A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

Abstract

Abstract:It is important to accurately evaluate ecosystem respiration (RE) in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST), enhanced vegetation index (EVI), and land surface water index (LSWI) to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China's grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R 2 = 0.79, RMSE = 0.77 g C m −2 day −1 ) and temperate grasslands (R 2 = 0.75, RMSE = 0.60 g C m −2 day −1 ). The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided overestimating RE in water-limited regions compared to the popular process-based model. These findings provide a better understanding of the biotic and climatic controls over spatiotemporal patterns of RE for two typical grasslands and a new alternative up-scaling method for large-scale RE evaluation in grassland ecosystems.

Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

“…Globally, temperature and precipitation are known to be important regulators of Rs, although their influence may vary between ecosystem types . Additionally, previous studies have demonstrated the relationships between EVI, vegetation productivity, soil carbon content, and rates of Rs (Chen et al, 2014;Ge et al, 2018).…”

Remotely monitoring ecosystem respiration from various grasslands along a large-scale east-west transect across Northern China

“…Grasslands and their huge variety of types are almost never included as a separate category in remote sensing models of carbon flux, and as such are certain to be over or under-estimated. Ge et al (2018) proposed similar models to evaluate grassland R e at a regional scale by integrating flux measurements and the corresponding MODIS products. But this study simplified the grasslands in northern China by alpine grasslands and temperate grasslands.…”

“…But the model cannot be transferred well to specific ecosystems, and may particularly be inaccurate in hydrologically sensitive areas owing to lack of water index affecting R e . Nevertheless, previous studies have implied the significant correlations between grassland respiration and vegetation growth status (Huang et al, 2013), as well as the environmental variables such as LST (Wohlfahrt et al, 2008;Ai et al, 2018;Ge et al, 2018) from the time-series satellite data.…”

Remotely monitoring ecosystem respiration from various grasslands along a large-scale east–west transect across northern China

“…Grasslands and their huge variety of types are almost never included as a separate category in remote sensing models of carbon flux, and as such are certain to be over or under-estimated. Ge et al [30] proposed similar models to evaluate grassland R e at a regional scale by integrating flux measurements and the corresponding MODIS products. But this study simplified the grasslands in northern China by alpine grasslands and temperate grasslands.…”

“…But the model cannot be transferred well to specific ecosystems, and may particularly be inaccurate in hydrologically sensitive areas owing to lack of water index affecting R e . Nevertheless, previous studies have implied the significant correlations between grassland respiration and vegetation growth status [25], as well as the environmental variables such as LST [15,17,30] from the time-series satellite data.…”