Harmonization of GEOV2 fAPAR time series through MODIS data for global drought monitoring

The temporal consistency of the fAPAR GEOV2 full time series (constituted by data derived from SPOT-VGT1/2 and PROBA-V) is analyzed against the single-sensor MODIS dataset, with a particular focus on the most recent fAPAR anomalies (z-scores) produced from PROBA-V in the period 2014–2017. The intercomparison highlights a systematic overestimation of GEOV2 fAPAR z-scores when compared to MODIS fAPAR, likely related to the observed positive bias (over 90% of the domain) in the PROBA-V vs. SPOT-VGT1/2 relationship. A simple two step harmonization procedure has been proposed to remove this discrepancy, based on two separate linear corrections of SPOT-VGT1/2 (2001–2013) and PROBA-V (2014–2017) data with respect to MODIS, followed by a time lag correction. The harmonized GEOV2 time series preserves the overall dynamic of fAPAR, while removing the sensor bias and improving the consistency with MODIS data. The fAPAR anomalies from the harmonized GEOV2 time series provide unbiased estimates of z-scores that are overall well correlated (R=0.55 ± 0.25) with the MODIS fAPAR anomalies.JRC.E.1-Disaster Risk Managemen

GEOV2 : Improved smoothed and gap filled time series of LAI, FAPAR and FCover 1 km Copernicus Global Land products

Essential vegetation variables including leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and fraction of green vegetation cover (FCover) are produced and distributed in the Copernicus Global Land Service. We describe here the algorithmic principles, consistency and improvements of GEOV2, Version 2 of LAI, FAPAR and FCover products derived from SPOT/VGT (1999-2013) and PROBA-V data (2014-2020) at 1 km resolution, as compared to the earlier version GEOV1. GEOV2 is based on neural networks first trained with CYCLOPES and MODIS products to estimate LAI, FAPAR and FCover from daily top of canopy reflectance. Temporal techniques are then applied to filter, smooth, fill gaps and get a composited value every 10 days. Results show that GEOV2 products keep a high consistency with GEOV1 (90% of residuals within ± max(0.5, 20%) LAI, and 80% within ± max(0.05, 10%) FAPAR / FCover) and improves in terms of product completeness (<1% of missing data), temporal consistency, consistency across variables and accuracy

GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products

This paper describes the scientific validation of the first version of global biophysical products (i.e., leaf area index, fraction of absorbed photosynthetically active radiation and fraction of vegetation cover), namely GEOV1, developed in the framework of the geoland-2/BioPar core mapping service at 1 km spatial resolution and 10-days temporal frequency. The strategy follows the recommendations of the CEOS/WGCV Land Product Validation for LAI global products validation. Several criteria of performance were evaluated, including continuity, spatial and temporal consistency, dynamic range of retrievals, statistical analysis per biome type, precision and accuracy. The spatial and temporal consistencies of GEOV1 products were assessed by intercomparison with reference global products (MODIS c5, CYCLOPES v3.1, GLOBCARBON v2 LAI, and JRC SeaWIFS FAPAR) over a global network of homogeneous sites (BELMANIP-2) during the 2003-2005 period. The accuracy of GEOV1 was evaluated against a number of available ground reference maps. Our results show that GEOV1 products present reliable spatial distribution, smooth temporal profiles which are stable from year to year, good dynamic range with reliable magnitude for bare areas and dense forests, and optimal performances with ground-based maps. GEOV1 outperforms the quality of reference global products in most of the examined criteria, and constitutes a step forward in the development of consistent and accurate global biophysical variables within the context of the land monitoring core service of GMES

Quality assessment of PROBA-V LAI, fAPAR and fCOVER collection 300 m products of copernicus global land service

Altres ajuts: this research was funded by European Commission/Joint Research Centre under the Framework Service Contract N◦199494 of the Copernicus Global Land Service.The Copernicus Global Land Service (CGLS) provides global time series of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and fraction of vegetation cover (fCOVER) data at a resolution of 300 m and a frequency of 10 days. We performed a quality assessment and validation of Version 1 Collection 300 m products that were consistent with the guidelines of the Land Product Validation (LPV) subgroup of the Committee on Earth Observation System (CEOS) Working Group on Calibration and Validation (WGCV). The spatiotemporal patterns of Collection 300 m V1 LAI, fAPAR and fCOVER products are consistent with CGLS Collection 1 km V1, Collection 1 km V2 and Moderate Resolution Imagery Spectroradiometer Collection 6 (MODIS C6) products. The Collection 300 m V1 products have good precision and smooth temporal profiles, and the interannual variations are consistent with similar satellite products. The accuracy assessment using ground measurements mainly over crops shows an overall root mean square deviation of 1.01 (44.3%) for LAI, 0.12 (22.2%) for fAPAR and 0.21 (42.6%) for fCOVER, with positive mean biases of 0.36 (15.5%), 0.05 (10.3%) and 0.16 (32.2%), respectively. The products meet the CGLS user accuracy requirements in 69.1%, 62.5% and 29.7% of the cases for LAI, fAPAR and fCOVER, respectively. The CGLS will continue the production of Collection 300 m V1 LAI, fAPAR and fCOVER beyond the end of the PROBA-V mission by using Sentinel-3 OLCI as input data

GEOV2: Improved smoothed and gap filled time series of LAI, FAPAR and FCover 1 km Copernicus Global Land products

Essential vegetation variables including leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and fraction of green vegetation cover (FCover) are produced and distributed in the Copernicus Global Land Service. We describe here the algorithmic principles, consistency and improvements of GEOV2, Version 2 of LAI, FAPAR and FCover products derived from SPOT/VGT (1999–2013) and PROBA-V data (2014–2020) at 1 km resolution, as compared to the earlier version GEOV1. GEOV2 is based on neural networks first trained with CYCLOPES and MODIS products to estimate LAI, FAPAR and FCover from daily top of canopy reflectance. Temporal techniques are then applied to filter, smooth, fill gaps and get a composited value every 10 days. Results show that GEOV2 products keep a high consistency with GEOV1 (90% of residuals within ± max(0.5, 20%) LAI, and 80% within ± max(0.05, 10%) FAPAR / FCover) and improves in terms of product completeness (<1% of missing data), temporal consistency, consistency across variables and accuracy.This research was supported by the European Commission through the Framework Service contract no. 199494-JRC and the 7th Framework Programme for Research under grant agreement no. 218795 (geoland2 project).Peer reviewe

GEOV2: Improved smoothed and gap filled time series of LAI, FAPAR and FCover 1 km Copernicus Global Land products

Essential vegetation variables including leaf area index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and fraction of green vegetation cover (FCover) are produced and distributed in the Copernicus Global Land Service. We describe here the algorithmic principles, consistency and improvements of GEOV2, Version 2 of LAI, FAPAR and FCover products derived from SPOT/VGT (1999–2013) and PROBA-V data (2014–2020) at 1 km resolution, as compared to the earlier version GEOV1. GEOV2 is based on neural networks first trained with CYCLOPES and MODIS products to estimate LAI, FAPAR and FCover from daily top of canopy reflectance. Temporal techniques are then applied to filter, smooth, fill gaps and get a composited value every 10 days. Results show that GEOV2 products keep a high consistency with GEOV1 (90% of residuals within ± max(0.5, 20%) LAI, and 80% within ± max(0.05, 10%) FAPAR / FCover) and improves in terms of product completeness (<1% of missing data), temporal consistency, consistency across variables and accuracy

AN UPDATE ON THE GLOBCARBON INITIATIVE: MULTI-SENSOR ESTIMATION OF GLOBAL BIOPHYSICAL PRODUCTS FOR GLOBAL TERRESTRIAL CARBON STUDIES

The ESA GLOBCARBON project aims to generate fully calibrated estimates of at-land products quasiindependen

Combining hectometric and decametric satellite observations to provide near real time decametric FAPAR product

A wide range of ecological, agricultural, hydrological and meteorological applications at local to regional scales requires decametric biophysical data. However, before the launch of SENTINEL-2A, only few decametric products are produced and most of them remain limited by the small number of available observations, mostly due to a moderate revisit frequency combined with cloud occurrence. Conversely, kilometric and hectometric biophysical products are now widely available with almost complete and continuous coverage, but the associated spatial resolution limits the application over heterogeneous landscapes. The objective of this study is to combine unfrequent decametric spatial resolution products with frequent hectometric spatial resolution products to improve the temporal frequency and completeness of decametric observations. The study focuses on the fraction of photosynthetically active radiation absorbed by the green vegetation (FAPAR) because of its important role in canopy models and small dependency to scaling issues. An algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step. It is assumed that the FAPAR time course is described by a second-degree polynomial function over a limited 60-days temporal window for each decametric pixel. To reduce the dimensionality of the problem, landcover classes are considered instead of each individual pixel. For each class, the coefficients of the polynomial function are adjusted using the temporal course of the available decametric FAPAR products, under the constraint of providing a good match with the time course of the hectometric dekadal FAPAR products. The point spread function associated to the hectometric FAPAR products and the possible biases between the decametric and hectometric FAPAR products are explicitly accounted for. The algorithm was evaluated over a time series of decametric Landsat-8 FAPAR images (30 m) and hectometric (330 m) dekadal GEOV3 FAPAR derived from PROBA-V images acquired in 2014 over a site in the South-West of France. Results show that the estimated DHF FAPAR products capture well the expected seasonal variation and spatial distribution while improving the temporal frequency and spatial and temporal completeness of the original Landsat-8 products. A leave one out exercise shows that the DHF values are in very good agreement with the Landsat-8 FAPAR (RMSE = 0.05–0.14) that were not used when computing the DHF. This demonstrates the robustness of the algorithm and interest under cloudy regions. Additional comparison with ground measurements collected over 14 sunflower fields along the growth season confirms the good performances of the DHF FAPAR products (RMSE = 0.11)

Operational monitoring services for our changing environment

With climate change speeding up and the on-going growth of the World’s population, the pressure on nature, biodiversity and our own living conditions increase steadily. To mitigate these threats, by effective adaptation strategies and counter measures, the frequent monitoring of our environment is crucial to provide decision makers and European citizens with accurate, up-to-date and reliable information on the changing conditions of our natural resources. Benefiting from Earth observation satellite data, the GMES Land Services provide such cross-border harmonised geo-information at global to local scales in a timely and cost-effective manner. These monitoring services have been defined, developed and implemented within a series of projects funded since 2003 by the European Commission (geoland, BOSS4GMES) and the European Space Agency (GSE Land / GSE Forest Monitoring). Building upon the results of the earlier projects, geoland2 now closes the gap between research and the operational implementation of fully mature GMES Land Services, consisting of Core Mapping Services and Core Information Services. The project aims to organise a qualified production network, to build, validate and demonstrate operational processing lines and to set-up a user driven product quality assurance process, to guarantee that the products meet the actual user requirements. The Core Mapping Services produce basic geo-information on land surfaces such as cover, use and biophysical parameters along with their annual and seasonal changes. This geo-information can thus describe, for instance, the continental vegetation state, the global radiation budget at the surface and the water cycle on the basis of satellite Earth observation data. The mapping products are of broad generic use being a very valuable information source in themselves. They also form the basis for more specialised geo-information services, i.e. the Core Information Services and further downstream applications. In geoland2 the Core Information Services offer specific information for European environmental policies and international treaties on climate change, food security and the sustainable development of Africa. Currently they address a broad variety of thematic fields, like for instance: water quality, forest managing, spatial planning, agri-environmental issues, the global carbon cycle, international food security, etc. In the framework of GMES for Africa, biophysical parameters have been provided to the AMESD (African Monitoring of the Environment for Sustainable Development) stations and specific e-tools are being tested to facilitate local data analysis supporting the sustainable management of natural resources.JRC.DDG.H.3-Global environement monitorin