PROBA-V
Menu

Product Evaluation Introduction

In order to assess the PROBA-V data products’ quality, comparisons with various reference datasets are performed. The validations are performed using similar or comparable land surface variables (e.g. surface reflectance and NDVI) that are derived from other satellite platforms, such as from the MODIS satellites. The product validation follows a standardised protocol, in which the following statistical metrics are calculated:

  • Geometric mean (GM) regression: the geometric mean regression uses an orthogonal model to calculate the slope and intercept values and includes the errors of both satellite datasets.
  • Accuracy: is calculated as the Mean Bias Error (MBE), and is a measure of the overall average difference. It preserves the sign of the difference.

  • Precision: is calculated as the standard deviation of the bias (MBE) and represents the dispersion of the product retrievals around their expected value. 

  • Uncertainty: is calculated as the Root Mean Squared Difference (RMSD) and measures how far the difference between datasets deviates from 0. It is an expression of overall difference, including random and systematic differences. 

In the product evaluations, the following aspects are examined:

Product completeness

The completeness of a dataset is assessed in a spatial and temporal extent. Data can be incomplete due to for example bad radiometric quality in one of the four PROBA-V bands.

Statistical consistency

The difference magnitude is presented as difference histograms between the examined and reference dataset, as well as global and/or regional scatterplots.

Spatial consistency

Spatial consistency refers to the realism and repeatability of the spatial distribution of retrievals over the globe, including the absence of artefacts (e.g., missing data, stripes, unrealistic values, etc.), based on expert knowledge.

Temporal consistency

The realism of temporal variations of the product are qualitatively assessed for point locations, well distributed over the globe. Spatio-temporal evolution of validation metrics is assessed through Hovmöller plots.

Reports

Reports Collection 2
  • PROBA-V Collection 2 Change Document: In 2022, the entire PROBA-V archive (spanning from October 2013 – present) was re-processed to Collection 2. This document summarizes the major and minor modifications to algorithms, data, and metadata.
  • PROBA-V Collection 2 Evaluation: This report provides a comparison of the reprocessed PROBA-V data (Collection 2) with the previous version of the data archive (Collection 1). The comparison was carried out on S1, S5 and S10 surface reflectance and NDVI data over the entire Collection 2 and Collection 1 data archives for the three resolutions. The evaluation focuses on; (i) quantification of the effect of the reprocessing by comparing C2 to C1; (ii) evaluation of the statistical, spatial and spatio-temporal consistency with SPOT-VGT C3 and Sentinel-3 VGT-SYN products, and (iii) evaluation of the statistical, spatial and spatio-temporal consistency of the PROBA-V archive through comparison to METOP/AVHRR and MODIS. The NDVI profiles for all LANDVAL sites of all datasets used in the evaluation can be found in the digital annex (NDVI_profiles.001.zip - NDVI_profiles.002.zip).
  • PROBA-V Collection 2 Atmospheric Correction Validation: This report provides the validation of the new atmospheric correction for PROBA-V Collection 2 using the ACIX approach for validation.
  • PROBA-V Collection 2 Cloud Masking Validation Report for 1 km, 300 m and 100 m data: These reports provide the validation results of the new cloud detection method for the different resolutions.
Reports Collection 1
  • PROBA-V Collection 1 Change Summary: 2016, the entire PROBA-V archive (spanning from October 2013 - present) was re-processed to Collection 1. This document summarizes the major and minor modifications to algorithms, data and metadata.
  • PROBA-V Collection 1 Cloud Mask Evaluation: This report documents the validation of the cloud mask method for PROBA-V Collection 1.
  • Quality Note - Extending the SPOT/VEGETATION-PROBA-V Collection 1 archive with Sentinel-3 SYN-VGT products: The Sentinel-3 Ocean and Land Colour Instrument (OLCI), in synergy with the Sea and Land Surface Temperature Radiometer (SLSTR) instrument, is foreseen to provide continuity to the SPOT/VGT capability (Donlon et al., 2012). The ESA Sentinel-3 synergy (SYN_VGT) products replicate the attributes and quality of standard 1 km SPOT/VGT products through innovative spectral remapping and co-location techniques (North and Heckel, 2010).
  • Comparison SPOT-VGT C3 - PROBA-V C1: A short note reporting on the comparison between SPOT-VGT and PROBA-V surface reflectances and NDVI for data before and after the re-processings. The comparison was performed on S10 composites for the overlapping observational period (November 2013 - May 2014).
Other reports
  • SPOT-VGT Collection 3 Evaluation: The entire SPOT-VGT data archive (21 April 1998 - 31 May 2014) was reprocessed in 2015 - 2016. The paper describes the comparison S10 surface reflectance and NDVI from the re-processed Collection 3 with the previous Collection 2, as well as a consistency analysis between Collection 3 VGT1 and VGT2 data. Further, the Collection 3 data were compared with external satellite reference data (MODIS and METOP-AVHRR).

Our Applications and Help

Toggle all items

Agriculture

For years, global food security has been at the forefront as one of the most pressing development targets. According to the FAO, food production around the world has to grow by more than 70% if we want to achieve food for all in 2050. Both public and private organizations bear a huge responsibility to raise food production in a sustainable way. Remote Sensing and especially satellite missions delivering daily global observations, like PROBA-V, are crucial to monitor the status of crops worldwide and predict yields.

Land Use / Land Cover

Land is an essential natural resource - for humanity and all terrestrial ecosystems. But while resources are strictly finite, human demands are not. Today, this leads to events such as deforestation, soil degradation and the loss of wildlife. PROBA-V, the small satellite for global observations, helps monitor and map the extent and dynamics of land cover and land use.

Climate

Global daily satellite observations are indispensable to monitor the driving forces of climate change, arguably the largest and most challenging problem our society is facing. Additionally, remote sensing serves to monitor the effects of climate change, e.g. changes in land cover and land use. Hereto PROBA-V satellite imagery with low spatial resolution but delivering daily global coverage is invaluable.

Coastal

Although PROBA-V is designed as a land mission, the good image quality provides opportunities to extend its applications to coastal waters.  By applying a dedicated atmospheric correction above water  we can derive information on the turbidity of the water and the suspended sediment concentration. Combining these turbidity and suspended sediment products from PROBA-V with products from other typical Ocean Colour sensors allows for better monitoring of turbidity in dynamic near shore areas and it increases the chance to detect short term events in particular for areas with rapid changing cloud cover.

Toggle all items

Help

Collection 2 Products User Manual

Deprecated Collection 1 Products User Manual

Contact Scientific Helpdesk

the SNAP PROBA-V Toolbox

consists of a rich set of visualisation, analysis and processing tools for the exploitation of the VEGETATION instrument. As a multi-mission remote sensing toolbox, it also supports the Sentinel missions, Envisat (MERIS & AATSR), ERS (ATSR), SMOS as well as third party data from MODIS (Aqua and Terra), Landsat (TM), ALOS (AVNIR & PRISM) and others. The various tools can be run from an intuitive desktop application or via a command-line interface. A rich application programming interface allows for development of plugins using Java or Python.