%0 Dataset
%T Maps of plant functional traits with 1-km spatial resolution in terrestrial ecosystems across China
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/ea602038-d362-4681-9d63-e5634ced4d92
%W NCDC
%R 10.12072/ncdc.figshare.db6491.2024
%A Lv Nan
%K Specific leaf area (SLA);leaf dry matter content (LDMC);leaf nitrogen concentration (LNC);leaf phosphorus concentration (LPC);wood density (WD);leaf area LAI
%X The dataset selected six key plant functional traits that reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen concentration (LNC), leaf phosphorus concentration (LPC), leaf area (LA), and wood density (WD). 34589 in-situ trait measurements of 3447 seed plants were collected from 1430 sampling points in China, and two machine learning models (random forest and lift regression tree) were used to generate spatial plant functional trait maps (∼ 1 km) as well as environmental variables and vegetation indices. To obtain the best estimate, the weighted average algorithm is further applied to merge the prediction results of the two models and obtain the final spatial plant functional trait map.