Updating Conventional Soil Maps through Digital Soil Mapping

Yang, Lin; You, Jing; Fahmy, Sherif H.; Zhu, A‐Xing; ShelDon, Hann; Burt, Janis M.; Feng, Qianjin

doi:10.2136/sssaj2010.0002

Cited by 81 publications

(42 citation statements)

References 34 publications

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“…The success of SoLIM relies highly on whether the selected environmental covariates relate spatial variations of soil properties (Liu and Zhu 2009;Yang et al 2011;Zhu et al 2001). This study supports the conclusion that when some easily measured soil covariates, which affect the SOC distribution such as land-use type, topographic parameters, and surface area ratio (SAR) were put into the SoLIM model, a high accuracy SOC map can be generated.…”

Section: Methods Comparisonsupporting

confidence: 70%

Mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess Plateau of China: a comparative study based on three kriging methods and a soil land inference model (SoLIM)

et al. 2014

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Detailed maps of regional spatial distribution of soil organic carbon (SOC) are needed to guide sustainable soil uses and management decisions. Interpolation methods based on spatial auto-correlations, environmental covariates, or hybrid methods are commonly used to predict SOC maps. Many of these methods perform well for gentle terrains. However, it is unknown how these methods perform to capture SOC variations in complex terrains, especially areas of which land uses are interrupted by human activities, such as the Loess Plateau of China. This study compared four interpolations or predictive methods including ordinary kriging (OK), regression kriging, ordinary kriging integrated with land-use type (OK_LU) and a soil land inference model (SoLIM). The purpose of this study is to find appropriate methods, which are suitable to the complex terrain in Loess Plateau region of China. The study area was a typical watershed in Loess Plateau with complex hilly-gully terrain and various land-use types. A field sampling dataset of 200 points was partitioned into 1/2 for model building and 1/2 for accuracy validation in a random way. Nine environmental covariates were selected: land-use types, digital elevation model, solar radiation, slope degree, slope aspect, plan curvature, profile curvature, surface area ratio, and topographic wetness index. The mean absolute percentage error, root mean square error, and goodness-of-prediction statistic value were selected to evaluate mapping results. The results showed that the use of easily obtained environmental covariates, land-use types and terrain variables improved accuracies of SOC interpolation, which will be of interests

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Section: Methods Comparisonsupporting

confidence: 70%

Mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess Plateau of China: a comparative study based on three kriging methods and a soil land inference model (SoLIM)

et al. 2014

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“…Bui and Moran (2001) use k-means clustering to classify soils with Landsat MSS bands, slope position and relief as predictor variables. Yang et al (2011) used fuzzy clustering to quantify soil-landscape relationships on a 1:20.000 soil map in Canada. The extracted knowledge was used for refined soil mapping using the Soil Land Inference Model SoLIM.…”

Section: Introductionmentioning

confidence: 99%

Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils

et al. 2012

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“…Run FCM on covariates (minus the geology type) for the location with high uncertainty in Geology Type 6 to identify different environment classes. Based on the coefficient (F) and entropy (H) (Zhu et al, 2008;Yang et al, 2011), the best cluster number is 5 and the fuzzy parameter m is 1.5. After hardening the cluster results, Class 1 has 40,135 cells and Class 2 through Class 5 have 20,875, 26,084, 17,585 and 11,426 cells, respectively.…”

Section: Uncertainty Reduction Stagementioning

confidence: 99%

An heuristic uncertainty directed field sampling design for digital soil mapping

et al. 2016

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Legacy samples are a valuable data source for digital soil mapping. However, these sample sets are often small in size and ad hoc in spatial distribution. Constrained by the limited representativeness of such a sample set, the obtained soil maps are often incomplete in spatial coverage with "gaps" at the locations which cannot be well represented by these samples. The maps may also contain areas of high prediction uncertainty. In order to extend the predicted area and reduce prediction uncertainty, additional samples are needed. This paper presents a sampling design based on prediction uncertainty to select samples which will effectively complement the sparse and ad hoc samples, and maximize the spatial coverage of prediction and minimize prediction uncertainty. A case study in China shows that this sampling scheme was effective in achieving these goals. Compared with stratified random sampling scheme, when the number of additional samples is the same, the produced map using uncertainty directed samples has larger predicted area, and the accuracy of the produced map is higher than that of the maps using stratified random samples. The finding of this study suggests that prediction uncertainty is a useful indicator to aid field sample selection and to complement the legacy data. Furthermore, the mapping accuracy produced using this method can be quantitatively related to the number of additional samples needed which opens a new horizon for digital soil mapping.

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Updating Conventional Soil Maps through Digital Soil Mapping

Cited by 81 publications

References 34 publications

Mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess Plateau of China: a comparative study based on three kriging methods and a soil land inference model (SoLIM)

Mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess Plateau of China: a comparative study based on three kriging methods and a soil land inference model (SoLIM)

Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils

An heuristic uncertainty directed field sampling design for digital soil mapping

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