A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 4; p. 972
• Main Authors: Huang, Anqi, Shen, Runping, Li, Yeqing, Han, Huimin, Di, Wenli, Hagan, Daniel Fiifi Tawia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Accuracy; Artificial intelligence; Classification; Classification systems; data fusion; Dempster-Shafer Method; Dempster-Shafer theory; Fuzzy sets; Growing season; Land cover; land surface model; Land use; Mathematical models; Optimization; Process parameters; Remote sensing; Robustness (mathematics); Set theory; Simulation; Soil moisture; Statistical methods; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14040972

Land cover type is a key parameter for simulating surface processes in many land surface models (LSMs). Currently, the widely used global remote sensing land cover products cannot meet the requirements of LSMs for classification systems, physical definition, data accuracy, and space-time resolution. Here, a new fusion method was proposed to generate land cover data for LSMs by fusing multi-source remote sensing land cover data, which was based on improving Dempster-Shafer evidence theory with mathematical models and knowledge rules optimization. The new method has the ability to deal with seriously disagreement information, thereby improving the robustness of the theory. The results showed the new method can reduce the disagreement between input data and realized the conversion of multiple land cover classification systems to into a single land cover classification system. China Fusion Land Cover data (CFLC) in 2015 generated by the new method maintained the classification accuracy of the China land use map (CNLULC), which is based on visual image interpretation and further enriched land cover classes of input data. Compared with Geo-Wiki observations in 2015, the overall accuracy for CFLC is higher than other two global land cover data. Compared with the observations, the 0–10 cm soil moisture simulated by the CFLC in Noah–MP LSM during the growing season in 2014 had better performance than that simulated by initial land cover data and MODIS land cover data. Our new method is highly portable and generalizable to generate higher quality land cover data with a specific land cover classification system for LSMs by fusing multiple land cover data, providing a new approach to land cover mapping for LSMs.