Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging
19
Citation
33
Reference
10
Related Paper
Citation Trend
Abstract:
In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models—random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)—were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.Keywords:
Digital Soil Mapping
Soil carbon
Digital Soil Mapping
Soil survey
USDA soil taxonomy
Digital mapping
Soil series
Cite
Citations (32)
Digital soil maps of different scales have been widely used in the estimates of soil organic carbon (SOC). However, exactly how the scale of the soil map impacts SOC dynamics and the key factors influencing SOC estimations during the map generalization process have rarely been assessed. In this research, a newly available soil database of Zhejiang Province in southeastern China, which contains 2154 geo-referenced soil profiles and six digital soil maps at scales of 1:50,000, 1:250,000, 1:500,000, 1:1,000,000, 1:4,000,000, and 1:10,000,000, and three different linkage methods (i.e., the mean, median, and pedological professional knowledge-based (PKB) methods) were used to evaluate their influence on the estimates of SOC. The findings of our study were as follows: (1) The scale of the soil map was identified as being of crucial importance for regional SOC estimations. (2) The linkage method played an important role in the accurate estimates of SOC, and the PKB method could provide the most detailed information on the spatial variability of SOC estimations. (3) The key factors affecting the estimates of SOC during the map generalization process as the soil map scale decreased from 1:50,000 to 1:10,000,000 were determined, including the changes in the number of soil profiles, the conversions between different soil types, the conversions from non-soils to soils, and the linkage methods of aggregating the SOC density values of soil profiles to represent map units. The results suggest that the most detailed 1:50,000-scale soil map coupled with the PKB method would be the optimal choice for regional SOC estimations in China.
Soil carbon
Digital Soil Mapping
Linkage (software)
Soil survey
Cite
Citations (1)
Digital Soil Mapping
Digital mapping
Soil survey
Cite
Citations (5)
Digital Soil Mapping
Elevation (ballistics)
Land Cover
Soil survey
Cite
Citations (30)
The main objective of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project is to significantly extend the potential, how demands on spatial soil related information could be satisfied in Hungary. Although a great amount of soil information is available due to former mappings and surveys, there are more and more frequently emerging discrepancies between the available and the expected data. The gaps are planned to be filled with optimized digital soil mapping (DSM) products heavily based on legacy soil data, which still represent a valuable treasure of soil information at the present time. The paper presents three approaches for the application of Hungarian legacy soil data in object oriented digital soil mapping.
Digital Soil Mapping
Soil survey
USDA soil taxonomy
Treasure
Cite
Citations (10)
Digital soil mapping utilizes computer analysis and digital data to create a soil map. This exciting field provides the opportunity for employing new data and developing new techniques to create better soil maps, and to do so more efficiently. Although digital soil mapping often relies on concepts developed from traditional soil mapping, it is distinguished by the quantitative nature of the data and the analysis processes employed to generate the map. Digital soil maps are critical resources for precision agriculture and land‐use and conservation planning, as well as for environmental modeling. Pedometrics is a quantitative and statistical form of pedology. Both the quantitative basis of digital soil mapping and the importance of soil geography to pedology make digital soil mapping closely related to pedometrics. However, in the dynamic and diverse field of soil science, these terms are not necessarily synonymous, because not all techniques for digital soil mapping are considered to be a part of the field of pedometrics.
Pedology
Digital Soil Mapping
Digital mapping
Soil survey
Cite
Citations (2)
Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.
Digital Soil Mapping
Thematic Mapper
Thematic map
Soil survey
Digital mapping
Cite
Citations (69)
Versa
Digital Soil Mapping
Cite
Citations (10)
Conventional soil maps, as the major data source for information on the spatial variation of soil, are limited in terms of both the level of spatial detail and the accuracy of soil attributes. These soil maps, however, contain valuable knowledge on soil–environment relationships. Such knowledge can be extracted for updating conventional soil maps through the use of available high-quality data on environmental variables and data analysis techniques. We developed a method to update conventional soil maps using digital soil mapping techniques without additional field work, which can be used in situations where the study area contains no or few soil profile descriptions at points. The basis of the method is that soil polygons on a conventional soil map correspond to landscape units, which can be considered as combinations of environmental factors. Such environmental combinations were approximated through fuzzy clustering on the environmental factors. We extracted the knowledge on soil–environment relationships by relating the environmental combinations to the mapped soil types. The extracted knowledge was then used for soil mapping using the Soil Land Inference Model (SoLIM) framework. This method was demonstrated through a case study for updating a conventional 1:20,000 soil map of Wakefield, NB, Canada. The case study showed that the updated digital soil map contained much greater spatial detail than the conventional soil map. Field validation indicated that the accuracy of the updated soil map was much higher than the conventional soil map at the level of soil associations with drainage classes, indicating that the proposed method is an effective approach to updating conventional soil maps.
Digital Soil Mapping
Soil functions
Digital mapping
Soil survey
Soil series
Cite
Citations (90)
Digital Soil Mapping
Soil survey
Digital mapping
Cite
Citations (32)