Xiaoxue Guo, Zhijun Liu, D. Gao, Cheng-Tang Xu, Kexin Zhang, Xianzhao Liu
{"title":"土地利用模式在城市绿地土壤有机碳空间预测中的应用","authors":"Xiaoxue Guo, Zhijun Liu, D. Gao, Cheng-Tang Xu, Kexin Zhang, Xianzhao Liu","doi":"10.31545/intagr/156027","DOIUrl":null,"url":null,"abstract":". 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.","PeriodicalId":13959,"journal":{"name":"International Agrophysics","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces\",\"authors\":\"Xiaoxue Guo, Zhijun Liu, D. Gao, Cheng-Tang Xu, Kexin Zhang, Xianzhao Liu\",\"doi\":\"10.31545/intagr/156027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". 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. 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Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces
. 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.
期刊介绍:
The journal is focused on the soil-plant-atmosphere system. The journal publishes original research and review papers on any subject regarding soil, plant and atmosphere and the interface in between. Manuscripts on postharvest processing and quality of crops are also welcomed.
Particularly the journal is focused on the following areas:
implications of agricultural land use, soil management and climate change on production of biomass and renewable energy, soil structure, cycling of carbon, water, heat and nutrients, biota, greenhouse gases and environment,
soil-plant-atmosphere continuum and ways of its regulation to increase efficiency of water, energy and chemicals in agriculture,
postharvest management and processing of agricultural and horticultural products in relation to food quality and safety,
mathematical modeling of physical processes affecting environment quality, plant production and postharvest processing,
advances in sensors and communication devices to measure and collect information about physical conditions in agricultural and natural environments.
Papers accepted in the International Agrophysics should reveal substantial novelty and include thoughtful physical, biological and chemical interpretation and accurate description of the methods used.
All manuscripts are initially checked on topic suitability and linguistic quality.