摘要
土壤有机碳含量的估算是当前全球碳循环研究的热点之一,但不同学者之间的估算值差异较大。从估算方法看,主要有基于土壤剖面的直接估算法和基于生态系统碳循环过程模型的间接估算法,这两种方法各有优缺点。直接估算法由于只反映了不同土壤或植被类型的土壤有机碳含量平均值的差异,因而空间分辨率较低。而间接估算法由于参数的简化,影响了土壤有机碳估算的空间精度。作者将遥感的高时空分辨率特征、反映生态系统碳循环动态变化的过程模型、实际测量的土壤有机碳结合起来,以求提高土壤有机碳估算的空间分辨率。考虑到受温度、水分的影响,土壤呼吸与土壤有机碳含量的关系并不好,而土壤基础呼吸由于剔除了温度和水分的影响,从而使其与土壤有机碳的关系非常密切,其测定系数R2可达0.78。采用了结合遥感和碳循环过程的CASA模型及Van’tHoff土壤呼吸模型,首先估算了8km分辨率的土壤基础呼吸的空间分布,在此基础上结合实测的土壤有机碳估算了8km分辨率的土壤有机碳的空间分布。
The estimation of soil organic carbon content（SOC） is one of the important issues in the researches of global carbon cycle. However, the estimated magnitude of SOC exist great differences among different scientists. There are two commonly used methods for the estimation of SOC, each method having both advantage and disadvantage. One of the methods is so called direct method, which is based on the samples of measured SOC and maps of soil or vegetation types. The other is so called indirect method, which is based on ecosystem process model of carbon cycle. The disadvantage of direct method is that it mainly discloses the difference of SOC among different soil or vegetation types, so it can hardly distinguish the difference of SOC in the same type of soil or vegetation. The indirect method, process-based method, bases on the mechanics of carbon transfer in the ecosystem and then could potentially improve the spatial resolution of the SOC estimation if the input variables have 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. So this simplification will produce a great deal of uncertainties in the estimation of SOC, especially on the spatial precision. In this paper, the authors 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 basal respiration, the Van＇ t Hoff model was used to combine with CASA model. The results showed that this method could significantly improve the spatial precision （8km spatial resolution）. The results also showed that the relationship between soil basal respiration and .SOC is very well, as the influence of environmental factors, i.e. temperature and moisture, have been removed from soil respiration, which makes the SOC become the most important factor of soil basal respiration. The statistic model of soil basal respiration and SOC showed that the determinant coefficient（R^2） is 0. 75. As the method in this paper contains advantages from both direct and indirect method, so it could significantly improve the spatial resolution and, at the same time, keep the estimation of SOC well matched with the measured SOC.
出处
《遥感学报》
EI
CSCD
北大核心
2007年第1期127-136,共10页
NATIONAL REMOTE SENSING BULLETIN
基金
国家自然科学基金面上项目(编号:40401028
40671173)
国家杰出青年基金项目(编号:40425008)
国家自然科学基金重大项目(编号:30590384)
国家重点基础发展规划研究项目(编号:2006CB400505)
关键词
土壤有机碳
土壤基础呼吸
遥感
过程模型
中国
soil organic carbon
soil base respiration
remote sensing
process-based model
China
