Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models
| dc.contributor.author | Ma, Bingxin | en |
| dc.contributor.author | Shao, Yang | en |
| dc.contributor.author | Yang, Hequn | en |
| dc.contributor.author | Lu, Yiwen | en |
| dc.contributor.author | Gao, Yanqing | en |
| dc.contributor.author | Wang, Xinyao | en |
| dc.contributor.author | Xie, Ying | en |
| dc.contributor.author | Wang, Xiaofeng | en |
| dc.date.accessioned | 2024-10-25T13:55:24Z | en |
| dc.date.available | 2024-10-25T13:55:24Z | en |
| dc.date.issued | 2024-10-10 | en |
| dc.date.updated | 2024-10-25T13:42:38Z | en |
| dc.description.abstract | This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Ma, B.; Shao, Y.; Yang, H.; Lu, Y.; Gao, Y.; Wang, X.; Xie, Y.; Wang, X. Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models. Remote Sens. 2024, 16, 3759. | en |
| dc.identifier.doi | https://doi.org/10.3390/rs16203759 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121392 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | land cover mapping | en |
| dc.subject | random forest | en |
| dc.subject | accuracy assessment | en |
| dc.subject | plastic greenhouses | en |
| dc.subject | East China | en |
| dc.title | Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models | en |
| dc.title.serial | Remote Sensing | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |