Wouter Dierckx

PROBA-V mission for global vegetation monitoring: standard products and image quality

PROBA-V is a new global vegetation monitoring mission, to be launched in the second quarter of 2013. PROBA-V has been developed to show a consistent performance with SPOT-VEGETATION (SPOT-VGT) data, with similar spectral bands but with an improved spatial resolution of 1/3 km. The innovative mission concept has led to several key research topics related to image quality, which are discussed in this article. To support the existing VEGETATION user community, the data products for PROBA-V continue to provide daily top of canopy synthesis (S1-TOC) and 10 day synthesis products (S10-TOC). In addition, the new top of atmosphere daily synthesis (S1-TOA) products and a radiometrically/geometrically corrected (level 1C) product in raw resolution will also be provided for scientific users.

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.

Spatially variable filters — Expanding the spectral dimension of compact cameras for remotely piloted aircraft systems

Recent advances in hyperspectral imaging techniques using spectral filters deposited directly onto an image sensor chip, and suitable for RPAS platforms, are reported in this paper. New filter configurations for a compact spectral camera are described and a prototype of the compact hyperspectral payload developed for small RPAS systems, as well as the first data acquired with the new camera are presented. The spectral range of the payload was optimized for earth observations such as vegetation monitoring or water quality studies. Although the spatially variable filters have to date only been used in small satellite sensors, this technology has a clear potential for RPAS platforms.

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.

In-Orbit Radiometric Calibration and Stability Monitoring of the PROBA-V Instrument

Since its launch in May 2013, the in-orbit radiometric performance of PROBA-V has been continuously monitored. Due to the absence of on-board calibration devices, in-flight performance monitoring and calibration relies fully on vicarious calibration methods. In this paper, the multiple vicarious calibration techniques used to verify radiometric accuracy and to perform calibration parameter updates are discussed. Details are given of the radiometric calibration activities during both the commissioning and operational phase. The stability of the instrument in terms of overall radiometry and dark current is analyzed. Results of an independent comparison against MERIS and SPOT VEGETATION-2 are presented. Finally, an outlook is provided of the on-going activities aimed at improving both data consistency over time and within-scene uniformity.

Evaluation of PROBA-V Collection 1: Refined Radiometry, Geometry, and Cloud Screening

PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Système Pour l’Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme; (ii) quantification of the effect of the reprocessing by comparing C1 to C0; and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfaces. Stepwise updates to the visible and near infrared (VNIR) absolute calibration in C0 and the application of degradation models to the SWIR calibration in C1 result in sudden changes between C0 and C1 Blue, Red, and NIR TOC reflectance in the first year, and more gradual differences for short-wave infrared (SWIR). Other changes result in some bias between C0 and C1, although the root mean squared difference (RMSD) remains well below 1% for top-of-canopy (TOC) reflectance and below 0.02 for the normalized difference vegetation index (NDVI). Comparison to METOP/AVHRR shows that the recent reprocessing campaigns on SPOT/VGT and PROBA-V have resulted in a more stable combined time series.

A 100-m ground resolution global daily coverage earth observation mission

PROBA-V has been successfully launched on 7th May 2013 and is providing a global monitoring in the continuity of the SPOT-VEGETATION mission. The progress in terms of ground resolution between Spot VGT and PROBA-V is a factor 3 (1 km to 1/3 km ground resolution product).

Proba-V cloud detection Round Robin: Validation results and recommendations

This paper discusses results from 12 months of a Round Robin exercise aimed at the inter-comparison of different cloud detection algorithms for Proba-V. Clouds detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking directly translates into significant uncertainty in the retrieved downstream geophysical products. Cloud detection is particularly challenging for Proba-V due to the presence of a limited number of spectral bands and the lack of thermal infrared bands. The main objective of the project was the inter-comparison of several cloud detection algorithms for Proba-V over a wide range of surface types and environmental conditions. Proba-V Level 2a products have been distributed to six different algorithm providers representing companies and research institutes in several European countries. The considered cloud detection approaches are based on different strategies: Neural Network, Discriminant Analysis, Multi-spectral and Multi-textural Thresholding, Self-Organizing Feature Maps, Dynamic Thresholding, and physically-based retrieval of Cloud Optical Thickness. The results from all algorithms were analysed and compared against a reference dataset, consisting of a large number (more than fifty thousands) of visually classified pixels. The quality assessment was performed according to a uniform methodology and the results provide clear indication on the potential best-suited approach for next Proba-V cloud detection algorithm.