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Spatial soil quality information is needed for the agricultural development in Hong Kong. This study produced digital maps of more than 20 soil quality indicators of a study area in Hong Kong, using digital soil mapping techniques. These maps were then employed to evaluate the soil quality of this area via the application of scoring functions and integrated quality index (IQI) methods. The accuracy and uncertainty of the evaluated spatial soil quality information were assessed based on a probability sample and sequential Gaussian simulation (SGS), respectively. The sources of uncertainty were analyzed using stochastic sensitivity analysis. The results showed that mapping accuracy varied dramatically among soil indicators, with soil quality index (SQI) in the area ranging from 0.43 to 0.87. The obtained spatial soil quality information appeared to be moderately accurate with high uncertainty, which suggests that it cannot be fully relied on. The soil quality could have been overestimated with a probability of more than 0.95 for nearly half of the study area while being underestimated for 0.2% of the study area. Hence, this study shows that it is vitally important to derive uncertainty for soil quality information evaluated based on digital soil maps. Generally, heavily weighted soil quality indicators in the soil quality evaluation model contributed the most uncertainty, such as available phosphorus (A-P), total phosphorus (TP), and bulk density in this study. [ABSTRACT FROM AUTHOR]

Abstract: The effects of urbanization on soil have been accelerating around the globe and there is a need for these effects to be evaluated precisely. In order to do this, spatial outliers and mapping uncertainty should be handled properly. The present study aimed to deal with these two problems using a case study of soil pH and particle fractions (i.e., sand, silt and clay) in Hong Kong. Based on 133 topsoil samples collected in an urban and peri-urban mixed area of Hong Kong, robust estimators for spatial variogram were first adopted with a procedure for identifying spatial outliers to remove spatial outliers. Then, 1000 models were simulated for each soil property using the methods of maximum likelihood and Monte Carlo Markov Chain, in order to characterize parameter uncertainty. Finally, 100 of the 1000 simulated models were randomly selected to construct soil maps using kriging, and the interpolation uncertainty was characterized using Sequential Gaussian Simulation, generating 1000 simulations for each model. Based on the total 100,000 simulated values at each location for each soil property, temporal variation of the soil property at the location across the past half century was derived against the soil series map established in 1960s for this area. Probability that soil property value changed after urbanization was also computed, in order to show if soil property has changed with a significant confidence. Results showed that the situation of spatial outliers in the soil data of the present study was not serious and mapping uncertainties were large. Temporal variations of soil pH and particle fractions obtained through comparison between soil maps of pre-urbanization and post-urbanization were not completely attributed to the effects of urbanization, but partially to mapping uncertainties. Urbanization in Hong Kong during the past half century influenced soil pH slightly and influenced soil particle fractions relatively strongly. [Copyright &y& Elsevier]