Modelling and Mapping Soil Carbon

 To examine the mechanisms that affect soil carbon (SC) storage and subsequently model and map SC in the southern interior grasslands of British Columbia (BC).


Soil carbon (SC) sequestration, the process of storing CO2 in the soil through crop residues and other organic solids, has been an area under much investigation as it relates to reducing atmospheric carbon (C) and mitigating climate change. Since grasslands predominately sequester C below ground through root growth and consequent soil-building processes, they have a high potential for long term C storage and therefore are of major importance for maintaining Earth’s carbon cycle. Despite advances in SC determination in recent years, it remains a challenge to model and monitor SC across large regions. There are several factors, both anthropogenic and environmental, that influence C sequestration. Given this complex system, I have used remote sensing (RS) data in conjunction with accurate field measurements to examine the mechanisms that affect SC storage in order to produce predictive SC maps for the southern interior grasslands of British Columbia (BC).

Soil carbon prediction was based on the Normalized Difference Vegetation Index (NDVI), which has demonstrated high correspondence with SC distribution in past studies.  The relationship of SC and NDVI was evaluated on two scales using: i) the MOD 13Q1 (250 m/16 day resolution) NDVI data product from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the United States Terra satellite (NDVIMODIS), and ii) a handheld Multispectral Radiometer (MSR16R, Cropscan Inc., 1 m resolution) device (NDVIMSR). Other factors included in the model are: i) grazing, ii) climate data, iii) vegetation community zones, iv) soil classification and soil drainage, and v) topography.  A traditional linear stepwise regression (SR) modelling approach was compared with random forest (RF) modelling, which is a recursive partitioning technique that employs randomized bagging and bootstrapping of samples. Based on comparisons of mean squared error, RF models created better predictions than SLR.  Due to the strong relationship between NDVIMSR with SOC in fenced  (F2=0.47) and grazed (R2=0.34) conditions, when NDVIMSR data was used as model input, the percentage of explained variance was greater than for models which used NDVIMODIS, showing the potential of increased model accuracy with higher resolution RS data.  Significantly higher SC was recorded in 2014 as compared to 2013 (p<0.001). Based on the high R2 values produced from the SR and RF models (R2 = 0.63 for SC in 2014 for fenced systems, based on NDVI data derived from MODIS ; R2=0.73 for SC in 2014 for fenced systems, based on NDVI data derived from MSR), this research has demonstrated the effectiveness of NDVI based models to predict SC and SOC.

This project creates the groundwork for effective monitoring techniques of SC levels using RS techniques in order to develop a carbon offset program for the ranching industry and can be used to help direct land management efforts to increase SC sequestration in BC. The modelling results are being used n the economic model currently being produced by Sarah.

what is ndvi


  • Study Sites: Where did I collect samples?
  • Input Layers: What data were the models based on?


  • Predictive maps: How is soil carbon spatially distributed?

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