A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium)

Estimates of the amount of Soil Organic Carbon (SOC) at the regional scale are important to better understand the role of the SOC reservoir in global climate and environmental issues. This study presents a method for estimating the total SOC stock using data from Flanders (Belgium). More than 6900 SOC measurements from the national soil survey (database 'Aardewerk') are combined with a digital land use map and a digital soil map of Flanders. The spatial distribution of the SOC stock is studied in its relation to factors such as soil texture, soil moisture (drainage class) and land use. The resulting map with a resolution of 15 m consists of different classes forming a combination of these environmental factors. The results show that the lowest SOC amount (kg m(-2)) is stored under cropland whereas the highest amount is found under grassland. Regarding the effect of soil properties, a significant correlation between SOC stock and depth of the ground water table is observed. Sandy loam soils stock the lowest SOC amount (kg m(-2)), whereas clay soils retain the highest SOC amount. First, the mean SOC amounts of the land use-soil type classes are calculated and assigned to the corresponding cells in order to obtain a total SOC stock with its spatial distribution for Flanders. Then, a multiple regression model is applied to predict the SOC value of a particular land use-soil type class on the map. This model is based on the observed relationships between SOC and land use-soil type characteristics, using the entire dataset. The first approach does not allow to obtain a (reliable) SOC value for all land use-soil type classes due to a lack of samples in some classes. A major advantage of the regression model approach is the attribution of class specific SOC values to each land use-soil type class, regardless of the number of observations in the classes. Consequently, by applying the model approach instead of the mean approach, the area for which a reliable SOC estimate could be obtained increased by 8.1% (from 9420 km(2) to 10 179 km(2)) and the total predicted SOC stock increased by 10.1% (from 88.7 +/- 5.6 Mt C to 97.6 +/- 1.1 Mt C). (C) 2007 Elsevier B.V. All rights reserved.