Characterizing the surface dynamics for Land Cover mapping

In order to define the information needs in support to climate science, the Global Climate Observing System established a list of Essential Climate Variables (ECV), selected to be critical for a full understanding of the climate system and currently ready for global implementation on a systematic basis. In response to the ECV list, ESA initiated a new program - namely the Climate Change Initiative (CCI) –to develop global monitoring datasets to contribute in a comprehensive and timely manner to the need for long-term satellite-based products in the climate domain. Among the 14 ESA CCI components respectively addressing the atmospheric, oceanic and terrestrial domains, the ESA CCI Land Cover (CCI-LC) project is dedicated to land cover (LC) characterization. LC is indeed referred to as one of the most obvious and commonly used indicators for land surface and the associated human induced or naturally occurring processes, while also playing a significant role in climate forcing. This project builds on the ESA-GlobCover projects experiences (Arino et al. 2008, Defourny et al. 2009). It aims at revisiting all algorithms required for the generation of a global LC product from various Earth Observation (EO) instruments that matches the needs of key users’ belonging to the climate modelling community. First, a user requirements analysis was completed with this community to identify its specific needs in terms of satellite-based global LC products. This analysis highlighted a set of requirements in terms of thematic content, spatial and temporal resolution, stability and accuracy that are not met by existing global products. One finding of particular interest was the priority for both stable and consistent LC products over time. Some interest was also expressed for more dynamic information reflecting LC change and vegetation phenology. However, the most recent series of global land cover products are specifically pointing out this inconsistency issue as a quite difficult one (Friedl et al. 2010, Bontemps et al. 2011). Yet, it must be recognized that the land cover cannot, at the same time, be defined as the physical and biological cover on the Earth’s surface (Herold et al. 2009, Di Gregorio 2005) and remains stable and consistent over time as expected by most users. This conclusion calls for the development of a new land cover ontology, which explicitly addresses the issue of inconsistency between annual land cover products and/or of products sensitivity to the observation period. The proposed land cover ontology assumes that the land cover is organized along a continuum of temporal and spatial scales and that each land cover type is defined by a characteristic scale, i.e. by typical spatial extent and time period over which its physical traits are observed (Miller 1994). This twofold assumption requires introducing the time dimension in the land cover characterization, which contributes to define the land cover in a more integrative way. Accounting for the time dimension allows distinguishing between the stable and the dynamic component of land cover. The stable component refers to the set of land elements which remain stable over time and thus define the land cover independently of any sources of temporary or natural variability. Conversely, the dynamic component is directly related to this temporary or natural variability that can induce some variation in land observation over time but without changing the land cover feature in its essence. This LC-condition is typically driven by biogeophysical processes and encompasses different observable variables such as the green vegetation phenology, snow coverage, open water presence and burnt scars. The CCI-LC project aims at delivering four global LC-condition products: the Normalized Vegetation Index (NDVI), snow, burnt area (BA) and water condition products. On a per pixel basis, these LC-conditions reflect, along the year, the average trajectory, also called climatology profile, and the intra-annual variability of a land surface feature over the 1998-2012 period. They are expressed as 7-day time profiles of the mean and standard deviation for continuous variables (NDVI) or as temporal series of occurrence probabilities for discrete variables (snow, BA and water). These products are complementary to the three CCI-LC global maps products characterizing the stable component of the land cover for the same period (see Bontemps et al. – also submitted to the same conference). The condition products are built from existing global datasets which beneficiate from high temporal frequency (capacity to depict the intra-annual variability) and long-term dataset. The NDVI condition product v1.0 is therefore built from the time series of SPOT-Vegetation surface reflectance datasets (1km) over the 1999-2011 period. The BA condition product v1.0 covers the 1998-2012 period combining for the time being the GlobCarbon.v2 and the Global Fire Emissions Database version 3 (GFED.v3). Its spatial resolution is 500m. As soon as available, this condition product will be computed from the Level 3 CCI-Fire disturbance product generated by the ESA CCI Fire project. Each LC condition product is delivered in 52 files (1 file per 7-day time interval), each file including measurements and quality flag layers. The snow and water conditions are still in process. The major challenge but also the main added-value beyond the compilation of these already existing data sets is the consistency through space/time, with the LC and between conditions. It is indeed intended to describe the whole dynamic of the terrestrial surface in a meaningful way over time. As the respective production of the different data sets was fully independent and sometimes sensor-dependent, discrepancies and incompatibility were clearly observed and highlighted. A detailed analysis of the discrepancies, the mismatching and the inconsistency distribution allows developing a new method to build the consistency. These condition products should be considered as a first version of an integrative description of the terrestrial surface. The sensor-independent processing algorithms of these products and a better consistency between sensors, such as the one expected among Sentinel instruments, allow expecting major improvements in consistency, spatial and temporal resolution. Of course, other conditions could be also considered such as the Leaf Area Index (LAI) or Land Surface Temperature (LST) and help enrich the land surface characterization and modelling. The public release of the CCI global LC condition products is planned for October 2013. These products will be delivered with the three CCI global maps products which will depict the stable component of the land cover. References • Arino O., Bicheron P., Achard F., Latham J., Witt R. and Weber J. L., 2008, Globcover: the most detailed portrait of Earth, ESA Bulletin 136, 24–31 • Bontemps S., Herold M., Kooistra L., van Groenestijn A., Hartley A., Arino O., Moreau I. and Defourny P., 2012, Revisiting land cover observation to address the needs of the climate modeling community, Biogeosciences, 9, 2145–2157 • Defourny P.et al., 2009, Accuracy assessment of a 300-m global land cover map: the GlobCover experience, in: Proceedings of the 33rd International Symposium on Remote Sensing of Environment, Stresa, Italy, 4–8 May 2009, PS-B1-3 (Ref 863) • Defourny P. and Bontemps S., 2012. Revisiting Land-Cover Mapping Concepts. In: Remote Sensing of Land Use and Land Cover : Principles and Applications, 2012, p. 49-63. 978-1-4200-7074-3. http://hdl.handle.net/2078/114554 • Di Gregorio, A. (Ed.). (2005). Land Cover Classification System: Classification Concepts and User Manual: LCCS (No. 8). FAO. • Friedl, M. A., Sulla-menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). • Remote Sensing of Environment MODIS Collection 5 global land cover : Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182. doi:10.1016/j.rse.2009.08.016 • Herold, M. (2009). Land Cover. Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables (p. 36). Rome: Global Terrestrial Observing System.