Multi-source data-driven estimation of urban net primary productivity: A case study of Wuhan
Jinlong Chen, Zhenfeng Shao, Xiao Huang, Bin Hu
Abstract
Amidst urban expansion, numerous ecological challenges have surfaced, notably the swift alterations in the net primary productivity (NPP), presenting complexities in discerning their scale and spatial distribution. Leveraging meteorological data, multi-modal remote sensing datasets, field sampling data, and a digital elevation model (DEM), this research amalgamates field assessments, model simulations, and cutting-edge remote sensing techniques to craft a city-scale NPP estimation model for vegetation rooted in the Carnegie-Ames-Stanford Approach (CASA). This improved CASA model facilitated the computation of monthly NPP for Wuhan over 2017–2020. Our analysis divulged NPP spatiotemporal trends and distribution nuances in Wuhan, and assessed meteorological influences on the same. Salient conclusions encompass: (1) Wuhan's annual NPP maximum between 984.40 and 1310.51 gC/(m2 a), averaging from 316.78 to 430.32 gC/(m2 a), and culminating in totals from 170.15 to 231.60 × 1010 gC/a. (2) Monthly NPP trajectories in Wuhan exhibited a unimodal pattern: ascending from January to June, peaking in July–August, and tapering off through September-December, with a spatial distribution characterized by lower NPP values centrally and higher values peripherally. (3) Regionally, temperature markedly influenced NPP over precipitation. This research underpins urban ecological planning, offering empirical insights and strategic guidance for fostering a sustainable coexistence between humans and their environment, and charting future urban development paradigms.