Soil Organic Carbon Mapping at Field and Regional Scales Using GIS and Remote Sensing Applications
Received Date: Jun 04, 2013 / Accepted Date: Jun 05, 2013 / Published Date: Jun 10, 2013
Soil Organic Carbon (SOC) is strongly influenced by soil type, and different environmental variables. The SOC has been disturbed continuously by land use change, intensive grazing, cultivation, and for corn (Zea mays L.) cultivation for the ethanol production. This has adversely affected the SOC pool and strongly impacting global warming and climate change. Therefore, a profound understanding is very important to explore conservation practices those are helpful in improving SOC sequestration and mitigating climatic change impacts. An estimate of the SOC pool for the regional, national, and global scale is needed to provide policymakers a basis with which to manage and/or improve this soil C pool in order to address global warming issues and to offset anthropogenic emissions. A critical and credible estimation of this pool is necessary. Because of the spatial heterogeneity of soil C, extensive soil sampling is required at regional and global scales to precisely estimate the SOC pool. However, direct measurement of soil C at these larger scales is expensive and time consuming. Furthermore, sampling density and design, analytical techniques, and calculation methods produce a significant error associated with SOC pool prediction at continental and global scales . Therefore, various estimation techniques have been used extensively to interpolate the SOC at larger scales.
Geostatistical methods have been used effectively to spatially interpolate SOC using different spatial interpolation methods such as kriging . These spatial interpolation methods estimate the values of a point source data (SOC in this case) at unsampled locations with Geographical Information Systems (GIS) modeling . It is well known that SOC depends on climate, topography, hydrology, land use, soil type, and other properties . Hence, a reliable estimate of SOC can be made by considering all of these variables . Therefore, researchers have developed approaches such as RK and MLR to improve estimations over OK. Since then, these approaches have been used effectively across the globe. Regression Kriging (RK) is used to characterize relationships among different environmental variables and involves various combinations of linear regressions and kriging. This approach is based on a normal regression followed by OK with the regression residuals . The RK incorporates the auxiliary terrain information. Multi Linear Regression (MLR) is used to model the linear relationship between a dependent variable and independent variables. Both these approaches incorporate secondary variables, and are superior in terms of their prediction performance as compared to generic geostatistical models such as OK . The RK and MLR approaches are used in predicting general trends in SOC pool of an area, and the relationships between environmental variables and soil C are assumed to be constant across the space . There has been some advancement over previously studied approaches, and subsequently GWR  and GWRK  approaches have been used and considered better in some conditions compared to previously used approaches such as OK, MLR and RK. In a study conducted by Kumar and colleagues used GWR and GWRK  for predicting SOC mapping at larger scale. These researchers have reported that GWR and GWRK performed better compared to RK and MLR approached. However, there are still some disadvantages and advantages for these different approaches those are need to address.
In conclusion, SOC mapping at larger scale can indirectly assess the C sequestration and is useful in predicting the future changes in the C because of climate change, land use change and different land management scenarios . This technique helps in identifying areas that have larger potential for C sequestration . However, the estimation of soil C is not unique due to the natural soil variability within different soil types and landscapes and lack of extensive measured field data . The SOC is spatially and temporally variable across the region and this stock can be estimated at different scales using different GIS and remote sensing applications. These methodologies can be applied anywhere in the world, provided that the data are reliable and meet the criteria for using a specific approach. Researchers have been using different methodologies for estimating the SOC stock at field, regional and global scale. This information will be very useful for the producers and policy makers to explore the potential areas with higher or lower potential for SOC stock.
- Wang S, Huang M, Shao X, Mickler RA, Li K, et al. (2004) Vertical distribution of soil organic carbon in China. Environ Manag 33: S200-S209.
- Bishop TFA, McBratney AB (2001) A comparison of prediction methods for the creation of field-extent soil property maps. Geoderma 103: 149-160.
- Hengl T, Heuvelink GBM, Stein A (2004) A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120: 75-93.
- McBratney AB, Santos MLM, Minasny B (2003) On digital soil mapping. Geoderma 117: 3-52.
- Vasques GM, Grunwald S, Sickman JO, Comerford NB (2010) Upscaling of Dynamic Soil Organic Carbon Pools in a North-Central Florida Watershed. Soil Sci Soc Am J 74: 870-879.
- Odeh IOA, McBratney AB, Chittleborough DJ (1995) Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67: 215-226.
- Mishra U, Lal R, Liu D, Van Meirvenne M (2010) Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale. Soil Sci Soc Am J 74: 906-914.
- Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis 28: 281-298.
- Harris P, Fotheringham AS, Crespo R, Charlton M (2010) The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets. Mathematical Geosciences 42: 657-680.
- Kumar S, Lal R, Liu D (2012) A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma 189: 627-634.
- Ardö J, Olsson L (2003) Assessment of soil organic carbon in semi-arid Sudan using GIS and the CENTURY model. J Arid Environ 54: 633-651.
- Al-Adamat R, Rawajfih Z, Easter M, Paustian K, Coleman K, et al. (2007) Predicted soil organic carbon stocks and changes in Jordan between 2000 and 2030 made using the GEFSOC Modelling System. Agriculture Ecosystem and Environment 122: 35-45.
- Batjes NH (1996) Total carbon and nitrogen in the soils of the world. Eur J Soil Sci 47: 151-163.
Citation: Kumar S (2013) Soil Organic Carbon Mapping at Field and Regional Scales Using GIS and Remote Sensing Applications. Adv Crop Sci Tech 1:e105. Doi: 10.4172/2329-8863.1000e105
Copyright: © 2013 Kumar S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Select your language of interest to view the total content in your interested language
Share This Article
- Total views: 13330
- [From(publication date): 6-2013 - May 25, 2020]
- Breakdown by view type
- HTML page views: 9262
- PDF downloads: 4068