Estimation Of Soil Organic Carbon Content Research Articles

Evaluation of two methods to eliminate the effect of water from soil vis–NIR spectra for predictions of organic carbon

Visible near infrared reflectance spectroscopy (vis–NIR) is an increasingly popular measurement method that can provide cheaper and faster predictions of soil properties, including soil organic carbon content (SOC). The spectroscopic prediction method relies significantly on the development of regressions of data in spectral databases or libraries. While the vis–NIR estimation of SOC was developed in controlled laboratory conditions, its natural development in recent years has been to perform the vis–NIR measurements in situ, where soil spectra are recorded under field conditions. However, environmental factors, such as soil moisture content, have been shown to affect soil spectra, making the use of regressions derived using soil spectral libraries difficult. Direct standardization (DS) and external parameter orthogonalisation (EPO) are two methods that were proposed for the correction of variable moisture conditions and other environmental factors. In this study, we compared DS and EPO on a set of 150 soil samples (3 depths from each of 50 soil cores) from a farm in New Zealand. The samples were re-wetted under controlled conditions, and spectra were recorded at nine different moisture levels. Our results show that DS and EPO are two effective strategies to mitigate the effects of soil water content on vis–NIR spectra. While DS and EPO results were similar when a large number of soil cores were reserved for calibrating the moisture correction methods, SOC predictions using the EPO correction significantly outperformed those using the DS correction for a lower number of cores (5 cores, 15 samples).

Organic carbon stock in some forest soils in Serbia

The content of organic carbon (C) was researched in topsoil layers (0-20 cm) in the most represented soils of forest ecosystems in central Serbia: eutric ranker, eutric cambisol and dystric cambisol. The soils were sampled during 2003, 2004 and 2010. Laboratory analyses included the soil physical and chemical properties necessary for the quantification of the soil organic carbon in organic and mineral layers. Mean values of the soil organic carbon (SOC) stores in organic horizons of the study soils varied between: 1.01?0.4 kg(C).m-2 (dystric cambisol), 0.90?0.41 kg(C).m-2 (eutric ranker) and 0.94?0.36 kg(C).m-2 (eutric cambisol). Average values of organic carbon in mineral layers (0-20 cm) ranged between: 3.83?1.70 kg(C).m-2 (dystric cambisol), 6.26?3.41 kg(C).m-2 (eutric ranker) and 4.36?1.91 kg(C).m-2 (eutric cambisol). The average value of total organic carbon stock in the study soils (both organic and mineral layers) was 5.77 kg(C).m-2. This paper addresses the methodological aspects of regional estimation of soil organic carbon content as the potential to be applied in the National Forest Inventory Program.

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

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.