Mapping Soil Organic Carbon Using Local Terrain Attributes: A Comparison of Different Polynomial Models

doi:10.1016/S1002-0160(17)60445-4

Pedosphere

Volume 27, Issue 4, August 2017, Pages 681-693

https://doi.org/10.1016/S1002-0160(17)60445-4 Get rights and content

Abstract

Local terrain attributes, which are derived directly from the digital elevation model, have been widely applied in digital soil mapping. This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China, by combining prediction methods with local terrain attributes derived from different polynomial models. The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice, rather than how morphometric variables and their geomorphologic interpretations are understood and calculated. In this study, 2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young, Horn, Zevenbergen-Thorne, Shary, Shi, and Florinsky algorithms) were applied. In general, 35 combinations of first- and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e., kriging with an external drift and geographically weighted regression). The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography. Among the different combinations of first- and second-order derivatives used, there was a best combination with a more accurate estimate. For different prediction methods, the relative improvement in the two zones varied between 0.30% and 9.68%. The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors. Therefore, it was concluded that the performance of predictive methods, which incorporated auxiliary variables, could be improved by attempting different terrain analysis algorithms.

References (46)

T Behrens et al.
Multi-scale digital terrain analysis and feature selection for digital soil mapping
Geoderma
(2010)
S Cavazzi et al.
Are fine resolution digital elevation models always the best choice in digital soil mapping?
Geoderma
(2013)
E G Gregorich et al.
Carbon distribution and losses: erosion and deposition effects
Soil Till Res
(1998)
K D Johnson et al.
Controls on soil organic matter content within a northern hardwood forest
Geoderma
(2009)
K H Jones
A comparison of algorithms used to compute hill slope as a property of the DEM
Comput Geosci
(1998)
M F Loutre et al.
Does mean annual insolation have the potential to change the climate?
Earth Planet Sci Lett
(2004)
M Martínez-Mena et al.
Effect of water erosion and cultivation on the soil carbon stock in a semiarid area of south-east Spain
Soil Till Res
(2008)
A B McBratney et al.
On digital soil mapping
Geoderma
(2003)
T Pei et al.
Mapping soil organic matter using the topographic wetness index: a comparative study based on different flow-direction algorithms and kriging methods
Ecol Indic
(2010)
W Schwanghart et al.
Linking spatial patterns of soil organic carbon to topography—A case study from south-eastern Spain
Geomorphology
(2011)

P A Shary et al.

Fundamental quantitative methods of land surface analysis

Geoderma

(2002)

X Shi et al.

A comparison of LiDAR-based DEMs and USGS-sourced DEMs in terrain analysis for knowledge-based digital soil mapping

Geoderma

(2012)

M P Smith et al.

The effects of DEM resolution and neighborhood size on digital soil survey

Geoderma

(2006)

C M Wang et al.

Influence of resolution on slope in areas with different topographic characteristics

Comput Geosci

(2012)

X F Wang et al.

Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China

Int J Appl Earth Observ Geoinform

(2012)

S D Warren et al.

An evaluation of methods to determine slope using digital elevation data

Catena

(2004)

J P Wilson

Digital terrain modeling

Geomorphology

(2012)

F Yimer et al.

Soil property variations in relation to topographic aspect and vegetation community in the south-eastern highlands of Ethiopia

For Ecol Manag

(2006)

Q M Zhou et al.

Analysis of errors of derived slope and aspect related to DEM data properties

Comput Geosci

(2004)

M B Bodaghabadi et al.

Using Canonical Correspondence Analysis (CCA) to identify the most important DEM attributes for digital soil mapping applications

Catena

(2011)

D B Duncan

Multiple range and multiple F tests

Biometrics

(1955)

R H Erskine et al.

Digital elevation accuracy and grid cell size: effects on estimated terrain attributes

Soil Sci Soc Am J

(2007)

I S Evans

An integrated system of terrain analysis and slope mapping

Zeitschr Geomorphol

(1980)

Cited by (17)

Application of a combinatorial approach for soil organic carbon mapping in hills
2021, Journal of Environmental Management
Citation Excerpt :
Second, the hilly area was divided into different terrain zones according to the different connections between the LS residuals and local factors (topographic and vegetation factors). This makes it feasible to accurately determine the varying relationships between SOC and local factors (Li et al., 2013; Wiaux et al., 2014; Song et al., 2017) and is also an important reason for the higher performance of LS_RBF_HASM compared to LS_HASM. Third, the RBF model has excellent capability to capture the complex relationships between SOC and LS residuals within different local terrain than MLR, and this led LS_RBF_HASM performance better than LS_MLR_HASM although they incorporated the same auxiliary environmental variables.
Accurate mapping of soil organic carbon (SOC) is critical to improve C management and develop sustainable management policies. However, it is constrained by local variations of the model parameters under complex topography, especially in hills. This study applied a methodological framework to optimize the spatial prediction of SOC in the hilly areas during 1981–2012 by quantifying the relative importance of environmental factors, which include both qualitative factors and quantitative variables. Results showed that SOC increased twofold with a moderate spatial dependence during the past 32 years. During this period, land use patterns, soil groups, topographic factors, and vegetation coverage had significant impacts on the SOC changes (p < 0.01). Specifically, the impact of land use patterns has exceeded the impact of soil groups and became the dominant factor affecting SOC changes. Meanwhile, impacts from the topographic factors and vegetation coverage have substantially declined. Based on those results, a combinatorial approach (LS_RBF_HASM) was developed to map SOC using radial basis function neural network (RBF) and high accuracy surface modelling (HASM), and to generate more detailed spatial mapping relationships between SOC and the affecting factors. Compared with ordinary kriging (OK), land use-soil group units (LS) and HASM combined (LS_HASM), multiple linear regression (MLR) and HASM combined with LS (LS_MLR_HASM); LS_RBF_HASM showed a better performance with a decline of 6.3%–37.7% prediction errors and more accurate spatial patterns due to the quantitative combination of auxiliary environmental variables and more information on the SOC variations within local factors captured by RBF and HASM. Additionally, MLR may partially undermine the relationship of the internal spatial structure due to the highly nonlinear relation between SOC and environmental variables. This methodological framework highlights the optimization of more environmental factors and the calculation of spatial variability within local factors and provides a more accurate approach for SOC mapping in hills.
Effects of neighborhood analysis window forms and derivative algorithms on the soil aggregate stability – Landscape modeling
2021, Catena
Citation Excerpt :
In this regard, A-Xing et al. (2008) demonstrated that the accuracy of DSM is strongly related to the neighborhood size applied to calculate terrain attributes. Xiaodong et al. (2017) also showed that appropriate terrain attribute algorithms could improve the digital mapping accuracy of soil organic carbon. The results of this study showed that the curvature attributes were more sensitive to the type of calculation patterns than the slope attributes.
One of the most important factors in soil-landscape analysis can be the operational scale applied for mathematical calculations of terrain attributes. This study aimed to investigate the effects of different calculation patterns on terrain attributes and soil aggregate stability-landscape modeling at the subwatershed scale. For this purpose, four terrain attributes (slope gradient, slope aspect, plan curvature, and profile curvature) were calculated by two derivative algorithms (Evans-Young and Zevenbergen-Thorne) from a one-meter digital elevation model (DEM), using two neighborhood shapes (square and circular) with ten different neighborhood scales (from 5 m to 50 m). The regression tree model was used for modeling, and Kruskal-Wallis test was performed for determining significant differences in the terrain attributes across different calculation patterns. The results showed that the curvature attributes were mostly sensitive to the type of calculation pattern rather than the slope attributes. In calculation patterns, the neighborhood scale was the most important factor, which influenced differences in terrain attributes more than the neighborhood shape or type of derivative algorithm. The terrain attributes, calculated by the Evans-Young algorithm, were more distinct between circular and square neighborhoods, compared to attributes calculated by the Zevenbergen-Thorne algorithm, because of the difference in the polynomial type. Investigation of the relationship between the aggregate stability and terrain attributes indicated the great importance of the neighborhood scale. Differences in the R-squared values of models indicated that the type of derivative algorithms and the neighborhood shape could have remarkable effects on the soil-landscape models. Overall, the present results revealed that differences in calculation patterns had considerable effects on the soil-landscape analysis, although the results might be influenced by changes in the spatial resolution of DEM.
Combining geomorphometry, feature extraction techniques and Earth-surface processes research: The way forward
2020, Geomorphology
Citation Excerpt :
The lower resolution DEM (Fig. 9j) presents vagueness in the valley. Comparative studies in various discipline, ranging from hydrology (Buchanan et al., 2014; Sørensen et al., 2006), natural hazard (Barbarella et al., 2017; Favalli and Fornaciai, 2017; Mahalingam and Olsen, 2016), geomorphometry (Purinton and Bookhagen, 2017; Sofia et al., 2014a), watershed analysis (Liffner et al., 2018), soil science (Song et al., 2017; Song et al., 2016) proves that a calculation method that performs best for all measured variables does not exist; rather the best methods is generally variable, site-specific and specific to each field-of-study. We can think of geomorphometry as a research that is bridging two very different timescales: that of much longer landform evolution, and that based upon measurement using specially designed instrumentation.
In recent years, the wealth of technological development revolutionised our ability to collect data in geosciences. Due to the unprecedented level of detail of these datasets, geomorphologists are facing new challenges, giving more in-depth answers to a broad(er) range of fundamental questions across the full spectrum of the Earth's (and Planetary) processes. This contribution builds on the existing literature of geomorphometry (the science of quantitative land-surface analysis) and feature extraction (translate land surface parameters into extents of geomorphological elements). It provides evidence of critical themes as well as emerging fields of future research in the digital realm, supporting the likely effectiveness of geomorphometry and feature extractions as they are advancing the theoretical, empirical and applied dimension of geomorphology. The review further discusses the role of geomorphometric legacies, and scientific reproducibility, and how they can be implemented, in the hope that this will facilitate action towards improving the transparency, and efficiency of scientific research, and accelerate discoveries in geomorphology. In the current landscape, substantial changes in landforms, ecosystems, land use, hydrological routing, and direct anthropogenic modifications impact systems across the full spectrum of geomorphological processes. Although uncertainties in the precise nature and likelihood of changes exist, geomorphometry and feature extraction can aid exploring process regimes and landscape responses. Taken together, they can revolutionise geomorphology by opening the doors to improved investigations crossing space and time scales, blurring the boundaries between traditional approaches and computer modelling, and facilitating cross-disciplinary research. Ultimately, the exploitation of the available wealth of digital information can help to translate our understanding of geomorphic processes, which is often based on observations of past or current conditions, into the rapidly changing future.
Mapping soil organic carbon and total nitrogen in croplands of the Corn Belt of Northeast China based on geographically weighted regression kriging model
2020, Computers and Geosciences
Understanding the spatial distributions of soil organic carbon (SOC) and total nitrogen (TN) in croplands is necessary for the sustainable management of soil resources. In this study, a geographically weighted regression kriging (GWRK) model was applied to investigate spatial variabilities of SOC and TN in croplands of the Corn Belt of Northeast China (CBNC). Based on this, a map of the ratio of SOC and TN (C:N) was obtained. Results of geostatistical analysis showed that the best-fit variogram models for SOC and TN were spherical and exponential, respectively; the spatial dependence structures of SOC and TN were strong. The variabilities of SOC and TN were explained by GWRK model having a relatively high R², and low root mean square error (RMSE) and mean absolute error (MAE) indices. These indices showed the potential applications of GWRK model on a large scale when environmental variables are quite different and the global trend is weak. The area under the curve (AUC) –receiver operating characteristic (ROC) curve exhibited that the prediction of SOC has a general performance whereas that of TN exhibited a good performance. Annual mean temperature had the largest impact on the prediction of SOC and TN in the CBNC. Mediated by topographic and climatic variables, remote sensing-derived variables did not play prominent roles as other studies for the explanation of the distributions of SOC and TN. SOC and TN contents reduced from northeast to southeast of the CBNC, and the average predicted contents were 13.22 g kg⁻¹ and 1.28 g kg⁻¹, respectively. The total level of C:N ratio was relatively low, suggesting high rates of decomposition. Our study results indicated that the influence of human activities on croplands of the CBNC should not be ignored. It also recommended that the improvement of agricultural management was an approach to re-configure the spatial distributions of SOC and TN in the CBNC of China.
Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review
2019, Geoderma
Citation Excerpt :
Parameters that can be derived from DEM have also been widely used in DSM. Song et al. (2017a) compared the predictive performance of first-order terrain derivatives such as slope and aspect with second-order derivatives including various measures of terrain curvature. In comparison to lower order terrain parameters, higher order terrain parameters derived from the DEM were found to yield moderately smaller prediction errors, which may be due to the local de-noising in comparison to lower order terrain parameters.
This article reviews the current research and applications of various digital soil mapping (DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks following a systematic mapping approach from 2013 until present (18 February 2019). It is intended that this review of relevant literature will assist prospective researchers by identifying knowledge clusters and gaps in relation to the digital mapping of SOC. Of 120 studies, most were clustered in some specific countries such as China, Australia and the USA. The highest number publications were in 2016 and 2017. Regarding the predictive models, there was a progression from Linear Models towards Machine Learning (ML) techniques, and hybrid models in Regression Kriging (RK) framework performed better than individual models. Multiple Linear Regression (MLR) was the most frequently used method for predicting SOC, although it was outperformed by other ML techniques in most studies. Random Forest (RF) was found to perform better than MLR and other ML techniques in most comparative studies. Other common and competitive techniques were Cubist, Neural Network (NN), Boosted Regression Tree (BRT), Support Vector Machine (SVM) and Geographically Weighted Regression (GWR). Due to the inconsistency in various comparative studies, it would be advisable to calibrate the competitive algorithms using specific experimental datasets. This review also reveals the environmental covariates that have been identified as the most important by RF technique in recent years in regard to digital mapping of SOC, which may assist in selecting optimum sets of environmental covariates for mapping SOC. Covariates representing organism/organic activities were among the most frequent among top five covariates, followed by the variables representing climate and topography. Climate was reported to be influential in determining the variation in SOC level at regional scales, followed by parent materials, topography and land use. However, for mapping at a resolution that represents smaller areas such as a farm- or plot-scale, land use and vegetation indices were stated to be more influential in predicting SOC. Furthermore, unlike a previous review work, all recent studies in this review incorporated validation and 41% of them estimated spatially explicit prediction of uncertainty. Only 9.16% studies performed external validation, whereas most studies used data-splitting and cross-validation techniques which may not be the best options for datasets obtained through non-probability sampling.
Multinomial logistic regression with soil diagnostic features and land surface parameters for soil mapping of Latium (Central Italy)
2019, Geoderma
Citation Excerpt :
The appropriate resolution of a DEM will depend on the scale of the processes controlling soil formation and this will be strongly landscape dependent (McKenzie and Ryan, 1999). More recently, Ließ et al. (2012) produced digital soil maps of thickness and occurrence probability of a soil diagnostic horizon using classification and regression trees; Vågen et al. (2016) developed prediction models for mapping soil functional properties relying on the spectral properties of individual MODIS pixels; Song et al. (2017) combined prediction methods with local terrain attributes to improve their prediction performance for DSM of soil organic carbon. Land components (LCs, also called landform elements, terrain units or land surface segments) are often used as land units, mainly because their boundaries frequently coincide with transitions in environmental land properties (MacMillan et al., 2004).
One of the main challenges in traditional soil mapping is the identification of land components (LCs) - suitable combinations of morphology, lithology and land use - representing a fundamental step in the definition of soil typological units. LCs are traditionally used by pedologists to correlate different soils and to identify the relationships between soil and geography. The recognition of the various soil characteristics for LCs definition is usually performed considering a number of classes for each feature (e.g. slope classes), defined a priori at a national scale. Such classes tend by nature to generalize and to flatten the actual local variability. Moreover, when dealing with large areas, taking into account a very fragmented layout is very difficult. To reduce subjectivity in interpretation, the choice of features defining soil associated LCs should be performed in an automatic or semi-automatic way. Digital Soil Mapping techniques can help to overcome these limits. In this work we propose a procedure to define the soil LCs for the territory of Latium administrative region (Central Italy), starting from a dataset of about 1500 fully described and analysed soil profiles and associated land surface parameters. The main soil diagnostic characteristics - depth, internal drainage, topsoil and subsoil texture, gravel, organic carbon content, cation exchange capacity, calcium carbonate content - were used, together with auxiliary information derived from existing thematic maps (geology, land use, pedoclimate), and from Digital Elevation Model (geomorphometric parameters). Some indices derived from remotely sensed high resolution images were also introduced to improve the estimate in areas with uniform land characteristics (i.e. flat alluvial valleys and coastal plains). Classes for the measured characteristics were defined based on their frequency distribution and of the WRB classification diagnostic thresholds. The covariates were chosen by an ANOVA, and a Principal Component Analysis was performed to avoid multicollinearity effects. Resulting principal components were used as predictors in a Multinomial logistic regression to build a raster layer for each considered soil characteristic. From all these layers, a final map of LCs was derived by means of a clustering operation. The final map represents the basis to map Soil Typological Units for the whole Latium region.

View all citing articles on Scopus

View full text

Mapping Soil Organic Carbon Using Local Terrain Attributes: A Comparison of Different Polynomial Models

Abstract

Geoderma

Geoderma

Soil Till Res

Geoderma

Comput Geosci

Earth Planet Sci Lett

Soil Till Res

Geoderma

Ecol Indic

Geomorphology

Geoderma

Geoderma

Geoderma

Comput Geosci

Int J Appl Earth Observ Geoinform

Catena

Geomorphology

For Ecol Manag

Comput Geosci

Using Canonical Correspondence Analysis (CCA) to identify the most important DEM attributes for digital soil mapping applications

Catena

Multiple range and multiple F tests

Biometrics

Digital elevation accuracy and grid cell size: effects on estimated terrain attributes

Soil Sci Soc Am J

An integrated system of terrain analysis and slope mapping

Zeitschr Geomorphol