4.7 Article

Revealing the scale- and location-specific controlling factors of soil organic carbon in Tibet

Journal

GEODERMA
Volume 382, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.geoderma.2020.114713

Keywords

Soil organic carbon; Empirical mode decomposition; Scale analysis; Tibet

Categories

Funding

  1. National Natural Science Foundation of China, China [41930754, 41871300]
  2. China Postdoctoral Science Foundation, China [2019M652099]
  3. Priority Academic Program Development of Jiangsu Higher Education Institutions, China
  4. University of Wisconsin-Madison, USA

Ask authors/readers for more resources

The study utilized 2D-EMD to analyze SOC variations in Tibet, revealing that environmental factors influence SOC differently at various scales. Factors such as elevation, radiation, evapotranspiration, and temperature play a significant role in determining SOC distribution, with climate having a larger impact at larger scales and other factors such as DEM, water erosion, and NDVI being more influential at smaller scales.
Soil organic carbon (SOC) leads to a significant impact on global carbon (C) cycling and soil quality. Variations in SOC are controlled by vegetation, geomorphic, geological and climatic factors, but the dominant environmental differs. In the Qinghai -Tibet Plateau, which contains large amount of low-latitude permafrost, the impact of environmental factors for the variations of SOC may be different due to the unique and complicated geographical condition. In this study, the two-dimension empirical mode decomposition (2D-EMD) is applied to examine the variations of SOC at different scales and locations, and the correlations between SOC and environmental factors are explained. The spatial distribution of SOC in Tibet was decomposed into three intrinsic mode functions (IMFs) under different scales, with spatial variation scales of approximately 7 km, 109 km and 338 km, which represented the small, medium and large scale, respectively. The remaining residual represented the variation trend of SOC across Tibet. The correlations between SOC and environmental factors (elevation, radiation, evapotranspiration and temperature) are distinguished by the physiographic zone at small and medium scales. Temperature is weekly or nonsignificantly correlated to SOC in cold-dry western Tibet at large scale. Normalized difference vegetation index (NDVI) and precipitation influenced SOC mainly at small scales, while the effects of precipitation and evapotranspiration on the distribution of SOC were due to geomorphology and type of permafrost. The combined effect of climate on SOC was larger than other factors at large scale while factors refer to DEM, evapotranspiration, water erosion and NDVI accounted for more contribution at small scale. The results indicated that the environmental factors influence SOC under a combination of scale and location effect. These findings are of great significance for future studies in SOC dynamic modelling under the influence of natural changes and human activities.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Geosciences, Multidisciplinary

High-resolution mapping and driving factors of soil erodibility in southeastern Tibet

Wu Yu, Yefeng Jiang, Wandong Liang, Dan Wan, Bo Liang, Zhou Shi

Summary: Quantifying the spatial distribution of soil erodibility (K factor) in the Qinghai-Tibet Plateau is important for global soil erosion management. A random forest model was used to map the high-resolution spatial distribution of K factor values in southeastern Tibet, providing detailed information even in unsampled areas. The study also found that soil physical properties, climate, and topography have a significant influence on the K factor.

CATENA (2023)

Article Environmental Sciences

Vegetation disturbances characterization in the Tibetan Plateau from 1986 to 2018 using Landsat time series and field observations

Yanyu Wang, Ziqiang Ma, Yuhong He, Wu Yu, Jinfeng Chang, Dailiang Peng, Xiaoxiao Min, Hancheng Guo, Yi Xiao, Lingfang Gao, Zhou Shi

Summary: This study characterized the spatiotemporal pattern and variation of vegetation disturbances on the Tibetan Plateau (TP) over the past decades, and identified the disturbance agents. The results showed that approximately 29.34% of the TP's area (75.71 M ha) experienced at least one disturbance, with 8.44 M ha area being subject to large-scale disturbances. The spatial distribution of these disturbances varied over time, with even distribution before 2002 possibly due to overgrazing and unscientific livestock management, and concentration in the south of the Yarlung Tsangpo after 2002 mainly caused by anthropogenic activities.

ENVIRONMENTAL RESEARCH LETTERS (2023)

Article Soil Science

An improved estimate of soil carbon pool and carbon fluxes in the Qinghai-Tibetan grasslands using data assimilation with an ecosystem biogeochemical model

Ruiying Zhao, Wenxin Zhang, Zheng Duan, Songchao Chen, Zhou Shi

Summary: Calibrating ecosystem models through data assimilation can provide reliable estimates of soil carbon pool and fluxes in grasslands of the Qinghai-Tibet Plateau, improving the overestimation of the default model.

GEODERMA (2023)

Article Environmental Sciences

A novel framework for vegetation change characterization from time series landsat images

Hancheng Guo, Yanyu Wang, Jie Yu, Lina Yi, Zhou Shi, Fumin Wang

Summary: Understanding terrestrial ecosystem dynamics requires a comprehensive examination of vegetation changes. Remote sensing technology has been established as an effective approach to comprehensively assess vegetation change. In this study, a novel framework integrating short-term disturbance detection and long-term trend analysis was proposed and applied to characterize vegetation changes in Zhejiang Province from 1990 to 2020. The results showed a browning trend in the plains and a greening trend in the mountains, with an overall greening of the vegetation during the study period.

ENVIRONMENTAL RESEARCH (2023)

Article Soil Science

Prediction of Soil Organic Carbon Contents in Tibet Using a Visible Near-Infrared Spectral Library

Xiaolin Jia, Modian Xie, Bifeng Hu, Yin Zhou, Hongyi Li, Wanru Zhao, Wanming Deng, Zhou Shi

Summary: Accurate measurement of soil organic carbon (SOC) is crucial for managing agricultural production and mitigating climate change. This study validates the effectiveness of visible near-infrared spectroscopy for predicting SOC content at a local field scale in Tibet. By using direct standardization algorithms, environmental factors were successfully removed from the in situ spectra, leading to improved prediction accuracy. The results showed that the local spectral library models outperformed the national spectral library models, particularly for shrub meadows, forests, and the overall dataset.

EURASIAN SOIL SCIENCE (2023)

Article Soil Science

Strategies for predicting soil organic matter in the field using the Chinese Vis-NIR soil spectral library

Meihua Yang, Songchao Chen, Dongyun Xu, Yongsheng Hong, Shuo Li, Jie Peng, Wenjun Ji, Xi Guo, Xiaomin Zhao, Zhou Shi

Summary: The large-scale soil spectral library (SSL) provides abundant information for predicting soil properties, but using SSL for predicting soil information from in situ spectra is still a challenge. This study compared different strategies for predicting soil organic matter (SOM) using SSL and found that the mean squared Euclidean distance (msd) is an optimal indicator for selecting representative samples. The recommended strategy depends on the availability of in situ and dry spectra. These findings contribute to efficient SOM prediction in situ by integrating large-scale SSL.

GEODERMA (2023)

Article Soil Science

Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping

Songchao Chen, Nicolas P. A. Saby, Manuel P. Martin, Bernard G. Barthes, Cecile Gomez, Zhou Shi, Dominique Arrouays

Summary: Digital soil mapping is seen as an efficient approach to evaluate soil ecosystem services by providing fine-resolution and up-to-date soil information. However, limited budget for field work and soil laboratory analysis has led to the development of spectroscopy as an alternative method for rapid and cost-effective soil data collection. This study evaluates the potential of spectroscopically inferred (SI) data in digital soil mapping of soil properties at a national scale and shows that adding additional SI data can improve the accuracy of digital soil maps.

GEODERMA (2023)

Article Environmental Sciences

Pollution and risk assessment of potentially toxic elements in soils from industrial and mining sites across China

Yefeng Jiang, Huading Shi, Lina Yi, Songchao Chen, Yin Zhou, Jieliang Cheng, Mingxiang Huang, Zhou Shi

Summary: By analyzing 188 peer-reviewed articles published between 2004 and 2022, it was found that potentially toxic elements in soils from industrial and mining sites in China pose a public health risk. The concentrations of eight elements, including As, Cd, Hg, and Pb, were significantly higher than background values, and a considerable proportion of the examined sites exceeded soil risk screening values. The study also demonstrated the ecological and health risks associated with these elements, highlighting the need for control measures.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2023)

Article Environmental Sciences

Climate and soil management factors control spatio-temporal variation of soil nutrients and soil organic matter in the farmland of Jiangxi Province in South China

Bifeng Hu, Modian Xie, Hongyi Li, Rebin He, Yue Zhou, Yefeng Jiang, Wenjun Ji, Jie Peng, Fang Xia, Zongzheng Liang, Wanming Deng, Junjie Wang, Zhou Shi

Summary: The study investigates the spatio-temporal variation of soil nutrients and soil organic matter (SOM) in farmland over Jiangxi Province in Southern China. Based on a dataset of soil samples collected between 2005 and 2012, the study examines the changes in SOM, available nitrogen (N), phosphorus (P), potassium (K), pH, and cation exchange capacity. The results indicate significant temporal trends in the concentrations of SOM, available P, available N, and available K, with climate and soil management practices playing a dominant role in determining soil fertility.

JOURNAL OF SOILS AND SEDIMENTS (2023)

Review Environmental Sciences

Research advances in mechanisms of climate change impacts on soil organic carbon dynamics

Yadong Guo, Zhenzhong Zeng, Junjian Wang, Junyu Zou, Zhou Shi, Songchao Chen

Summary: This review provides a concise framework for understanding the impact of climate change on soil organic carbon (SOC) dynamics. While valuable insights have been gained, there are still knowledge gaps that need to be addressed. Future research should focus on standardizing organismal traits, SOC fractions, and the interactions and biochemical pathways of biological communities. By integrating multidisciplinary knowledge and utilizing new technologies and methodologies, the accuracy of models can be enhanced, providing a scientific foundation for mitigating climate change.

ENVIRONMENTAL RESEARCH LETTERS (2023)

Article Soil Science

The validity domain of sensor fusion in sensing soil quality indicators

Jie Xue, Xianglin Zhang, Songchao Chen, Rui Lu, Zheng Wang, Nan Wang, Yongsheng Hong, Xueyao Chen, Yi Xiao, Yuxin Ma, Zhou Shi

Summary: This study investigates the potential of visible near-infrared and mid-infrared spectroscopy, as well as three model averaging methods, in predicting soil health properties. The results show that the combination of mid-infrared spectroscopy and the S-GEM model performs the best in predicting soil organic matter and pH.

GEODERMA (2023)

Article Soil Science

Spectral fusion modeling for soil organic carbon by a parallel input-convolutional neural network

Yongsheng Hong, Songchao Chen, Bifeng Hu, Nan Wang, Jie Xue, Zhiqing Zhuo, Yuanyuan Yang, Yiyun Chen, Jie Peng, Yaolin Liu, Abdul Mounem Mouazen, Zhou Shi

Summary: Visible-to-near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy are widely used to estimate soil organic carbon (SOC). The fusion of vis-NIR and MIR data can provide accurate prediction for SOC as the individual sensor range may lack important features. Six data fusion strategies were compared, with PI-CNN achieving the best accuracy (validation R2 = 0.84) for SOC estimation. The better performance of PI-CNN over DC-CNN demonstrates the necessity of using different kernel sizes in the CNN network for fusing vis-NIR and MIR spectral data. The deep-learning fusion method based on PI-CNN is an efficient tool for integrating data from multiple sensors in soil spectral modeling.

GEODERMA (2023)

Article Environmental Sciences

Mapping soil organic matter and identifying potential controls in the farmland of Southern China: Integration of multi-source data, machine learning and geostatistics

Bifeng Hu, Hanjie Ni, Modian Xie, Hongyi Li, Yali Wen, Songchao Chen, Yin Zhou, Hongfen Teng, Hocine Bourennane, Zhou Shi

Summary: Soil organic matter (SOM) is crucial for terrestrial ecosystem functioning and is linked to global issues such as soil fertility, soil health, and climate regulation. This study collected 16,580 soil samples from farmland in Jiangxi Province and compared different models to determine the factors influencing SOM. Anthropogenic activities were found to strongly affect SOM levels, with the amount of straw return being the most important factor (31.46%). The study also showed that returning straw can improve crop production and SOM content.

LAND DEGRADATION & DEVELOPMENT (2023)

Article Geosciences, Multidisciplinary

Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation

Yongsheng Hong, Jonathan Sanderman, Tomislav Hengl, Songchao Chen, Nan Wang, Jie Xue, Zhiqing Zhuo, Jie Peng, Shuo Li, Yiyun Chen, Yaolin Liu, Abdul Mounem Mouazen, Zhou Shi

Summary: This study used a globally distributed topsoil MIR spectral library to predict SOC using different modeling methods. The results showed that fractional-order derivatives (FODs) improved the prediction accuracy of SOC. The 0.75-order derivative was found to be optimal for ratio index-based linear regression (RI-LR) models, while the convolutional neural network (CNN) model outperformed other models for full-spectrum modeling.

CATENA (2024)

No Data Available