Mapping continuous depth functions of soil carbon storage and available water capacity

Malone, Brendan; McBratney, Alex B.; Minasny, Budiman; Laslett, G. M.

doi:10.1016/j.geoderma.2009.10.007

Cited by 413 publications

(243 citation statements)

References 24 publications

Supporting

Mentioning

214

Contrasting

Unclassified

Order By: Relevance

“…For the 0-30 cm the R 2 was 0.75, for the 30-60 cm layer, 0.23 for the 60-90 cm layer, and 0.09 for the 60-90 cm layer. These results agree with findings of Malone et al (2009) andMinasny et al (2006) who also reported a decreasing accuracy of prediction with depth for soil organic carbon and available water capacity.…”

Section: <>supporting

confidence: 92%

See 1 more Smart Citation

Three-dimensional mapping of soil organic matter content using soil type–specific depth functions

2011

View full text Add to dashboard Cite

In this study depth functions of soil organic matter (SOM) were mapped across a 125 km 2 study area in the Netherlands. The mapping method is based on general pedological knowledge about the ten soil types in the area and their associated vertical distributions of SOM. For each soil type the depth function structure was obtained by stacking a subset from five model horizons. Each model horizon had two associated parameters that characterizes the depth distribution of SOM. The depth function parameters were calibrated with data from soil profile descriptions, and then spatially interpolated. Given the soil type at a prediction site, the depth function of SOM was obtained by taking the depth function structure of that soil type and the interpolated, site-specific parameters of the model horizons in that structure. For the study area a soil map was available that represents soil type at any location with a probability distribution. Combining the soil type-specific depth functions with this soil type map resulted in an estimated probability distribution of depth functions at any location in the study area. For the purpose of validation the depth functions were used to compute map the SOM stock for depth intervals 0-30 cm, 30-60 cm, 60-90 cm. The R 2 values were 0.75, 0.23, and 0.09, respectively. Similar results were found in other study areas. This illustrates that there is a general challenge of capturing subsurface variation of soil properties by our pedometric models.

show abstract

Section: <>supporting

confidence: 92%

“…Malone et al, 2009;Meersmans et al, 2009;Minasny et al, 2006;Mishra et al, 2009). These attempts typically involve the use of splines or exponential decay functions to describe the variation of soil properties down a profile.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional mapping of soil organic matter content using soil type–specific depth functions

2011

View full text Add to dashboard Cite

show abstract

“…We constructed one less complicated and higher accurate approach for assessing regional scale soil salinity compared to remote sensing (Singh, 2011;Malone et al, 2009), on the basis of the significant relationship between ECa measured with EMI and soil salinity (ECe) for an entire region. Most researches indicated that digital soil mapping with remote sensing was not easy to reach a good accuracy with R 2 > 70%, especially in interpreting soil characteristics for deep depth.…”

Section: Accuracy Of the Interpretation Modelsmentioning

confidence: 99%

GIS-mapping spatial distribution of soil salinity for Eco-restoring the Yellow River Delta in combination with Electromagnetic Induction

Liu

Zhang

et al. 2016

Ecological Engineering

View full text Add to dashboard Cite

a b s t r a c tSoil salinization is one of serious ecological problems around the world, which seriously restricts the stability of ecosystem and the economic development of agriculture. Mapping and monitoring spatial distribution of soil salinity is important for management of ecology and agriculture. This study was carried out to explore the spatial distribution of soil salinity in the Yellow River Delta using the portable device EM38-MK2 with geostatistical analysis. Apparent soil electrical conductivities were measured under four kinds of measurement modes (0.5H, 0.5 V, 1.0H and 1.0 V, respectively). The results revealed that electrical conductivity of 1:5 soil to water extract (ECe) varied from 0.965 to 1.872 dS m −1 for all sampled soil profiles, and the salinity of topsoil was the highest, which indicated that soil soluble salts accumulated to the surface. The salinity in the top layer showed strong spatial variability while salinities of other layers were moderate. Soil salinity displayed a significant zonal distribution, gradually decreasing with the increase of distance to the coastline. The regions with high ECa values were located in the north and the east of the study area, whereas regions with low ECa values were located in the south and the west parts. The correlation coefficient showed that salinities of adjacent two soil layers reached a significant level of 0.01, and gradually decreased with increasing soil depth. The linear interpretation models with ECa as independent variables and ECe as dependent variables for each depth were with R 2 between 0.828 and 0.919. The interpretation models, taking ECa and ECe of 0-15 cm depth as independent variables, and ECe of each layer in 15-100 cm depth as dependent variables, were with higher R 2 between 0.930 and 0.953. The mean error (ME) showed that there was small positive deviation in 40-100 cm whereas a high positive deviation in the topsoil (0-40 cm). The scatter plots also indicated that the models had better accuracy of salinity estimation in the top soil layers (0-80 cm). The results provides solid basis for eco-restoring in the Yellow River delta.

show abstract

“…They concluded that patterns of soil redistribution greatly improved the prediction of subsoil SOC patterns. By using equal area quadratic smoothing spline functions at the regional scale, Lacoste et al, 2014;Malone et al, 2009 mapped the SOC content for individual soil layers. Although these approaches provide a first assessment of the three-dimensional patterns of SOC storage, they focus largely on the spatial (i.e., horizontal) patterns; the use of fixed and relatively large depth intervals (25-40 cm or topsoil versus subsoil) results in low-resolution characterization of the vertical SOC profile.…”

Section: Introductionmentioning

confidence: 99%

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

Aldana-Jague

Sommer

Saby

et al. 2016

Soil and Tillage Research

View full text Add to dashboard Cite

A B S T R A C TThe identification of soil management strategies as well as the evaluation of their effectiveness requires detailed information on the spatial and temporal patterns of soil organic carbon storage. High-resolution SOC profile data are generally not available and traditional methods for collecting these are time consuming and costly. Recent studies use geo-statistical approaches to assess the three-dimensional patterns of SOC storage. However, there is still a large discrepancy between the continuous and high resolution mapping of the horizontal SOC variability on the one hand, and the coarse and discontinuous mapping of the vertical SOC profile on the other. In this study, we combine spectroscopic techniques with spatial modeling in a small, cultivated catchment in Germany and we evaluate the contribution of soil redistribution processes and topographical parameters to the observed spatial and vertical patterns. Using high-resolution data from soil cores, we evaluated the robustness of a third order polynomial function to model the vertical SOC profile. Using a crossvalidation, our results show that this approach results in a robust model (RSME = 0.24%) and performs better than the widely used exponential depth model (RMSE = 0.39%). In a next step, we evaluated the relationship between the parameters of the SOC depth model and co-variables including soil redistribution (inferred from 137 Cs data) and topographical indices using a multiple linear regression model. The performance was calculated by cross-validation and we found a low robustness of the models because of the low number of profiles (i.e. n = 19). A statistical evaluation of the co-variables highlighted two key factors influencing the SOC vertical distribution. Soil redistribution processes mainly influenced the surface SOC content (first centimeters) whereas the shape of the depth distribution was controlled by slope curvature alone. The mapping of polynomial parameters was validated using an external SOC profile dataset and showed a poor prediction of the surface content but a good prediction of the depth distribution once the surface SOC content is known (RMSE = 0.15-0.25%C). This suggests that estimating the vertical SOC profile from topsoil data by applying remote sensing data, in combination with our SOC profile model, is promising and can will result in an accurate mapping of 3D SOC patterns at a very high resolution.

show abstract

Mapping continuous depth functions of soil carbon storage and available water capacity

Cited by 413 publications

References 24 publications

Three-dimensional mapping of soil organic matter content using soil type–specific depth functions

Three-dimensional mapping of soil organic matter content using soil type–specific depth functions

GIS-mapping spatial distribution of soil salinity for Eco-restoring the Yellow River Delta in combination with Electromagnetic Induction

High resolution characterization of the soil organic carbon depth profile in a soil landscape affected by erosion

Contact Info

Product

Resources

About