Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

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This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables (n=40). Ratio-based and soil-linerelated broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R 2 =0.67, RMSE=0.69 m 2 m À2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R 2 =0.77, RMSE=0.54 m 2 m À2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) (R 2 =0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved. D

The implementation of the United Nations Convention to Combat Desertification (UNCCD) needs agreed, scientifically sound and practical methodologies for monitoring and assessing the state and trend of land degradation as well as for monitoring the performance of management programmes. The lack of sufficient and integrated monitoring and assessment (M&A) has in the past been identified as a major constraint for combating desertification. Implementing efficient M&A programmes, however, requires careful analysis of the information needs of the different stakeholders, a clear scientific concept of the processes and drivers of land degradation and an analysis of the theoretical and practical possibilities for adequate M&A. This paper briefly analyses the information needs of diverse stakeholders, reviews existing M&A systems, and highlights key aspects for a scientifically sound approach to monitoring and assessment. Analysis of existing approaches shows that in spite of their relevance, standardised procedures for their implementation at operational scales are lacking. This is partly attributable to the lack of agreed and clear definitions, related difficulties in defining and hence in measuring the attributes chosen to represent land degradation and desertification and the varying degrees of paucity of field data. There is also the urgent need to better integrate bio-physical and socioeconomic aspects of desertification through a suitably robust scientific framework that links the drivers, processes and symptoms of desertification. Such a framework will allow for the identification of key variables to be monitored and will provide a basis for an improved forecasting and assessment of vulnerability, thereby providing highly important information for policy-and decision-making.

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The selection of calibration method is one of the main factors influencing measurement accuracy of soil properties estimation in visible and near infrared reflectance spectroscopy. In this study, the performance of three regression techniques, namely, partial least-squares regression (PLSR), support vector regression (SVR), and multivariate adaptive regression splines (MARS) were compared to identify the best method to assess organic matter (OM) and clay content in the salt-affected soils. One hundred and two soil samples collected from Northern Sinai, Egypt, were used as the data set for the calibration and validation procedures. The dry samples were scanned using a FieldSpec Pro FR Portable Spectroradiometer (Analytical Spectral Devices, ASD) with a measurement range of 350-2500 nm. The spectra were subjected to seven pre-processed techniques, e.g., Savitzky-Golay (SG) smoothing, first derivative with SG smoothing, second derivative with SG smoothing, continuum removed reflectance, standard normal variate and detrending (SNV-DT), multiplicative scatter correction (MSC) and extended MSC. The results of cross-validation showed that in most cases MARS models performed better than PLSR and SVR

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