Mapping of ESA-CCI land cover data to plant functional types for use in the CLASSIC land model. Wang, L., Arora, V. K, Bartlett, P., Chan, E., & Curasi, S. R EGUsphere, 923:43, 2022. ![Mapping of ESA-CCI land cover data to plant functional types for use in the CLASSIC land model [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 Plant functional types (PFTs) are used to represent vegetation distribution in land surface models 2 (LSMs). Large differences are found in the geographical distribution of PFTs currently used in 3 various LSMs. These differences arise from the differences in the underlying land cover products 4 but also the methods used to map or reclassify land cover data to the PFTs that a given LSM 5 represents. There are large uncertainties associated with existing PFT mapping methods since 6 they are largely based on expert judgment and therefore are subjective. In this study, we propose 7 a new approach to inform the mapping or the cross-walking process using analyses from sub-8 pixel fractional error matrices, which allows for a quantitative assessment of the fractional 9 composition of the land cover categories in a dataset. We use the Climate Change Initiative 10 (CCI) land cover product produced by the European Space Agency (ESA). A previous study has 11 shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an 12 improved land cover distribution compared to that from the GLC2000 dataset currently used in 13 the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree 14 cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the 15 sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to 16 create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs 17 represented in the CLASSIC model. There are large differences between the new PFTs and those 18 currently used in the model. Offline simulations performed with the CLASSIC model using the 19 ESA-CCI based PFTs show improved winter albedo compared to that based on the GLC2000 20 dataset. This emphasizes the importance of accurate representation of vegetation distribution for 21
@article{wang_mapping_2022,
title = {Mapping of {ESA}-{CCI} land cover data to plant functional types for use in the {CLASSIC} land model},
volume = {923},
url = {https://doi.org/10.5194/egusphere-2022-923},
doi = {https://doi.org/10.5194/egusphere-2022-923},
abstract = {1 Plant functional types (PFTs) are used to represent vegetation distribution in land surface models 2 (LSMs). Large differences are found in the geographical distribution of PFTs currently used in 3 various LSMs. These differences arise from the differences in the underlying land cover products 4 but also the methods used to map or reclassify land cover data to the PFTs that a given LSM 5 represents. There are large uncertainties associated with existing PFT mapping methods since 6 they are largely based on expert judgment and therefore are subjective. In this study, we propose 7 a new approach to inform the mapping or the cross-walking process using analyses from sub-8 pixel fractional error matrices, which allows for a quantitative assessment of the fractional 9 composition of the land cover categories in a dataset. We use the Climate Change Initiative 10 (CCI) land cover product produced by the European Space Agency (ESA). A previous study has 11 shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an 12 improved land cover distribution compared to that from the GLC2000 dataset currently used in 13 the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree 14 cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the 15 sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to 16 create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs 17 represented in the CLASSIC model. There are large differences between the new PFTs and those 18 currently used in the model. Offline simulations performed with the CLASSIC model using the 19 ESA-CCI based PFTs show improved winter albedo compared to that based on the GLC2000 20 dataset. This emphasizes the importance of accurate representation of vegetation distribution for 21},
journal = {EGUsphere},
author = {Wang, Libo and Arora, Vivek K and Bartlett, Paul and Chan, Ed and Curasi, Salvatore R},
year = {2022},
keywords = {NALCMS},
pages = {43},
}
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