Spatial and temporal variations of net ecosystem productivity in Xinjiang Autonomous Region, China based on remote sensing

Abstract

Net ecosystem productivity (NEP), which plays a key role in the carbon cycle, is an important indicator of the ecosystem's carbon budget. In this paper, the spatial and temporal variations of NEP over Xinjiang Autonomous Region, China from 2001 to 2020 were studied based on remote sensing and climate re-analysis data. The modified Carnegie Ames Stanford Approach (CASA) model was employed to estimate net primary productivity (NPP), and the soil heterotrophic respiration model was used to calculate soil heterotrophic respiration. Then NEP was obtained by calculating the difference between NPP and heterotrophic respiration. The annual mean NEP of the study area was high in the east and low in the west, high in the north and low in the south. The 20-year mean vegetation NEP of the study area is 128.54 gC·m-2, indicating that the study area is a carbon sink on the whole. From 2001 to 2020, the annual mean vegetation NEP ranged between 93.12 and 158.05 gC·m-2, and exhibited an increasing trend in general. 71.46% of the vegetation area showed increasing trends of NEP. NEP exhibited a positive relationship with precipitation and a negative relationship with air temperature, and the correlation with air temperature was more significant. The work reveals the spatio-temporal dynamics of NEP in Xinjiang Autonomous Region and can provide a valuable reference for assessing regional carbon sequestration capacity.

Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020

“…Numerous research efforts have been undertaken to explore the spatial and temporal changes in NEP using remote sensing techniques. However, most of the studies were based on MODIS remote sensing data [11,12,44,54]. MODIS data have been available since 2000, limiting the examination of carbon source/sink dynamics over extended temporal periods.…”

“…This study derived NEP from NOAA/AVHRR remote sensing data, which have been available since 1981 and provided a much longer temporal coverage than MODIS data. In addition, previous studies on NEP primarily concentrate on smaller regional scales or specific provincial and regional units [11,12,44]. This study took China as the study area, covering a large area with various climates and vegetation zones.…”

“…NEP measures the net exchange of carbon between ecosystems and the atmosphere. In general, if NEP is positive, the ecosystem is a carbon sink; if it is negative, the ecosystem is a carbon source [8][9][10][11][12][13]. Within the framework of anthropogenic disturbance and climate change, it is crucial to have an accurate understanding of the spatiotemporal variations in NEP and the factors that drive them.…”

“…Satellite remote sensing of Earth's surface offers several advantages, including access to extensive coverage and temporal continuity, which overcomes the limitations of traditional fixed-point estimation methods for carbon sinks [8,16] and facilitates the estimation of NEP spatiotemporal variations at large scales. Numerous studies have been carried out utilizing remote sensing data to map NEP in various regional ecosystems [8][9][10][11][12][13]17,18]. Light utilization efficiency (LUE) models, with the Carnegie-Ames-Stanford Approach (CASA) being prominent among them, have been extensively utilized for estimating NPP.…”

“…Wang et al [11] employed a soil respiration model and the MODIS NPP product to estimate NEP in the Qilian Mountains of northwestern China over the past 20 years and then quantified the individual as well as combined influences of natural environmental factors, climatic factors, and human activity intensity on NEP changes. Lu et al [12] applied MODIS data to map the NEP of Xinjiang Autonomous Region from 2001 to 2020 and then examined the relationship between NEP and climate factors. Feng et al [13] employed the CASA model and a soil respiration model to estimate the NPP and NEP in the Yulin region, a typical area of desertification reversal in the Mu Us Sandy Land, from 2000 to 2020.…”