Mapping of ESA's Climate Change Initiative 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. Biogeosciences, 20(12):2265–2282, June, 2023. Publisher: Copernicus GmbH![Mapping of ESA's Climate Change Initiative 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 Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.
@article{wang_mapping_2023,
title = {Mapping of {ESA}'s {Climate} {Change} {Initiative} land cover data to plant functional types for use in the {CLASSIC} land model},
volume = {20},
issn = {1726-4170},
url = {https://bg.copernicus.org/articles/20/2265/2023/},
doi = {10.5194/bg-20-2265-2023},
abstract = {Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.},
language = {English},
number = {12},
urldate = {2023-08-11},
journal = {Biogeosciences},
author = {Wang, Libo and Arora, Vivek K. and Bartlett, Paul and Chan, Ed and Curasi, Salvatore R.},
month = jun,
year = {2023},
note = {Publisher: Copernicus GmbH},
keywords = {NALCMS},
pages = {2265--2282},
}
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There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. 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