Exploring the optimal sampling density to characterize spatial heterogeneity of soil carbon stocks in a Karst Region

Zhang, Zhenming; Huang, Xianfei

doi:10.1002/agj2.20467

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…In addition, the selection of samples in the study area also affects the interpolation accuracy. Zhang et al (2021) compared the interpolation accuracy of SOCS in the Karst Region at different sampling distances, and it was concluded that the interpolation accuracy varied with sample distance. When interpolating in flat areas, it is more sensitive to the distribution of sample points than when interpolating in complex terrain (Long et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces

Guo¹,

Liu²,

Gao³

et al. 2022

Int. Agrophys.

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A b s t r a c t . The challenge of predicting soil organic carbon distribution accurately has received great attention in order to support urban green space soil management during climate change. This study compared four geostatistical methods: kriging combined with land use, ordinary kriging, inverse distance weighting and radial basis function, to predict the spatial distribution patterns of soil organic carbon content and soil organic carbon density in the Xiong'an New Area, estimate organic carbon stocks, and assess the role of land use types in the spatial prediction of soil organic carbon stocks. The results showed that the soil organic carbon content decreased with increasing soil depth, and was significantly affected by different land use types (p<0.05). The correlation coefficient values of kriging combined with land use were on average 0.229 higher than those of other methods. The root mean squared error and the mean absolute error of kriging combined with land use were on average 0.148 and 0.139 lower than those of the other methods. Kriging combined with land use has a greater advantage over other methods in predicting the spatial distribution of soil organic carbon content, and also the spatial distribution of soil organic carbon density and the spatial distribution of soil organic carbon, the prediction results of the four interpolation methods were similar. The average soil organic carbon density was 2085 Gg (0-30 cm) and 1363 Gg (30-60 cm). In conclusion, land use type clearly influences the spatial distribution of soil organic carbon in urban areas, and by using land use type as auxiliary data, we can obtain a more accurate spatial distribution of soil organic carbon and predict the total storage capacity of the soil. This study may result in significant advances in the spatial prediction of soil organic carbon for urban areas. K e y w o r d s: geostatistics, soil organic carbon, spatial distribution, urban land use, Xiong'an New Area

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Section: Discussionmentioning

confidence: 99%

Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces

Guo¹,

Liu²,

Gao³

et al. 2022

Int. Agrophys.

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“…This differs from the results found by other scholars at different regions and scales. For example, in a small karst-basin scale with an area of 75 km 2 in Guizhou Province, southern China, 357 sample points could better express the spatial variation of 0-20 cm soil organic carbon, equivalent to 4760 sample points collected on an area of 1000 km 2 [26]. In a small undulating hilly area with an area of 184 hm 2 in São Paulo state of southwestern Brazil, collecting one sample point per 3.75 hm 2 and one sample point per 7.2 hm 2 could satisfy the expression of the spatial variability of SOM and clay, which was equivalent to 26,667 samples and 13,889 samples collected on 1000 km 2 , respectively [27].…”

Section: Comparison Of the Rational Sampling Numbers In Different Reg...mentioning

confidence: 99%

Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China

et al. 2022

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Soil organic matter (SOM) plays a crucial role in promoting soil tillage, improving soil fertility and providing crop nutrients. Investigation and sampling are the premise and basis for understanding the spatial distribution of SOM. The number of sampling points will affect the accuracy of spatial variation of SOM. Therefore, it is important scientific work to determine a reasonable number of sampling points under the premise of ensuring accuracy. In this study, Kenli County, a typical area of the Yellow River Delta in China, was taken as an example to investigate the effect of different sampling points on spatial-variation expression of SOM. A total of 12 sample subsets (including 900 samples) were randomly sampled at equal intervals from the 900 sample points, using geographic information system (GIS) technology and geostatistical analyses to explore the optimal number of samples. The results showed that the SOM content in the study area had a lower-middle degree of variation. As the number of sample points decreased, the spatial distribution of SOM showed the gradual weakening of detail-characterization ability; and when the number of sample points was too small (<100), there was a wrong expression that was not consistent with the actual situation. The value of RMSE has no obvious regularity with the change of sample number. The values of both ME and ASE showed a significant inflection point when the number of samples was 150 and remained around 0 and 4 as the number of samples increased, respectively. Combined with the three indicators of ME, RMSE and ASE, collecting at least 150 samples can satisfy the spatial-variation expression of SOM, equivalent to 107 sample points within the area of 1000 km2. The research results could provide important references for investigation of SOM content in areas with similar natural geographical conditions.

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Differences of soil carbon pools and crop growth across different typical agricultural fields in China: The role of geochemistry and climate change

Yang,

Zheng,

Wan

et al. 2024

Environmental Research

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Exploring the optimal sampling density to characterize spatial heterogeneity of soil carbon stocks in a Karst Region

Cited by 3 publications

References 41 publications

Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces

Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces

Determining Optimal Sampling Numbers to Investigate the Soil Organic Matter in a Typical County of the Yellow River Delta, China

Differences of soil carbon pools and crop growth across different typical agricultural fields in China: The role of geochemistry and climate change

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