Estimation of soil organic carbon based on remote sensing and process model |
| |
Authors: | Tao Zhou Peijun Shi Jinying Luo Zhenyan Shao |
| |
Institution: | (1) Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education of China, Beijing Normal University, Beijing, 100875, China;(2) Institute of Resources Science, College of Resources Science and Technology, Beijing Normal University, Beijing, 100875, China;(3) Institute of Disaster and Public Security, College of Resources Science and Technology, Beijing Normal University, Beijing, 100875, China;(4) Beijing Education Publishing Company, Beijing, 100011, China |
| |
Abstract: | The estimation of the soil organic carbon content (SOC) is one of the important issues in the research of the global carbon
cycle. However, there are great differences among different scientists regarding the estimated magnitude of SOC. There are
two commonly used methods for the estimation of SOC, with each method having both advantages and disadvantages. One method
is the so called direct method, which is based on the samples of measured SOC and maps of soil or vegetation types. The other
method is the so called indirect method, which is based on the ecosystem process model of the carbon cycle. The disadvantage
of the direct method is that it mainly discloses the difference of the SOC among different soil or vegetation types. It can
hardly distinguish the difference of the SOC in the same type of soil or vegetation. The indirect method, a process-based
method, is based on the mechanics of carbon transfer in the ecosystem and can potentially improve the spatial resolution of
the SOC estimation if the input variables have a high spatial resolution. However, due to the complexity of the process-based
model, the model usually simplifies some key model parameters that have spatial heterogeneity with constants. This simplification
will produce a great deal of uncertainties in the estimation of the SOC, especially on the spatial precision. In this paper,
we combined the process-based model (CASA model) with the measured SOC, in which the remote sensing data (AVHRR NDIV) was
incorporated into the model to enhance the spatial resolution. To model the soil base respiration, the Van’t Hoff model was
used to combine with the CASA model. The results show that this method could significantly improve the spatial precision (8
km spatial resolution). The results also show that there is a relationship between soil base respiration and the SOC as the
influence of environmental factors, i.e., temperature and moisture, had been removed from soil respiration which makes the
SOC the most important factor of soil base respiration. The statistical model of soil base respiration and the SOC shows that
the determinant coefficient (R
2) is 0.78. As the method in this paper contains advantages from both direct and indirect methods, it could significantly improve
the spatial resolution and, at the same time, keep the estimation of SOC well matched with the measured SOC.
__________
Translated from Journal of Remote Sensing, 2007, 11(1): 127–136 译自: 遥感学报] |
| |
Keywords: | soil organic carbon (SOC) soil base respiration remote sensing process-based model China |
本文献已被 SpringerLink 等数据库收录! |
|