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

Dierckx, Wouter; Sterckx, Sindy; Benhadj, Iskander; Livens, Stefan; Duhoux, Geert; Achteren, Tanja Van; François, M.; Mellab, Karim; Saint, G.

doi:10.1080/01431161.2014.883097

Cited by 109 publications

(78 citation statements)

References 19 publications

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“…The method discussed here only applies to two crops (wheat and maize), because the aerodynamic roughness of different land use types differ significantly (by as much as orders of magnitude) [53,54]. Results are based on limited datasets under particular meteorological Proba-V was designed to offer global coverage at spatial resolutions of 100 m, 300 m and 1 km [40]. The 300 m TOC reflectance products used here can facilitate the observation of a certain terrestrial target on a daily basis in most locations on Earth.…”

Section: Discussionmentioning

confidence: 99%

“…The Proba-V satellite was launched on 6 May 2013 and was designed to bridge the gap in space-borne vegetation measurements between the SPOT-VGT and the upcoming Sentinel-3 satellites. The technical specifications of the on-board sensors on the Proba-V satellite are listed in Tables 2 and 3 [40]. In this study, 300 m daily top-of-canopy (TOC) reflectance products were downloaded from the web page of VITO's Product Distribution Portal (PDP) in HDF5 file format (http://www.vito-eodata.be/).…”

Section: In Situ Datamentioning

confidence: 99%

“…The technical specifications of the on-board sensors on the Proba-V satellite are listed in Tables 2 and 3 [40]. In this study, 300 m daily top-of-canopy (TOC) reflectance products were downloaded from the web page of VITO's Product Distribution Portal (PDP) in HDF5 file format (http://www.vito-eodata.be/).…”

Section: Satellite Datamentioning

confidence: 99%

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A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data

Niu

et al. 2016

Remote Sensing

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Aerodynamic roughness length is an important parameter for surface fluxes estimates. This paper developed an innovative method for estimation of aerodynamic roughness length (z 0m ) over farmland with a new vegetation index, the Hot-darkspot Vegetation Index (HDVI). To obtain this new index, the normalized-difference hot-darkspot index (NDHD) is introduced using a semi-empirical, kernel-driven bidirectional reflectance model with multi-temporal Proba-V 300-m top-of-canopy (TOC) reflectance products. A linear relationship between HDVI and z 0m was found during the crop growth period. Wind profiles data from two field automatic weather station (AWS) were used to calibrate the model: one site is in Guantao County in Hai Basin, in which double-cropping systems and crop rotations with summer maize and winter wheat are implemented; the other is in the middle reach of the Heihe River Basin from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, with the main crop of spring maize. The iterative algorithm based on Monin-Obukhov similarity theory is employed to calculate the field z 0m from time series. Results show that the relationship between HDVI and z 0m is more pronounced than that between NDVI and z 0m for spring maize at Yingke site, with an R 2 value that improved from 0.636 to 0.772. At Guantao site, HDVI also exhibits better performance than NDVI, with R 2 increasing from 0.630 to 0.793 for summer maize and from 0.764 to 0.790 for winter wheat. HDVI can capture the impacts of crop residue on z 0m , whereas NDVI cannot.

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Section: Discussionmentioning

confidence: 99%

Section: In Situ Datamentioning

confidence: 99%

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A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data

Niu

et al. 2016

Remote Sensing

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“…We will simulate observations from the upcoming Sentinel2/MSI [47], Sentinel3/SLSTR [48] and Proba-V [49] sensors. Table 6 shows the different properties of the sensors.…”

Section: The Synthetic "Satellite" Observationsmentioning

confidence: 99%

Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences

2016

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Abstract:There is an increasing need to consistently combine observations from different sensors to monitor the state of the land surface. In order to achieve this, robust methods based on the inversion of radiative transfer (RT) models can be used to interpret the satellite observations. This typically results in an inverse problem, but a major drawback of these methods is the computational complexity. We introduce the concept of Gaussian Process (GP) emulators: surrogate functions that accurately approximate RT models using a small set of input (e.g., leaf area index, leaf chlorophyll, etc.) and output (e.g., top-of-canopy reflectances or at sensor radiances) pairs. The emulators quantify the uncertainty of their approximation, and provide a fast and easy route to estimating the Jacobian of the original model, enabling the use of e.g., efficient gradient descent methods. We demonstrate the emulation of widely used RT models (PROSAIL and SEMIDISCRETE) and the coupling of vegetation and atmospheric (6S) RT models targetting particular sensor bands. A comparison with the full original model outputs shows that the emulators are a viable option to replace the original model, with negligible bias and discrepancies which are much smaller than the typical uncertainty in the observations. We also extend the theory of GP to cope with models with multivariate outputs (e.g., over the full solar reflective domain), and apply this to the emulation of PROSAIL, coupled 6S and PROSAIL and to the emulation of individual spectral components of 6S. In all cases, emulators successfully predict the full model output as well as accurately predict the gradient of the model calculated by finite differences, and produce speed ups between 10,000 and 50,000 times that of the original model. Finally, we use emulators to invert leaf area index (LAI), leaf chlorophyll content (C ab ) and equivalent leaf water thickness (C w ) from a time series of observations from Sentinel-2/MSI, Sentinel-3/SLSTR and Proba-V observations. We use sophisticated Hamiltonian Markov Chain Monte Carlo (MCMC) methods that exploit the speed of the emulators as well as the gradient estimation, a variational data assimilation (DA) method that extends the problem with temporal regularisation, and a particle filter using a regularisation model. The variational and particle filter approach appear more successful (meaning parameters closer to the truth, and smaller uncertainties) than the MCMC approach as a result of using the temporal regularisation mode. These work therefore suggests that GP emulators are a practical way to implement sophisticated parameter retrieval schemes in an era of increasing data volumes.

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“…As one of its main objectives, PROBA-V provides continuity of the SPOT-VEGETATION time series. To achieve this, its mission definition fulfills the same near-daily global coverage and provides products at 1km and 300m resolution in the same spectral bands: Blue, Red, NIR, SWIR (Dierckx et al, 2014). PROBA-V is currently operational and has been operational since 15 October 2013, thereby ensuring an overlapping period with SPOT-VEGETATION products of 7.5 months.…”

Section: Introductionmentioning

confidence: 99%

Validation of spectral continuity between PROBA-V and SPOT-VEGETATION global daily datasets

Dierckx

Swinnen

Kempeneers

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spectral consistency with SPOT-VEGETATION is an important mission objective for PROBA-V, in particular for its 1 km products. This must allow service providers such as the Copernicus Global Land Service to extend the 16-year long timeseries of SPOT-VEGETATION global 1km data with similar PROBA-V products. To evaluate the extent of spectral consistency, an evaluation of spectral response differences is performed by applying the spectral response of PROBA-V and SPOT-VEGETATION 2 to a spectral library of representative global land cover conditions. Datasets for surface reflectance values and Normalized Difference Vegetation Index (NDVI) are thus established for both missions. Through linear regression between the two datasets, spectral correction functions are defined, which can be used to improve the spectral consistency between PROBA-V and SPOT-VEGETATION products. The correspondence between PROBA-V and SPOT-VEGETATION products is then evaluated for the overlapping period when products from both missions were available. The effect of the spectral correction functions is assessed by comparing the correspondence obtained with and without this spectral correction applied. For the NDVI product, an additional offset correction is defined based on a limited sample of the overlapping period. The final spectral correction functions are then evaluated over the full overlapping period between PROBA-V and SPOT-VEGETATION. Discrepancies are observed which indicate a temporal behavior with respect to the correspondence of the mission products. Such temporal behavior can't be explained by spectral response differences, and therefore other causes are investigated.

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

Cited by 109 publications

References 19 publications

A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data

A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data

Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences

Validation of spectral continuity between PROBA-V and SPOT-VEGETATION global daily datasets

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