Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Revista Brasileira de Ciência do Solo

Waldir De Carvalho Junior²  Cesar Da Silva Chagas²  Philippe Lagacherie¹  Braz Calderano Filho¹  Silvio Barge Bhering² 

[1] ,Embrapa SolosRio de Janeiro RJ ,Brazil

关键词: multiple linear regression;    kriging;    Co-Kriging;    regressão linear múltipla;    krigagem;    cokrigagem;   

DOI  :  10.1590/S0100-06832014000300003

来源: SciELO

【 摘 要 】

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

【 授权许可】

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