Revisiting land cover observation to address the needs of the climate modeling community

Abstract

Abstract. Improving systematic observations of land cover, as an Essential Climate Variable, should contribute to a better understanding of the global climate system and thus improve our ability to predict climatic change. The aim of this paper is to bring global land cover observations closer to meeting the needs of climate science. First, consultation mechanisms were established with the climate modeling community to identify its specific requirements in terms of satellite-based global land cover products. This assessment highlighted specific needs in terms of land cover characterization, accuracy of products, as well as stability and consistency needs that are currently not met or even addressed. The current land cover representation and mapping techniques were then called into question to specifically focus on the critical need of stable products expressed by climate users. Decoupling the stable and dynamic components of the land cover characterization and using a multi-year dataset were proposed as two key approaches to allow generating consistent suites of global land cover products over time.

Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

“…The LC_CCI land cover maps depict the permanent features of the land surface by providing information on land cover classes defined by the United Nations Land Cover Classification System (UNLCCS). It also delivers land surface seasonality products in response to the needs of the ESM and DGVM communities for dynamic information about land-surface processes (Bontemps et al, 2012). Land surface seasonality products provide for each pixel the climatology describing, on a weekly basis, seasonal dynamics of snow cover, vegetation "greenness" based on the normalized difference vegetation index and burned area.…”

“…This paper describes the LC_CCI land cover classification and presents a conversion scheme that "cross-walks" the categorical UNLCCS land cover classes to their PFT fractional equivalent. This work is one of several LC_CCI publications that have previously described the need for consistent land cover mapping (Bontemps et al, 2012), the user requirements (Tsendbazar et al, 2014) and the processing of remote sensing data (Radoux et al, 2014). Land cover to PFT conversion is a complex task and until the mapping of plant functional traits at global scale becomes possible (i.e., via "optical types"; Ustin and Gamon, 2010), the cross-walking approach remains a viable alternative for generating vegetation requirements for ESM and DGVM modeling approaches (Bonan et al, 2002;Faroux et al, 2013;Gotangco Castillo et al, 2013;Jung et al, 2006;Lawrence et al, 2011;Lawrence and Chase, 2007;Poulter et al, 2011;Verant et al, 2004;Wullschleger et al, 2014).…”

“…The final LC_CCI product resulted in the development of three global land cover data sets, one for each of three epochs (1998-2002, 2003-2007 and 2008-2012) using a spectral classification approach derived from that of GLOBCOVER (Arino et al, 2008), yet with improved algorithms (Radoux et al, 2014). More importantly, its implementation to multi-year and multi-sensor time series ensured temporal consistency across epochs (Bontemps et al, 2012). The LC_CCI land cover maps depict the permanent features of the land surface by providing information on land cover classes defined by the United Nations Land Cover Classification System (UNLCCS).…”

“…To address these challenges, the European Space Agency established the land cover component of the Climate Change Initiative (LC_CCI) and surveyed the land-surface modeling community to define user requirements for developing a new global land cover data set (Bontemps et al, 2012;Herold et al, 2011;Hollmann et al, 2013). The LC_CCI addressed these data needs by implementing an improved approach for mapping moderate-resolution global land cover consistently through time using surface reflectance from the MERIS and VEGETATION 1 and 2 sensors aboard ENVISAT and SPOT 4 and 5, respectively.…”

Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

“…Land cover influences the energy balance and the carbon and hydrological cycles and, thus, plays an important role in global change research [2][3][4]. Land cover directly affects the physical characteristics of the land surface, such as soil moisture, albedo, temperature and transpiration, so many scientific studies require information regarding the spatial distribution and dynamic changes of land cover [5][6][7]. Many projects, such as the International Geosphere Biosphere Programme (IGBP), the Global Observations of Forest Cover and Global Observations of Land Dynamics (GOFC-GOLD) and the Global Rain Forest Mapping (GRFM) project, have been proposed to understand the land cover and land cover changes [8][9][10].…”