Modeling of Spring Phenology of Boreal Forest by Coupling Machine Learning and Diurnal Temperature Indicators  

作　　者：DENG Guorong ZHANG Hongyan HONG Ying GUO Xiaoyi YI Zhihua EHSAN BINIYAZ 

机构地区：[1]School of Hydraulic Engineering,Nanchang Institute of Technology,Nanchang 330099,China [2]Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains,Ministry of Education,School of Geographical Sciences,Northeast Normal University,Changchun 130024,China [3]Urban Remote Sensing Application Innovation Center,School of Geographical Sciences,Northeast Normal University,Changchun 130024,China [4]School of Geographical Sciences,Inner Mongolia Normal University,Hohhot 010022,China [5]Landscape Ecology and Geoinformation Science,Department of Geography,Kiel University,Kiel 24118,Germany

出　　处：《Chinese Geographical Science》2025年第1期38-54,共17页中国地理科学(英文版)

基　　金：Under the auspices of National Natural Science Foundation of China(No.42201374,42071359)。

摘　　要：The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.

关 键 词：spring phenology diurnal temperature machine learning future climate scenarios boreal forest 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] Q948[自动化与计算机技术—控制科学与工程]