A Model for Simulating the Soil Organic Carbon Pool of Steppe Ecosystems

Li, Guoqing; Li, Xiaobing; Zhou, Tao; Wang, Wanjun; Li, Ruihua; Wang, Han; Wei, Dandan

doi:10.1007/s10666-015-9488-9

Cited by 10 publications

(3 citation statements)

References 52 publications

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“…Therefore, for the estimation of soil organic carbon, Dai et al [214] directly constructed a linear regression model based on the NDVI. Li et al [215] built a TECO-R model based on the NDVI, which is an improved version of the CASA model and proved its robust performance in their experiments. In 2021, Venter et al [216] took numerous ground data, satellite indices, and products as input to an RF model and mapped the soil organic carbon stocks in South Africa.…”

Section: Carbon Cycle Monitoringmentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

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Section: Carbon Cycle Monitoringmentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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“…According to the suitability of the CASA model, we applied the improved CASA to estimate NPP [24]. The estimated NPP in the model is represented by the Absorbed Photosynthetically Active Radiation (APAR) and the actual light utilization efficiency (ε).…”

Section: Simulation Of Nppmentioning

confidence: 99%

“…These factors increase uncertainty in NPP estimates. Zhu improved the uncertainty of NPP estimates from the original CASA model [19,24]. In addition, the cold-temperate desert steppe is located in the transition zone between desert and steppe in the central part of the Inner Mongolia Autonomous Region.…”

Section: Introductionmentioning

confidence: 97%

Assessing the Net Primary Productivity Dynamics of the Desert Steppe in Northern China during the Past 20 Years and Its Response to Climate Change

Yang¹,

Li²,

Xian³

et al. 2022

Sustainability

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The net primary productivity (NPP) dynamics in arid and semi-arid ecosystems are critical for regional carbon management. Our study applied a light-utilization-efficiency model (CASA: Carnegie–Ames–Stanford Approach) to evaluate the vegetation NPP dynamics of a desert steppe in northern China over the past 20 years, and its response to climate change. Our results show that the annual average NPP of the desert steppe was 132 g C m−2 y−1, of which the grass- and shrub-dominated biome values were 142 and 91 g C m−2 y−1, respectively. The average change rate of NPP was 1.13 g C m−2 y−1, and in the grassland biome 1.31 g C m−2 y−1, a value which was significantly higher than that in shrubland, at 0.84 g C m−2 y−1. The precipitation and temperature at different time scales in the desert steppe showed a slow upward trend, and the degree of aridity tended to weaken. The correlation analysis shows that NPP changes were significantly positively and negatively correlated with precipitation and temperature, respectively. In terms of temperature, 43% of the area was significantly correlated during the growing season, which decreased to 12% on the annual scale. In 31% of the changed areas, the average NPP was 148.1 g C m−2 y−1, which was higher than the remaining significant areas. This suggests that higher NPP levels help to attenuate the negative effects of high temperature during the growing season on plant productivity in the desert steppe. This improves the understanding of the carbon cycle mechanism of arid and semi-arid ecosystems, which is beneficial to improving sustainable grassland development strategies.

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