Responses of net primary productivity to phenological dynamics based on a data fusion algorithm in the northern Qinghai-Tibet Plateau. (September 2022) - Li, Xiaoya; Zhao, Chengzhang; Kang, Manping; Ma, Min
Highlights: The accuracy of NPP and phenology was improved using a spatiotemporal fusion algorithm. Over the past 20 years, NPP showed an increasing trend, SGS and EGS advanced, and LGS showed a prolonged trend. The relationship between NPP and phenological indicators was significantly different among the different vegetation types. Abstract: Phenology is a key measure of how well an ecosystem functions and is pivotal in revealing vegetation productivity. Net Primary Productivity (NPP) and phenology have profound implications for the exploration of regional ecosystem processes. Several studies have investigated the effects of phenological indicators on NPP. However, these studies were carried out at a coarse resolution, making it difficult to obtain sufficient information on vegetation structure and dynamics; they also, potentially underestimate how productivity is influenced by phenology. This work uses fused NDVI images employing the ESTARFM model to determine how vegetation NPP reacts to phenology in the northern Qinghai-Tibet Plateau. The results showed that NPP was only 59.93 gC·m −2 ·year -1 . The start of the growing season (SGS) was concentrated from March to May, whereas the end of the growing season (EGS) spanned from late September to early November. The length of the growing season (LGS) spanned 4–8 months during 2000–2020, NPP showed an increasing trend, SGS and EGS advanced, and LGS showed a prolonged trend. SGS and EGS were negatively correlated with NPP and seasonal temperature, however NPP was positively correlated with seasonal precipitation. LGS was positively correlated with NPP and interannual temperature, whereas it was negatively correlated with interannual precipitation. This study will aid in providing an accurate understanding of how NPP responds to phenological dynamics. Concurrently, collective understanding of terrestrial ecosystem responses to changes on a global scale will be advanced.