Ils Reusen

The PROBA-V mission: image processing and calibration

With the launch of PROBA-V (Project for On-Board Autonomy – Vegetation) in 2013, the continuity and availability of global land-coverage data in four multispectral bands are ensured for the SPOT (Système Pour l’Observation de la Terre)-VEGETATION user community. This community has been served for already more than 14 years with high-quality 1 kilometre-resolution data. To guarantee continuation of this high quality over the full lifetime of PROBA-V, an operational processing platform and in-flight calibration algorithms have to be in place, which fully consider the specific PROBA-V platform and instrument design characteristics. Data quality has to be ensured for all available product levels, i.e. from the radiometrically corrected radiance data to the 10 day global synthesis. In this article we first focus on some specific design characteristics, which impose some challenges for data processing and calibration. Next, a technical description is given for all the processing steps such as mapping, cloud masking, atmospheric correction, and compositing. The functioning of the Image Quality Centre (IQC) is described. The IQC is in charge of the assessment of the PROBA-V performance, the analysis of the image quality, and the radiometric and geometric calibration after launch. Finally information is given on the distribution of the various products to the user community.

Effect of DEM Uncertainty on the Positional Accuracy of Airborne Imagery

The geometric and atmospheric processing of airborne imagery is a complex task that involves many correction steps. Geometric correction is particularly challenging because slight movements of the aircraft and small changes in topography can have a great impact on the geographic positioning of the processed imagery. This paper focused on how uncertainty in topography, represented by a digital elevation model (DEM), propagates through the geometric correction process. We used a Monte Carlo analysis, in which, first, a geostatistical uncertainty model of the DEM was developed to simulate a large number of DEM realizations. Next, geometric correction was run for each of the simulated DEMs. The analysis of the corrected images and their variability provided valuable information about the positional accuracy of the corrected image. The method was applied to a hyperspectral image of a mountainous area in Calabria, Italy, by using the Shuttle Radar Topography Mission-DEM as the topographic information source. We found out that the uncertainty varies greatly over the whole terrain and is substantial at large off-nadir viewing angles in the across-track direction. Also, positional uncertainty is larger in rugged terrains. We conclude that Monte Carlo uncertainty propagation analysis is a valuable technique in deriving quality layers that inform end users about the positional accuracy of airborne imagery, and we recommend that it is integrated in the operational processing steps of the Processing and Archiving Facilities.

An Overview of the Regional Experiments for Land-atmosphere Exchanges 2012 (REFLEX 2012) Campaign

The REFLEX 2012 campaign was initiated as part of a training course on the organization of an airborne campaign to support advancement of the understanding of land-atmosphere interaction processes. This article describes the campaign, its objectives and observations, remote as well as in situ. The observations took place at the experimental Las Tiesas farm in an agricultural area in the south of Spain. During the period of ten days, measurements were made to capture the main processes controlling the local and regional land-atmosphere exchanges. Apart from multi-temporal, multi-directional and multi-spatial space-borne and airborne observations, measurements of the local meteorology, energy fluxes, soil temperature profiles, soil moisture profiles, surface temperature, canopy structure as well as leaf-level measurements were carried out. Additional thermo-dynamical monitoring took place at selected sites. After presenting the different types of measurements, some examples are given to illustrate the potential of the observations made.

Sensitivity of Reflectance to Water Vapor and Aerosol Optical Thickness

The atmospheric condition parameters used in the radiative transfer-based atmospheric correction (AC) are often uncertain. This uncertainty propagates to the estimated reflectance. The reflectance, is, however, not equally sensitive to all the parameters. A sensitivity analysis (SA) helps in prioritizing the parameters. The objective of this study was to perform an SA of reflectance to water vapor concentration (wv) and aerosol optical thickness (AOT). SA was performed using the Fourier amplitude sensitivity test (FAST) method, which computes sensitivity indices (SI) of these parameters. Besides variation in the two parameters, we also studied the effect of surface albedo on the SI by quantifying SI for three target surfaces (in the spectral range 0.44-0.96 μm): 1) a dark target (water); 2) a bright target (bare soil); and 3) a target having low albedo in the visible and high albedo in near-infrared range (forest). For AOT, high (≈0.9) SI values were observed at the nonwater absorption wavelengths. For wv, high SI values were observed at wavelengths, where strong absorption features are located and when the surface albedo was high. For the dark target, the effect of AOT was prominent throughout the spectral range. We found that the sensitivity of reflectance to wv and AOT is a function of wavelength, strength of the absorption features, and surface albedo. We conclude that AOT is a more important parameter for dark targets than wv even at the principal absorption feature. For bright targets, the importance of wv and AOT depends on the strength of the absorption feature.

Multiple vicarious calibration using combined accuracy estimation

Radiometric calibration often employs several independent vicarious calibration techniques to increase robustness and accuracy. We present a statistical methodology for combining results in a hierarchical scheme. The method, developed for the PROBA-V remote sensing mission, is based on handling and propagating of accuracies in accordance with the ISO GUM. Robust estimation is performed and outliers removed. Results over different sites are combined using weighted averaging. Weighted linear regression is used for temporal averaging. Results from different methods are combined taking into account possible bias. Finally an operational update strategy is proposed which relies on a significance criterion.

An optimization approach to estimate and calibrate column water vapour for hyperspectral airborne data

ABSTRACT The article describes a novel approach to estimate and calibrate column water vapour (CWV), a key parameter for atmospheric correction of remote-sensing data. CWV is spatially and temporally variable, and image-based methods are used for its inference. This inference, however, is affected by methodological and numeric limitations, which likely propagate to reflectance estimates. In this article, a method is proposed to estimate CWV iteratively from target surface reflectances. The method is free from assumptions for at sensor radiance-based CWV estimation methods. We consider two cases: (a) CWV is incorrectly estimated in a processing chain and (b) CWV is not estimated in a processing chain. To solve (a) we use the incorrect estimations as initial values to the proposed method during calibration. In (b), CWV is estimated without initial information. Next, we combined the two scenarios, resulting in a generic method to calibrate and estimate CWV. We utilized the hyperspectral mapper (HyMap) and airborne prism experiment (APEX) instruments for the synthetic and real data experiments, respectively. Noise levels were added to the synthetic data to simulate real imaging conditions. The real data used in this research are cloud-free scenes acquired from the airborne campaigns. For performance assessment, we compared the proposed method with two state-of-the-art methods. Our method performed better as it minimizes the absolute error close to zero, only within 8–10 iterations. It thus suits existing operational chains where the number of iterations is considerable. Finally, the method is simple to implement and can be extended to address other atmospheric trace gases.

EUFAR goes hyperspectral in FP7

Two FP6 initiatives i) HYRESSA, hyperspectral remote sensing in Europe specific support action, and ii) EUFAR, European Facility for Airborne Research in Environmental and Geo-sciences, have joined forces in FP7. The FP7 Integrating Activity EUFAR (including HYRESSA) is now a network of 33 European airborne data providers and experts in airborne measurements. With the support of the European Commission, EUFAR provides European scientists with trans-national access to 6 airborne instruments (including hyperspectral imaging sensors) and 20 instrumented aircraft and early-stage researchers and university lecturers with training courses on airborne measurements. This paper reports on EUFAR activities and opportunities for European researchers with special attention to activities and opportunities related to airborne hyperspectral imaging.

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (Lrs,t(λ)). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (ρt,pre) where effects associated with Lrs,t(λ) are less influential. The method identifies pixels comprising pure materials from ρt,pre. AOD values at the pure pixels are iteratively estimated using l2-norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06–0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5–10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

Global sensitivity analysis of water vapour and visibility for atmospheric correction

This study aims to quantify sensitivity of two atmospheric condition parameters: water vapour concentration (wv) and visibility in atmospheric correction processes in an operational processing chain. wv and visibility are important atmospheric condition parameters when retrieving surface reflectance from at-sensor radiance. To save cost, these parameters are often estimated using an image-based method. Their values are therefore uncertain, which in turn propagates to the surface reflectance in the atmospheric correction process. This study proposes an e-FAST based methodology that quantifies sensitivity of the two parameters in order to calculate their relative importance. The methodology is demonstrated with HyMap data. The results show that the two parameters have high sensitivity indices (> 0.8) and are dependent on the wavelength. This indicates that both parameters are important. The uncertainty in the parameters can possibly be further reduced by improving the estimation method.

Optimizing the range of atmospheric condition parameters to avoid over and under-estimation of uncertainty

This paper aims to solve the critical problem of uncertainty optimization of two atmospheric condition parameters: water vapor concentration (wv) and visibility. Both are used in atmospheric correction (AC) processes when retrieving surface reflectance from at-sensor radiance. Parameter uncertainty is often over or under estimated. A framework is proposed that determines the relation (indirect) of the parameters with estimated surface reflectance and uses this to reduce the uncertainty. Results show that optimizing the uncertainty of the parameters reduces the range of visibility from 18–42 to 31–39 km and the range of wv from 1.6–2.9 to 2.05–2.30 g cm−2 for the class agriculture. Also, the total number of simulations that is needed for further studies such as an uncertainty or a sensitivity analysis in AC is reduced. The study concludes that there is an inverse effect of wv and visibility on the surface reflectance.