Estimation of soil organic carbon based on remote sensing and process model

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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 (R2) 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.

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