Forests play a crucial role in regulating the carbon balance and maintaining global climate stability.Remote sensing has provided new perspectives for regional monitoring of vegetation phenology.However,an accurate me...Forests play a crucial role in regulating the carbon balance and maintaining global climate stability.Remote sensing has provided new perspectives for regional monitoring of vegetation phenology.However,an accurate method for extracting the photosynthetic phenology of forests remains challenging.This study proposes an innovative method,the change point estimation of forest photosynthetic phenology method based on the maximum perpendicular distance(CBPD).CBPD extracted the dates of the start of the season(SOS)and the end of the season(EOS)for forests in North America from solar-induced chlorophyll fluorescence and daily flux tower observations.The validation results of CBPD indicated that compared to those of the double-logistic,first-order derivative,and dynamic threshold methods,the root mean square error of CBPD decreased by 0.04 to 14.04 d,while Pearson’s correlation coefficient and agreement index increased by 0.03 to 0.30 and by 0.34 to 21.52,respectively.Furthermore,CBPD demonstrated substantial consistency(P<0.01)with cross-validation based on remote sensing of photosynthetic phenology.In addition,SOS exhibited greater interannual variability compared to EOS.SOS was dominated by air temperature in 93.89% of the forest area.EOS was dominated by radiation in 48.70% of the forest area.In summary,CBPD has a great potential for tracking forest photosynthetic phenology,offering crucial insights into phenological responses to climate variations.展开更多
基金supported in part by the Postdoctor Project of Hubei Province under Grant Number 2024HBBHCXA064the Natural Resources Science and Technology Innovation Projects in Fujian Province under Grant Number KY-030000-04-2024-033+4 种基金the Open Fund of the Key Laboratory of JiangHuai Arable Land Resources Protection and Ecorestoration under Grant Number ARPE-2024-KF01the National Natural Science Foundation of China under Grant Number 42090012the Sichuan Science and Technology Program under Grant Numbers 2022YFN0031,2023YFS0381,and 2023YFN0022the Inte rgovernmental International Science and Technology Inno vation Cooperation Project under Grant Number 2023YFE0110400the Key Technology and Application Demonstration for Integ rated Remote Sensing Monitoring of Safety in Key Projects under Grant Number 2023YFB3906100.
文摘Forests play a crucial role in regulating the carbon balance and maintaining global climate stability.Remote sensing has provided new perspectives for regional monitoring of vegetation phenology.However,an accurate method for extracting the photosynthetic phenology of forests remains challenging.This study proposes an innovative method,the change point estimation of forest photosynthetic phenology method based on the maximum perpendicular distance(CBPD).CBPD extracted the dates of the start of the season(SOS)and the end of the season(EOS)for forests in North America from solar-induced chlorophyll fluorescence and daily flux tower observations.The validation results of CBPD indicated that compared to those of the double-logistic,first-order derivative,and dynamic threshold methods,the root mean square error of CBPD decreased by 0.04 to 14.04 d,while Pearson’s correlation coefficient and agreement index increased by 0.03 to 0.30 and by 0.34 to 21.52,respectively.Furthermore,CBPD demonstrated substantial consistency(P<0.01)with cross-validation based on remote sensing of photosynthetic phenology.In addition,SOS exhibited greater interannual variability compared to EOS.SOS was dominated by air temperature in 93.89% of the forest area.EOS was dominated by radiation in 48.70% of the forest area.In summary,CBPD has a great potential for tracking forest photosynthetic phenology,offering crucial insights into phenological responses to climate variations.
