, direct and diffuse solar flux) for a wide range of weather conditions. A digital elevation model is used to obtain information about surface elevation, slope, and orientation. Based on the Lambertian assumption the surface reflectance in rugged terrain is calculated for the specified atmospheric conditions. Regions with extreme illumination geometries sensitive to BRDF effects can be optionally processed separately. The method is restricted to high spatial resolution satellite sensors with a small swath angle such as the Landsat thematic mapper and Systeme pour l’Observation de la Terre high resolution visible, since some simplifying assumptions were made to reduce the required image processing time.

A comparison of topographic correction methods is conducted for Landsat-5 TM, Landsat-7 ETM+, and SPOT-5 imagery from different geographic areas and seasons. Three successful and known methods are compared: the semi-empirical C correction, the Gamma correction depending on the incidence and exitance angles, and a modified Minnaert approach. In the majority of cases the modified Minnaert approach performed best, but no method is superior in all cases.

Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

The automatic correction of atmospheric effects currently requires visible to short‐wave spectral bands (400–2500 nm) to derive high accuracy surface reflectance data. Common techniques employ spectral correlations of dark targets in the short‐wave infrared (SWIR, around 2.2 µm), blue (480 nm) and red (660 nm) regions to derive the aerosol optical depth. A large number of current Earth‐observing satellite sensors have only three or four spectral channels in the visible and near‐infrared (VNIR) region (400–1000 nm), making an automatic image‐based atmospheric correction very difficult. This contribution presents a new algorithm and first results with VNIR imagery. The method starts with the assumption of average clear atmospheric conditions (aerosol optical depth AOD = 0.27, corresponding to a visibility of 23 km) and calculates the surface reflectance in the red and near‐infrared (NIR) bands. The second step derives a mask of dark vegetation pixels. It is calculated using multiple thresholds of vegetation index combined with red and NIR surface reflectance values. Then the red band surface reflectance for the dark pixels is estimated from the NIR reflectance as ρ_red = 0.1 ρ_nir, from which the aerosol optical depth (or visibility) can be calculated. The core of the VNIR algorithm consists of two subsequent iteration loops (visibility and ρ_red) to improve the visibility estimate. Results of the VNIR method are presented for Landsat‐5 Thematic Mapper (TM) and Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) imagery using only the first four bands. The performance of the method is compared to the established dark pixel technique where the SWIR bands are included. Results show that the deviation between both methods is usually less than 0.005 reflectance units if measured in terms of the scene‐average reflectance, indicating a useful potential for this approach.

Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

A de‐shadowing technique is presented for multispectral and hyperspectral imagery over land acquired by satellite/airborne sensors. The method requires a channel in the visible and at least one spectral band in the near‐infrared (0.8–1 µm) region, but performs much better if bands in the short‐wave infrared region (around 1.6 and 2.2 µm) are available as well. The algorithm consists of these major components: (i) calculation of the covariance matrix and zero‐reflectance matched filter vector, (ii) derivation of the unscaled and scaled shadow function, (iii) histogram thresholding of the unscaled shadow function to define the core shadow areas, (iv) region growing to include the surroundings of the core shadow areas for a smooth shadow/clear transition, and (v) de‐shadowing of the pixels in the final shadow mask. The critical parameters of the method are discussed. Example images from different climates and landscapes are presented to demonstrate the successful performance of the shadow removal process over land surfaces.