Construction of membership functions for predictive soil mapping under fuzzy logic

Zhu, A‐Xing; Yang, Lin; Li, Baolin; Qin, Cheng‐Zhi; Pei, Tao; Liu, Baoyuan

doi:10.1016/j.geoderma.2009.05.024

Cited by 89 publications

(78 citation statements)

References 33 publications

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“…The average annual precipitation is about 500-600 mm and the annual temperature varies from −38°C to 36°C with very cool climate in winter and warm climate in summer . Land use, climate and parent materials in this study area have been fairly uniform (Yang et al, 2013;Zhu et al, 2010).…”

Section: Study Areasmentioning

confidence: 99%

“…One hundred and four sampling points in Heshan (with a density of 0.8 km 2 per observation) and 295 sampling points in Xuancheng (with a density of 25 km 2 per observation) were collected separately in (Yang et al, 2013;Zhang et al, 2016;Zhu et al, 2010) (Fig. 1).…”

Section: Soil Sampling and Analysismentioning

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%

“…Multiple linear regression (MLR) is one of those commonlyused methods in early time (Odeh and Mcbratney, 2000;Zornoza et al, 2007;Qiu et al, 2003;Chung and Alexander, 2002;Moore et al, 1993), and is usually used as a basic model comparing with other mapping methods (Zhu et al, 2010;Lesch et al, 1995;Qin et al, 2012;Song et al, 2016). The desirability for linear regression methods lies in its simplicity, easy to apply.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Study Areasmentioning

confidence: 99%

Section: Soil Sampling and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…The optimal or best typical locations can be identified using the fuzzy membership distribution of the environmental class (Zhu et al, 2008(Zhu et al, , 2010b. In general, pixels with a high fuzzy membership value are regarded as good examples of typical locations.…”

Section: Identification Of Typical Soil Texture Values Of Environmentmentioning

confidence: 99%

Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS

et al. 2012

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Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice

Sheng,

Li,

Zhang

et al. 2024

Food Frontiers

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Effective geographical origin discrimination of Chinese rice requires a large database of samples to ensure sufficient data for origin verification at a regional scale. In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). The spatial distribution model of 14 rice nutrient element (Al, Ba, Ca, Cu, Cr, Fe, K, Mg, Mn, Mo, Na, Ni, Rb, and Zn) on regional scale was built using an environmental similarity method for the first time. Elemental concentrations of 692 samples were used to build a simulated geographical origin prediction model for northeastern (N‐E), middle to lower Yangtze River plain (Y‐R), southwestern (S‐W), and southeastern (S‐E) in China. The results indicated that the performance of the environmental similarity model for these four growing regions was S‐W > N‐E > S‐E > Y‐R based on the lowest ranking root mean square error (RMSE) for each region. For example, the RMSEs of Zn in S‐W, N‐E, S‐E, and Y‐R regions were 2.0, 2.4, 2.7, and 3.7 mg/kg, respectively. A case study on the traceability of Japonica rice was shown that Japonica rice could be discriminated with higher origin accuracy using a simulated database (91.8%) than by the actual database (87.0%) using the BPNN model. This indicates that a simulated rice element database could improve the accuracy of geographical origin discrimination for Chinese rice and potentially be applied to other large national‐scale crop datasets.

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Construction of membership functions for predictive soil mapping under fuzzy logic

Cited by 89 publications

References 33 publications

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

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

Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS

Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice

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