# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model  
[PERSON]  
1 Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210307, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (G.S.) [PERSON]  
2 Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (G.S.) [PERSON]  
2 Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (G.S.) [PERSON]  
1 Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210307, China; [EMAIL_ADDRESS]  
Footnote 10: [[https://www.md](https://www.md)]([https://www.md](https://www.md))in phenology [8]. In addition, there is a great deal of uncertainty in how global change will alter the timing of phenological events in many ecosystems and thus, increased uncertainty in how forest carbon cycles will be altered [8,12]. Therefore, knowledge regarding phenological characteristics of different forests, specifically the length of the growing season, can contribute to a better understanding of the terrestrial carbon cycle with global change [13]. The scientific community has used two primary methods for studying phenology. Land surface phenology (LSP) includes near-surface and high-altitude methodologies [13], and ground observed phenology (GP) [14,15]. Early research using LSP methods relied on optical satellite data (remote sensing, RS) such as MODIS and SPOT vegetation indices (leaf area index, enhanced vegetation index, etc.) [16,17]. Observations taken at 3 or 8 d intervals are used to determine patterns of plant growth through statistical or physiological models and phenological time nodes (e.g., carbon uptake period, growing season, etc.) are extracted [18, 19, 20]. However, RS observations are limited by cloud cover, rain, or fog [21], and it is difficult to obtain adequate plant phenology data at different ecological (species, populations, communities, ecosystems, etc.) or temporal scales, due to limits in resolution [22,23]. GP methods provide the basis for plant phenology studies at community- and ecosystem-scales, which usually utilize continuous images taken by phenology cameras [14,15]. However, the area captured in these images is rather small and limits our ability to scale to larger areas and/or capture heterogeneity across the landscape. Phenological models provide a tool to enhance our understanding of changes in phenology. A number of parametric phenological models are currently available, including the seven-parameter double logistic function developed by [PERSON] et al. [19] and four-parameter piecewise logistic functions developed by [PERSON] et al. [24]. These two models are widely used to fit RS and ground observed data and extract key phenology nodes associated with growing season length [19,20]. These nodes include the start of growing season (SOS), end of growing season (EOS) and length of growing season (LOS = number of days between SOS and EOS; [19,20]), which are estimated via threshold or third derivative methods [19,20,25]. A nine-parameter phenology model developed by [PERSON] et al. [26] also uses a logistic function and has been found to have higher accuracy in predicting plant growing season in warmer regions and urban forests [26, 27, 28, 29, 30]. However, relatively fewer studies have parameterized this model to test hypotheses about plant phenological processes [29].