While satellite-derived Downwelling Surface Radiation (DSR) products offer coarse spatial resolution (100 -101 km2), regional and local applications (e.g. exploitation of solar energy, efficient building) require much finer spatial resolutions (⪅ 103 m2). Although several DSR downscaling studies were conducted during the last decades considering topographic effects, only a few of them achieved the required spatial resolution for local applications and none of them evaluated the contribution of each effect. The current study proposes the downscaling of the MSG/SEVIRI DSR product (LSA-207), which recently included the fraction of diffuse radiation, from the original approximately 3 km × 3 km spatial resolution to approximately 30 m × 30 m based on topographic corrections. In addition, the effect of each correction (i.e. elevation, shadowing, and sky obstruction) in the final result is evaluated. Results are validated against ground measurements taken in 142 stations from the Servei Meteorologic de Catalunya –Catalan meteorology center– in North-East Spain, under different conditions (elevation, sky state, solar altitude) at 30 minutes temporal resolution. Shadowing caused the most noticeable changes in downscaled DSR across all elevations and sky states: about one percentage point (pp) reduction in Mean Bias Error (MBE). Correlation and Root Mean Squared Error (RMSE) remained similar pre- and post-downscaling. Greater differences were observed between clear and cloudy skies (respectively, correlations of 0.98 and 0.89-0.90, 3-4% and 6-7% relative MBE, 13-14% and 40% relative RMSE), and for solar altitude smaller than 20 degrees (30-40 pps in relative MBE).
The Satellite Application Facility on Land Surface Analysis (LSA SAF) produces and provides access to remotely sensed variables for the characterization of terrestrial ecosystems, such as land surface fluxes and biophysical parameters, taking full advantage of the EUMETSAT satellites and sensors. In this work, a procedure for the joint estimation of LSA SAF vegetation parameters is proposed. The approach includes the use of multi-task learning with gaussian processes (MTGP). The MTGP learns a shared covariance function on input features and a covariance matrix over tasks. Unlike the single output approaches, the proposed multi-task captures the inter-task dependencies among outputs. Two comparison exercises were undertaken to assess the estimation power of the MTGP as compared to single output algorithms such as standard gaussian processes regression (GPR), neural networks (NN), and random forest (RF). First, we evaluate the performance of MTGP in the context of deriving CO2 fluxes such as the gross primary production (GPP), net ecosystem exchange (NEE), and total ecosystem respiration (TER) blending SEVIRI/MSG and eddy covariance (EC) data. In addition, the MTGP prediction power was also assessed for the joint estimation of LAI, FAPAR, and FVC in a hybrid approach using radiative transfer model simulations and AVHRR/MetOp observations. The results show that MTGP outperforms the single output approaches in terms of accuracy. The MTGP multi-task optimization links outputs in such a way that the relationships among the biophysical parameters are better described obtaining a more robust model and therefore improving the accuracy of the estimates. The findings pave the way for future multi-task implementations in order to derive consistent outputs and accurate estimates of vegetation properties from remote sensing.
The LSA-SAF produces and disseminates variables for the characterization of terrestrial ecosystems and their role in the energy balance of Earth, such as land surface fluxes and vegetation parameters, taking full advantage of remotely sensed data from EUMETSAT satellites and sensors. All LSA SAF products are distributed according with EUMETSAT data policy and have been classified as essential and are distributed free of charge. This work provides an overview of the SEVIRI/MSG and AVHRR/MetOp LSA-SAF vegetation products. The LSA-SAF vegetation products provide consistent long-term data records with well-characterized uncertainty, which are required by the scientific community to model terrestrial ecosystems and energy cycles at regional and global scales. Three vegetation products (FVC, LAI, FAPAR) are provided from SEVIRI/MSG and AVHRR/MetOp observations. The vegetation products are routinely validated and provide pixel-wise uncertainty estimates and quality flag information to identify unreliable observations. The entire archive with the latest version of the several retrieval algorithms has been reprocessed in recent years in order to generate a homogeneous Climate Data Records (CDRs) of these vegetation variables. LSA-SAF has also developed recently two new products, SEVIRI/MSG GPP and EPS/AVHRR CWC. The future generation of new LSA-SAF products derived from the future MTG and EPS-SG satellites, with higher spatial and spectral resolution, will guarantee the continuity of the service.
The 2020+ Common Agricultural Policy encourages the use of Copernicus remote sensing data for the monitoring of agricultural parcels. In this work, a procedure for automatic identification of land use from remote sensing data is proposed. The approach includes the use of spectral information of Sentinel-2 time series over the Valencia province (Spain) during the agronomic year 2027/2018, and deep learning recurrent networks. In particular, a bi-directional Long Short Term Memory (Bi-LSTM) network was trained to classify active land uses and abandoned lands. A comparison exercise was undertaken to assess the classification power of the Bi-LSTM as compared to the random forest (RF) algorithm. The Bi-LSTM network outperformed the RF, and provided and overall accuracy of 97.5% when discriminating eleven land uses including abandoned lands. The results suggest the proposed methodology could potentially be implemented in an automated procedure to supervise the CAP requirements to access subsidies. In addition, the classification process also supports the continuous update of the Land Parcel Identification System (LPIS), which allows paying agencies to uniquely identify land parcels in space, store records of land uses (and assess its evolution), and ultimately ease the declaration procedure to both farmers and paying agencies.
Biogeochemical ecosystem models describe the energy and mass exchange processes between natural systems and their environment. They normally require a large amount of inputs that present important spatial variations and require a parameterization. Other simpler ecosystem models focused on a single process only need a reduced amount of inputs usually derived from direct measurements and can be combined with the former models to calibrate their parameters. This study combines the biogeochemical model Biome-BGC and a production efficiency model (PEM) optimized for the study area to calibrate a key parameter for the simulation of the ecosystem water balance by Biome-BGC, the rooting depth. Daily gross primary production (GPP) time series for the 2005-2012 period are simulated by both models. First, the optimized PEM is validated against GPP derived from four eddy covariance (EC) towers located at different ecosystems representative of the study area. Next, GPP time series simulated by both models are combined to optimize rooting depth at the four sites: different values of rooting depth are tested and the one that results in the lowest root mean square error (RMSE) between the two GPP series is selected. Explained variance and relative RMSE between Biome- BGC and EC GPP series are respectively augmented between 3 and 14 percentage points (pp) and reduced between 1 and 33pp. Finally the methodology is extrapolated for the whole study area and an original rooting depth map for peninsular Spain, which is coherent with the spatial distribution of vegetation type and GPP in the study area, is obtained at 1-km spatial resolution.
This paper is the second part of two-part set which proposes a methodology in order to validate the LSA SAF vegetation products (LAI/FVC/fAPAR) derived from SEVIRI/MSG. The main objective of this methodology refers to assessing the uncertainty of SEVIRI/MSG products by analytical comparison to in situ measurements. The scaling problem is solved in this work by considering high-resolution maps in order to make the direct comparison between ground truth and coarse-resolution products. A detail description of the measurement acquisition and estimates was presented in a first document whereas the estimation of the high-resolution biophysical maps from this in situ data set is undertaken in this paper.
This work attempts to evaluate the capabilities of a geostatistical approach in the estimation of high-resolution LAI/FVC/fAPAR maps. The geostatistical approach is based on collocated cokriging, which allows to derive high-resolution maps from in situ measurements over a small area (5x5 km2), centred at Barrax test site. This technique takes into account the spatial dependence of the data, the neighbouring information, densely sampled auxiliary information and the variance estimation as opposed to empirical functions. The method has shown to be appropriate for the spatial extension of in situ measurements. An important contribution of this work is the analysis of the uncertainties associated to the method which provides an appreciation of the varying precision of the cokriged estimates due to the irregular disposition of informative points by means of the estimated variance. On the other hand, a flag image is also provided by using the convex hull tool in order to account for possible uncertainties in previous steps to the final cokriging output.
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.
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.
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.
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.
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