Impact of potentially toxic elements on pines in a former ore processing mine: Exploitation of hyperspectral response from needle and canopy scales.

Anthropic potentially toxic element (PTE) releases can lead to persistent pollution in soil. Monitoring PTEs by their detection and quantification on large scale is of great interest. The vegetation exposed to PTEs can exhibit a reduction of physiological activities, structural damage … Such vegetation trait changes impact the spectral signature in the reflective domain 0.4-2.5 µm. The objective of this study is to characterize the impact of PTEs on the spectral signature of two pine species (Aleppo and Stone pines) in the reflective domain and ensure their assessment. The study focuses on nine PTEs: As, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Zn. The spectra are measured by an in-field spectrometer and an aerial hyperspectral instrument on a former ore processing site. They are completed by measurements related to vegetation traits at needle and tree scales (photosynthetic pigments, dry matter, morphometry …) to define the most sensitive vegetation parameter to each PTE in soil. A result of this study is that chlorophylls and carotenoids are the most correlated to PTE contents. Context-specific spectral indices are specified and used to assess metal contents in soil by regression. These new vegetation indices are compared at needle and canopy scales to literature indices. Most of the PTE contents are predicted at both scales with Pearson correlation scores between 0.6 and 0.9, depending on species and scale.

SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval.

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.

SIELETERS, an airborne infrared dual-band spectro-imaging system for measurement of scene spectral signatures.

More and more, hyperspectral images are envisaged to improve the aerial reconnaissance capability of airborne systems, both for civilian and military applications. To confirm the hopes put in this new way of imaging a scene, it is necessary to develop airborne systems allowing the measurement of the spectral signatures of objects of interest in real conditions, with high spectral and spatial resolutions. The purpose of this paper is to present the design and the first in-flight results of the dual-band infrared spectro-imaging system called Sieleters. This system has demonstrated simultaneously a ground sampling distance of 0.5m, associated with a spectral resolution of 11 cm(-1) for the Mid-Wave InfraRed (MWIR) and 5 cm(-1) for the Long-Wave InfraRed (LWIR).

Directional effects consideration to improve out-doors emissivity retrieval in the 3-13 mum domain.

This work analyses and solves for issues encountered when retrieving surface emissivity in LWIR (750 to 1250 cm(-1)) and MWIR (2000 to 3500 cm(-1)) bands under outdoor conditions. The Spectral Smoothness method, which takes advantage of high spectral resolution measurements to solve for temperature emissivity separation, and which has already proven its efficiency in the LWIR domain, was applied in an experimental campaign to assess its ability to operate both in the LWIR and MWIR domains. In the MWIR band, directional behaviour of surface emissivity is shown to be a source of systematic errors in the retrieved emissivity and a new method, called SmaC (SMoothness And Continuity), corrects for this error by providing quantitative estimates on the deviation of the surface from Lambertian behavior.

Multiple-scattering lidar retrieval method: tests on Monte Carlo simulations and comparisons with in situ measurements.

A multiple-field-of-view (MFOV) lidar measurement and solution technique has been developed to exploit the retrievable particle extinction and size information contained in the multiple-scattering contributions to aerosol lidar returns. We describe the proposed solution algorithm. The primary retrieved parameters are the extinction coefficient at the lidar wavelength and the effective particle diameter from which secondary products such as the extinction at other wavelengths and the liquid-water content (LWC) of liquid-phase clouds can be derived. The solutions are compared with true values in a series of Monte Carlo simulations and with in-cloud measurements. Good agreement is obtained for the simulations. For the field experiment, the retrieved effective droplet diameter and LWC for the available seven cases studied are on average 15% and 35% (worst case) smaller than the measured data, respectively. In the latter case, the analysis shows that the differences cannot be attributed solely to lidar inversion errors. Despite the limited penetration depth (150-300 m) of the lidar pulses, the results of the studied cases indicate that the retrieved lidar solutions remain statistically representative of measurements performed over the full cloud extent. Long-term MFOV lidar monitoring could thus become a practical and economical option for cloud statistical studies but more experimentation on more varied cloud conditions, especially for LWC, is still needed.