Guangxi Academy of Forestry science

Nanning, China

Guangxi Academy of Forestry science

Nanning, China
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Sun X.-L.,Hong Kong Baptist University | Sun X.-L.,CAS Nanjing Institute of Soil Science | Sun X.-L.,Guangxi University | Sun X.-L.,The Open University of Hong Kong | And 7 more authors.
Soil Science Society of America Journal | Year: 2012

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. © Soil Science Society of America.

Sun X.-L.,CAS Nanjing Institute of Soil Science | Sun X.-L.,Guangxi University | Sun X.-L.,Hong Kong Baptist University | Zhao Y.-G.,CAS Nanjing Institute of Soil Science | And 4 more authors.
Soil Use and Management | Year: 2012

Estimation of spatio-temporal change of soil is needed for various purposes. Commonly used methods for the estimation have some shortcomings. To estimate spatio-temporal change of soil organic matter (SOM) in Jiangsu province, China, this study explored benefits of digital soil maps (DSM) by handling mapping uncertainty using stochastic simulation. First, SOM maps on different dates, the 1980s and 2006-2007, were constructed using robust geostatistical methods. Then, sequential Gaussian simulation (SGS) was used to generate 500 realizations of SOM in the area for the two dates. Finally, E-type (i.e. conditional mean) temporal change of SOM and its associated uncertainty, probability and confidence interval were computed. Results showed that SOM increased in 70% of Jiangsu province and decreased in the remaining 30% during the past decades. As a whole, SOM increased by 0.22% on average. Spatial variance of SOM diminished, but the major spatial pattern was retained. The maps of probability and confidence intervals for SOM change gave more detailed information and credibility about this change. Comparatively, variance of spatio-temporal change of SOM derived using SGS was much smaller than sum of separate kriging variances for the two dates, because of lower mapping variances derived using SGS. This suggests an advantage of the method based on digital soil maps with uncertainty dealt with using SGS for deriving spatio-temporal change in soil. © 2012 The Authors. Journal compilation © 2012 British Society of Soil Science.

Sun X.-L.,CAS Nanjing Institute of Soil Science | Sun X.-L.,The Open University of Hong Kong | Sun X.-L.,Guangxi University | Zhao Y.-G.,CAS Nanjing Institute of Soil Science | And 8 more authors.
Geoderma | Year: 2012

Fuzzy c-means clustering (FCM) has been used frequently in digital soil mapping. One of the key issues in applying FCM is the determination of the appropriate classification parameters of the fuzzy exponent (m) and the number of clusters (c). To determine the optimal selection of appropriate m and c values, in this study, we first used two simulated datasets to demonstrate the sensitivity of three commonly used validity functions to m and c. These two simulated datasets contained overlapping clusters and hierarchical clusters, respectively. The three studied validity functions were fuzzy performance index (FPI), compactness and separation (S) and a derivative of the objective function with respect to the fuzzy exponent (-[(δJ E/δm)c 0.5]). Then, a case study mapping soil organic matter (SOM) based on memberships from FCM clustering terrain attributes was conducted to investigate the sensitivity of soil maps to m and c. The results of the study on the simulated datasets showed that the three validity functions were sensitive in differing degrees to the structures of the clustered datasets under a wide range of m, but the sensitivities and the range of m were different for different validity functions and depended on the clustered datasets. The results from the case study of the soil mapping showed that soil maps based on FCM clustering were sensitive to m and c, but only the spatial variations of SOM presented on the maps were significantly sensitive to c. Furthermore, mapping accuracy was slightly sensitive to m and c. It is concluded that there was a range of optimal m over which digital soil maps did not change very much, but this was not certain for c, given that the spatial variation presented on the maps changed significantly with c. © 2011 Elsevier B.V.

Sun X.-L.,CAS Nanjing Institute of Soil Science | Sun X.-L.,Sun Yat Sen University | Wu Y.-J.,Nanjing Institute of Environmental Sciences | Wang H.-L.,Guangxi Academy of Forestry science | And 2 more authors.
Mathematical Geosciences | Year: 2014

Information on the spatial distribution of soil particle-size fractions (psf) is required for a wide range of applications. Geostatistics is often used to map spatial distribution from point observations; however, for compositional data such as soil psf, conventional multivariate geostatistics are not optimal. Several solutions have been proposed, including compositional kriging and transformation to a composition followed by cokriging. These have been shown to perform differently in different situations, so that there is no procedure to choose an optimal method. To address this, two case studies of soil psf mapping were carried out using compositional kriging, log-ratio cokriging, cokriging, and additive log-ratio cokriging; and the performance of Mahalanobis distance as a criterion for choosing an optimal mapping method was tested. All methods generated very similar results. However, the compositional kriging and cokriging results were slightly more similar to each other than to the other pair, as were log-ratio cokriging and additive log-ratio cokriging. The similar results of the two methods within each pair were due to similarities of the methods themselves, for example, the same variogram models and prediction techniques, and the similar results between the two pairs were due to the mathematical relationship between original and log-ratio transformed data. Mahalanobis distance did not prove to be a good indicator for selecting an optimal method to map soil psf. © 2014 International Association for Mathematical Geosciences.

Sun X.-L.,Sun Yat Sen University | Sun X.-L.,CAS Nanjing Institute of Soil Science | Wu Y.-J.,Nanjing Institute of Environmental Sciences | Lou Y.-L.,Guangxi Academy of Forestry science | And 4 more authors.
European Journal of Soil Science | Year: 2015

The rapid developments in the acquisition of data on soil should enable pedologists to update existing digital soil maps readily. The methods by which that is done must take into account temporal change in soil properties and local differences in spatial variation. The common mapping techniques will have to be modified to make full use of digital data. We show what can be achieved with a case study on updating maps of soil organic matter (SOM) in Jiangsu Province, China, with three sets of soil data collected in the 1980s, 2000 and 2006. Our results showed that temporal changes in SOM between the three sampling periods occurred in only very small parts of the regions. Models of spatial variation of SOM based on the data collected in the 1980s and 2006 for the whole region differed somewhat, whereas models based on the data collected in the 1980s, 2000 and 2006 for the Taihu region (south Jiangsu) were significantly different. As updating with Bayesian maximum entropy continued, the accuracy of prediction increased and that of the prediction variance decreased. Finally, our study leads us to suggest improved technologies for updating digital soil maps with new data. © 2015 British Society of Soil Science.

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