The Satellite Application Facilities on Land Surface Analysis (LSA SAF) is aimed to produce and disseminate geophysical products using data from EUMETSAT satellites such as the geostationary MSG1 and the polar orbiting METOP. One of the main scientific objectives for LSA SAF validation activities is to provide the User Community with measures of uncertainty for all derived products.
In this context, this document is the first of a two-part set which proposes a consistent methodology for the validation of the LSA SAF vegetation products (LAI/FVC/fAPAR) derived from SEVIRI /MSG . The methodology includes (1) an appropriate field data sampling strategy over different test sites, (2) derivation of high-resolution biophysical variable maps over a larger area (approximately the same size as the SPOT4-HRVIR2 multispectral image) along with an associated uncertainty, and (3) up-scaling to medium and coarse (MSG) resolution scales.
This paper aims at developing the stage (1) of the methodology at the specific test site of Barrax, an agricultural area in Central Spain (39°3'N, 2°12'W), whereas the part (2) is addressed in a second document (this issue) and the part (3) will be addressed for future tasks. This work includes a detailed description along with an exhaustive analysis of the vegetation product estimates by the hemispherical camera during the SPARC'03 field campaign, which took place in July 2003 at Barrax test site. The hemispherical photographs have proved to provide accurate estimates of biophysical parameters in crop canopies with significant advantages such as the possibility to evaluate the gap fraction in all viewing direction. On the other hand, a test analysis of the (CAN-EYE) software package used for the hemispherical photographs processing was undertaken. This paper also includes the intercomparison with another ground data set collected by the optical instrument LI-COR LAI2000 during the same campaign.
A proper determination of the BRDF is of interest for land surface studies in different topics such as albedo estimation, correction of anisotropy effects, and retrieval of vegetation parameters by defining optimal geometries. In this paper, we evaluate a set of parametric models widely-used for BRDF characterisation (Roujean model, Ambrals combinations, non-linear RPV and the empirical Walthall's model). These models are inverted and tested against atmospherically-corrected BRF measurements acquired with the CHRIS (Compact High Resolution Imaging Spectrometer) instrument on board the PROBA (Project for On-Board Autonomy) satellite over an agricultural test site located in Barrax (Spain) during the SPARC (SPectra bARrax Campaign) 2003 campaign. The study area presents different land crops with high variability in LAI values from 0 to 6.
The objectives of the present study are to determine how well the different parametric BRDF models are able to fit CHRIS/PROBA's observed multiangular reflectances in order to determine the nadir-zenith reflectance, which is the optimal geometry to retrieve the fractional vegetation coverage (FVC), and to describe the anisotropy of vegetation canopies, which can be useful to estimate accurately the leaf area index (LAI). To do so, performance indicators are obtained for the different models. The results of this study show that all the tested models are fairly accurate in the entire spectral range (RMS<0.016 at 674 nm and RMS<0.025 at 803 nm) and thus are suitable for normalisation purposes. However, most of them are not able to describe BRDF features such as the hot spot, which hampers the use of these models for exploiting the directional information. There are no significant differences, for the experimental conditions, among those evaluated although the best models appear to be the linear Ross-Li model (low RMS) and the non-linear RPV model (more realistic BRDF).
In this work we present an innovative method for retrieving vegetation variables whilst at the same time making optimal use of the new generation satellite sensors. The approach is aimed to the generation of vegetation products exploding the angular capabilities provided by the MSG/SEVIRI and EPS/AVHRR within the LSA SAF Project. The products include leaf area index (LAI) and fractional vegetation cover (FVC). The algorithm is based on the complementary use of Variable Multiple Endmember Spectral Mixture Analysis (VMESMA) and the inversion of a light-canopy interaction model, namely DISMA (DIrectional Spectral Mixture Analysis), which combines the geometric optics of large scale canopy structure with principles of radiative transfer for volume scattering within individual crowns. Unlike VMESMA, DISMA fully accounts for additional information on directional anisotropy. The prototype has been implemented in the LSA SAF system and tested using SEVIRI synthetic data. The algorithm validation includes feasibility analyses, sensitivity assessments as well as evaluation of the prototype on SEVIRI synthetic data. The study contributes to assess the uncertainties with SEVIRI based vegetation products.
EUMETSAT has developed a network of Satellite Application Facilities (SAF) for the future Application Ground Segments for the new generation Meteosat Second Generation (MSG) and European Polar System (EPS) platforms. Our main concern in LSA SAF is to develop an operational algorithm for retrieving vegetation parameters. In particular, fractional vegetation cover (FVC) and leaf area index (LAI), which are key parameters in the description of both land-surface processes and land-atmosphere interactions. The LSA SAF vegetation products will be provided over the full MSG disk at 3-km spatial resolution with a temporal resolution of 10-days. The use of BRDF models assures that these products will be corrected of the surface anisotropy effects. The algorithm is based on the complementary use of variable and multiple endmember spectral mixture analysis (DISMA), according with the available directional sampling. Land cover map, soil type databases and the clumping index are auxiliary information in the prototype. The prototyping algorithm has been tested using both airborne POLDER data over croplands, and the POLDEr on ADEOS BRDF database. A first version of the prototype for the MSG developed on synthetic MSG data is already implemented in the LSA SAF system. In this paper, the prototyping algorithm designed to retrieve the LSA SAF vegetation products and its validation on the above mentioned data sets are presented.
The BRDF (Bidirectional Reflectance Distribution Function) of vegetation canopies exhibits an anisotropic behaviour that is related to illumination and viewing geometries. However, some other aspects such as the optical properties and the structural parameters of the targets should be taken into account for an adequate explanation of the bidirectional phenomenon. The present investigation examines the anisotropic behaviour of the homogeneous canopies reflectance from laboratory data as a function of viewing geometry, structural parameters and optical properties of the samples in order to obtain relevant information to improve biophysical parameters retrieval and discrimination of vegetation canopies from optical spectral data. Airborne data acquired in Daisex-99 campaign over Barrax test site (Albacete/Spain) with the HyMap instrument are also included. The HyMap concept is able to record hot spot effect, and moreover, the different flight tracks carried out in Daisex-99 allow us to complete anisotropic behaviour shown in laboratory experience, where illumination was fixed, with airborne data acquired under different solar zenith angle. Results confirm initial hypothesis that anisotropy reflectance is related to structural parameters of the vegetation and show anisotropic behaviour usefulness to study vegetation canopies increasing data dimensionality, varying both illumination and view angles. The anisotropy factor, ANIF, has resulted a simple relationship to provide us with relevant information about vegetation canopies structure. Keywords- Vegetation Canopies, Anisotropy, Reflectance, Hot Spot, Hymap.
In the field of remote sensing applications, more than 40 vegetation indices have been developed in recent years with the aim of minimizing the influence of internal and external factors (such as soil properties and atmosphere) which can affect the radiometric response of vegetation canopies. However, although those indices have showed good performances from laboratory and simulated data, most of them are difficult to be implemented from satellite data because of their complex definition that frequently requires the knowledge of different parameters besides the reflectance itself. That is the case of the generalized soil-adjusted vegetation index (GESAVI). The GESAVI was developed on the basis of a simple vegetation canopy model. It is defined in terms of the near-infrared NIR and red R reflectances and the soil line parameters (A and B) as: GESAVI = (NIR-BR-A)/(R+Z), where Z is related to red reflectance at the cross point between the soil line and the vegetation isolines in the NIRJR plane. This new index showed a better normalization of soil background effects when compared to the traditional NDVI using different reflectance data sets (acquired under laboratory conditions as well as by means of a simulation procedure). At present, a methodology is proposed to implement the GESAVI from satellite data. We will focus our attention mainly on semiarid landscapes, where the perturbance introduced by soil optical properties is very important. It would be desirable that the application of this new vegetation index to satellite images would require only information contained in the image itself. This is the main goal of the present research. Results show that GESAVI can be easily obtained from NDVI.