NEP Estimation of Terrestrial Ecosystems in China Using an Improved CASA Model and Soil Respiration Model

Abstract

Net ecosystem productivity (NEP) is a critical indicator of the CO2 capture capacity of vegetation ecosystems. Based on the land classification and clumping index (CI) datasets, the key parameters of the CASA model, including fraction of photosynthetic active radiation (FPAR) and maximum light use efficiency (ε max ) , were optimized. Then, the NEP of China's terrestrial ecosystems was estimated, using the improved CASA coupled with a soil respiration model. Finally, the accuracy of NEP estimation was evaluated by observation data from ChinaFLUX station. The research results indicated the RMSE of the improved NEP estimations decreased from 21.139 to 10.179 (unit: gC•m −2 •month −1 ), and the R 2 value increased from 0.413 to 0.832, indicating that optimizing the parameters ε max and FPAR are both effective methods to improve the model. The spatiotemporal variation of China's NEP was analyzed using the optimized results. The NEP value of China shows a decreasing distribution pattern from southeast to northwest, and the values of different regions are in the order South > North > Qinghai-Tibet > Northwest. The monthly NEP variation in different regions of China is a unimodal curve, reaching the maximum in summer. This study optimized the NEP estimation, which can better characterize the distribution pattern of carbon sinks/sources in China's terrestrial ecosystems and lay a scientific foundation for developing regional carbon neutrality schemes.

A Review of Anthropogenic Ground-Level Carbon Emissions Based on Satellite Data

“…The study of atmospheric CO 2 by the IPCC is categorized into upper and lower layers, with boundary height being a critical factor.CO 2 in the upper layer is a result of historical emissions over thousands of years, representing the residual effects that carbon sinks have not had the opportunity to absorb [2]. Meanwhile, CO 2 in the lower layer primarily originates from current human activities, influencing future global temperatures [3,4].…”