Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators

Sumfleth, Kay; Duttmann, Rainer

doi:10.1016/j.ecolind.2007.05.005

Cited by 169 publications

(78 citation statements)

References 48 publications

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“…Large WI values indicate an increased likelihood of saturated conditions and are usually found in lower-lying parts and convergent hollow areas and soils with small hydraulic conductivity or areas of gentle slope (Beven and Wood, 1983). These indexes have been used to identify water flow characteristics in landscapes (Sumfleth and Duttmann, 2008).…”

Section: Knowledge-based Digital Soil Mapping Approachmentioning

confidence: 99%

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Menezes

Silva

Mello

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledgebased digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soillandscape relationship explicit.

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Section: Knowledge-based Digital Soil Mapping Approachmentioning

confidence: 99%

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Menezes

Silva

Mello

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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“…of a target soil attribute on its environmental co-variables with kriging of the regression residuals. Many studies show higher accuracy than feature space-only models, due to the inclusion of spatial autocorrelation of model residuals in the models (Brus and Heuvelink, 2007;Odeh and Mcbratney, 2000;Sumfleth and Duttmann, 2008;Sun et al, 2012). However, the model residuals can't always tally with the first order or second order stationarity and seldom of these methods can be adapted to fit data locally with varying coefficients over space for the regression (Kumar et al, 2012;Walter et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Soil has long been considered as the result of the interaction of its formative environment, including climate, parent material, terrain, and vegetation conditions (Winklerprins, 1999;Mcbratney et al, 2003;Yang et al, 2008;Stoorvogel et al, 2009). Therefore, the relationships between soil and its environmental covariates can be used to map soil variations over space (Thompson et al, 2006;Sumfleth and Duttmann, 2008;Zhu et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

et al. 2016

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The present regression models in digital soil mapping usually assume that relationships between soil properties and environmental variables are always fixed (as in MLR) or varying (as in GWR) in geographical space. In reality, some of the environmental variables may be fixed in affecting soil property variation and some are local varying. In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales. Three groups of sample sets were created based on the total samples in the study areas to evaluate the robustness and stability of the model. Multiple linear regression (MLR), geographically weighted regression (GWR), GWR-kriging (GWRK), local regression-kriging (LRK), kriging with an external drift (KED), and ordinary kriging (OK) were used for comparison with MGWR. The validation results showed that the use of MGWR reduced the RMSE of GWR by 10.5% and 7.6% on average, reduced the RMSE of MLR by 12.8% and 9.9% on average for Heshan and Xuancheng study areas respectively. MGWR also showed a good competitiveness when compared with GWRK, LRK, KED and OK. In Heshan study area, the influence of flow length, relative position index, foot slope and distance to the nearest drainage were constant, whereas the elevation, topographic wetness index and valley index showed different influence in different regions. In Xuancheng study area, the fixed environmental variables were profile curvature, topographic wetness index and slope, whereas the varying environmental variables were precipitation, temperature, elevation, and limestone. The results indicate that the accuracy of predictions can be improved by adaptive coefficient according to the variation of environmental variables as implemented in MGWR compared with others considering only the local or global relationships. It was concluded that mixed geographically weighted regression model could be a potential method for digital soil mapping.

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“…Furthermore, the accuracy of the interpolated map will still be dependent on the density and distribution of the original data points (Heuvelink and Bierkens 1992;McBratney et al 2000McBratney et al , 2003. Statistical models with predictive powers could potentially overcome the problem of interpolation methods (Chaplot et al 2001;Cerri et al 2004;Chen 2000Chen , 2005Sumfleth and Duttmann 2008). But empirical models derived from traditional statistical methods may hinder the real relationships between the SOC and independent data because the relationships are rarely linear in nature (Somaratne et al 2005).…”

mentioning

confidence: 99%

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Zhao¹,

Yang²,

Benoy³

et al. 2010

Can. J. Soil. Sci.

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network models to produce soil organic carbon content distribution maps across landscapes. Can. J. Soil Sci. 90: 75Á87. Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain highresolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within 90.5% of the measured values and 70.6% within 91% of the measured values. The prediction of the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within 90.5% of the measured values and 87% within 91% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within 90.5% of the measured values and 98% of the total points within 91% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method. For personal use only.

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Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators

Cited by 169 publications

References 48 publications

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

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