综合土地利用及空间异质性的土壤有机碳空间插值模型

doi:10.13287/j.1001-9332.201801.013

应用生态学报 ›› 2018, Vol. 29 ›› Issue (1): 238-246.doi: 10.13287/j.1001-9332.201801.013

综合土地利用及空间异质性的土壤有机碳空间插值模型

吴子豪¹, 刘艳芳^1,2,3, 陈奕云^1,2,3,4,5*, 郭龙⁶, 姜庆虎⁷, 王少辰¹

¹武汉大学资源与环境科学学院, 武汉 430079;
²武汉大学地球空间信息技术协同创新中心, 武汉 430079;
³武汉大学教育部地理信息系统重点实验室, 武汉 430079;
⁴土壤与农业可持续发展国家重点实验室, 南京 210008;
⁵武汉大学苏州研究院, 苏州 215123;
⁶华中农业大学资源与环境学院, 武汉 430070;
⁷中国科学院武汉植物园水生植物与流域生态重点实验室, 武汉 430074

收稿日期:2017-06-09 出版日期:2018-01-18 发布日期:2018-01-18
通讯作者: * E-mail: chenyy@whu.edu.cn
作者简介:吴子豪, 男, 1994年生, 博士研究生.主要从事土地信息科学研究. E-mail: zihaowu88@163.com
基金资助:
本文由国家自然科学基金项目(41501444,41771440,41401448)和苏州市应用基础农业项目(SYN201422)资助

Spatial interpolation model of soil organic carbon density considering land-use and spatial heterogeneity.

WU Zi-hao¹, LIU Yan-fang^1,2,3, CHEN Yi-yun^1,2,3,4,5*, GUO Long⁶, JIANG Qing-hu⁷, WANG Shao-chen¹

¹School of Resource and Environment Science, Wuhan University, Wuhan 430079, China;
²Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China;
³Ministry of Education Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China;
⁴State Key Laboratory of Soil and Sustainable Agriculture, Nanjing 210008, China;
⁵Suzhou Institute, Wuhan University, Suzhou 215123, Jiangsu, China;
⁶College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China;
⁷Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China

Received:2017-06-09 Online:2018-01-18 Published:2018-01-18
Contact: * E-mail: chenyy@whu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (41501444, 41771440, 41401448) and the Suzhou Applied Basic Agriculture Project (SYN201422)

摘要/Abstract

摘要： 土壤有机碳库是陆地碳库的重要组成部分,土壤有机碳库及其动态变化对陆地生态系统碳循环有着重要的影响.土壤有机碳密度(SOCD)是土壤碳储量的重要参数,也是评价农田土壤质量的重要指标,准确预测区域SOCD空间分布对发展精确农业有重要意义.本文使用江汉平原地区242个农田土壤样本数据,探究平原地区土地利用类型对SOCD空间分布的影响,以及当SOCD空间分布规律呈现空间异质性且存在空间异常值的情况下,虚拟变量回归克里格法(DV_RK)、均值中心化克里格法(MC_OK1)和中位数中心化克里格法(MC_OK2)这3种结合土地利用的克里格法在SOCD空间预测中的应用.结果表明: 土地利用方式的差异是研究区水田和水浇地SOCD存在空间异质性的原因之一,导致SOCD存在空间非平稳特征,降低了普通克里格法(OK)的预测精度;而DV_RK、MC_OK1和MC_OK2在消除了由土地利用引起的SOCD空间异质性对建模的影响后,模型的稳定性提升,其预测精度均高于OK,其中,MC_OK2的模型可靠程度、预测精度和对SOCD总方差的解释能力最优.因此,土地利用类型作为易获取的辅助变量,可以有效减弱空间异质性和空间异常值对SOCD空间插值的影响,提升模型精度,降低不确定性,并与MC_OK2结合生成更高精度的SOCD空间分布图,帮助揭示SOCD空间分异规律,指导农业生产.

关键词: 土壤有机碳密度, 土地利用, 克里格插值, 空间异质性, 水田和水浇地

Abstract: Soil organic carbon pool is an important component of terrestrial carbon pool. Soil organic carbon pool and its dynamic change have important influence on carbon cycle in terrestrial ecosystem. Soil organic carbon density (SOCD) is an important parameter of soil carbon storage, and it is also an important index to evaluate farmland soil quality. Accurate prediction of regional organic carbon density spatial distribution is of great significance to the development of precision agriculture. A total of 242 farmland soil samples collected from the Jianghan Plain were used to explore the effects of land use types on the spatial distribution of SOCD in plain areas. Moreover, in the presence of spatial heterogeneity and spatial outliers of SOCD, three Kriging approaches combining land use types were used for the spatial prediction of SOCD. They were dummy variable regression Kriging (DV_RK), mean centering ordinary Kriging (MC_OK1) and median centering ordinary Kriging (MC_OK2). Results showed that the difference of land use types between paddy field and irrigable land was one of the reasons for the spatial heterogeneity of SOCD in the study area, resulting in spatial non-stationary characteristics of SOCD and lowering the performance of OK. DV_RK, MC_OK1 and MC_OK2, however, eliminating the impacts of SOCD spatialheterogeneity caused by land use types while modeling, enhancing the model stability. Therefore, the prediction accuracy of these three models was higher than that of ordinary Kriging (OK). Moreover, MC_OK2 outperformed the others in terms of model reliability, prediction accuracy and the ability to explain the total variance of SOCD. In summary, as an easily accessed auxiliary variable, land use type could effectively decrease the effects of spatial heterogeneity and spatial outliers on SOCD spatial interpolation model, improving the prediction performance and reducing the model uncertainty. SOCD map with higher quality could also be achieved to help reveal the spatial characteristics of SOCD for guiding the agricultural production.

Key words: paddy field and irrigable land, soil organic carbon density, Kriging interpolation, spatial heterogeneity, land use type

吴子豪, 刘艳芳, 陈奕云, 郭龙, 姜庆虎, 王少辰. 综合土地利用及空间异质性的土壤有机碳空间插值模型[J]. 应用生态学报, 2018, 29(1): 238-246.

WU Zi-hao, LIU Yan-fang, CHEN Yi-yun, GUO Long, JIANG Qing-hu, WANG Shao-chen. Spatial interpolation model of soil organic carbon density considering land-use and spatial heterogeneity.[J]. Chinese Journal of Applied Ecology, 2018, 29(1): 238-246.

参考文献

[1] Tang G-Y (唐国勇), Huang D-Y (黄道友), Huang M (黄敏), et al. Spatial variations of organic carbon in surface soils in a hilly landscape of the red-earth region and their affecting factors. Acta Pedologica Sinica (土壤学报), 2010, 47(4): 753-759 (in Chinese)
[2] Zhang Z-G (张治国), Hu Y-B (胡友标), Zheng Y-H (郑永红), et al. Advances on soil organic carbon cycling research in territorial ecosystem. Bulletin of Soil and Water Conservation (水土保持通报), 2016, 36(4): 339-345 (in Chinese)
[3] Zhou S (周双). Analysis of Influencing Factors and Prediction of Soil Organic Carbon at Agricultural Landscape in Hilly Area. Master Thesis. Chongqing: Southwest University, 2016 (in Chinese)
[4] Gao J-L (高君亮), Luo F-M (罗凤敏), Gao Y (高永), et al. Review on the calculation of the soil carbon storage in typical terrestrial ecosystems. Ecological Science (生态科学), 2016, 35(6): 191-198 (in Chinese)
[5] Chen J-H (陈杰华). Study on Storage and Evolution Trend of Soil Organic Carbon in Cropland in Chongqing City. PhD Thesis. Chongqing: Southwest University, 2013 (in Chinese)
[6] Liu YL, Guo L, Jiang QH, et al. Comparing geospatial techniques to predict SOC stocks. Soil and Tillage Research, 2015, 148: 46-58
[7] Tang H-M (唐海明), Cheng K-K(程凯凯), Xiao X-P (肖小平), et al. Effects of different winter cover crops on soil organic carbon in a double cropping rice paddy field. Chinese Journal of Applied Ecology (应用生态学报), 2017, 28(2): 465-473 (in Chinese)
[8] Wang K, Zhang CR, Li WD. Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and coKriging. Applied Geography, 2013, 42: 73-85
[9] Qin CZ, Zhu AX, Qiu WL, et al. Mapping soil organic matter in small low-relief catchments using fuzzy slope position information. Geoderma, 2012, 171: 64-74
[10] Liu F (刘峰), Zhu A-X (朱阿兴), Pei T (裴韬), et al. Application of high temporal resolution satellite remote sensing in identifying soil texture patterns. Geo-Information Science (地球信息科学学报), 2011, 12(5): 733-740 (in Chinese)
[11] Liu Y-F (刘艳芳), Song Y-L (宋玉玲), Guo L (郭龙), et al. Geostatistical models of soil organic carbon density prediction based on soil hyperspectral reflectance. Transactions of the Chinese Society of Agricultural Engineering (农业工程学报), 2017, 33(2): 183-191 (in Chinese)
[12] Guo L, Zhao C, Zhang HT, et al. Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology. Geoderma, 2017, 285: 280-292
[13] Wu Q, Li QL, Gao JB, et al. Non-algorithmically integrating land use type with spatial interpolation of surface soil nutrients in an urbanizing watershed. Pedosphere, 2017, 27: 147-154
[14] Zhang Z-Q (张忠启), Shi X-Z (史学正), Yu D-S (于东升), et al. Spatial prediction method of soil organic matter and total nitrogen in red soil region. Acta Ecologica Sinica (生态学报), 2010, 30(19): 5338-5345 (in Chinese)
[15] Gu C-J (顾成军). Application of Kriging method in spatial prediction of regional soil organic carbon. Soil and Fertilizer Sciences in China (中国土壤与肥料), 2014(3): 93-97 (in Chinese)
[16] Liu Y-F (刘艳芳), Lu Y-N (卢延年), Guo L (郭龙), et al. Construction of calibration set based on the land use types in visible and near-infrared (VIS-NIR) model for soil organic matter estimation. Acta Pedologica Sinica (土壤学报), 2016, 53(2): 332-341 (inChinese)
[17] Liang C-S (梁重山), Dang Z (党志), Liu C-Q (刘丛强). Rapid determination of total organic carbon in soil sediment samples. Acta Pedologica Sinica (土壤学报), 2002, 39(1): 129-133 (in Chinese)
[18] Dai J-H (戴金辉), Yuan J (袁靖). Comparison of univariate analysis of variance and multiple linear regression analysis. Statistics and Decision (统计与决策), 2016(9): 23-26 (in Chinese)
[19] Yang S-H (杨顺华), Zhang H-T (张海涛), Guo L (郭龙), et al. Spatial interpolation of soil organic matter using regression Krigingand geographically weighted regression Kriging. Chinese Journal of Applied Ecology (应用生态学报), 2015, 26(6): 1649-1656 (in Chinese)
[20] Xu J-W (许金炜). GDP forecasting based on virtual variable regression and SARIMA combination model. Statistics and Decision (统计与决策), 2016(24): 38-41 (in Chinese)
[21] Wang Z-G (王志刚), Zhao Y-C (赵永存), Huang B (黄标), et al. Effects of sample size on spatial characterization of soil fertility properties in an agricultural area of the Yangtze River Delta region, China. Soils (土壤), 2010, 42(3): 421-428 (in Chinese)
[22] Yao XL, Fu BJ, Lu YH, et al. Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment. PLoS One, 2013, 8(1): e54660
[23] Gu C-J (顾成军), Shi X-Z (史学正), Yu D-S (于东升). Indicating method on spatial distribution ofregional soil organic carbon by combining land use and Kriging. Research of Soil and Water Conservation (水土保持研究), 2014, 21(2): 39-42 (in Chinese)
[24] Li H-C (李洪成). Data normality test method and its statistical software realization. Statistics and Decision (统计与决策), 2009 (12): 155-156 (in Chinese)
[25] Yao L-X (姚丽贤), Zhou X-C (周修冲), Cai Y-F (蔡永发), et al. Spatial variability of soil properties at different sampling intensities and accuracy of their estimation. Soils (土壤), 2004, 36(5): 538-542 (in Chinese)
[26] Dowd PA. The variogram and kriging: robust and resis-tant estimators// Verly G, David M, Journel AG, eds. Geostatistics for Natural Resources Characterization. Hingham: Reidel Publishing Co., 1984: 91-106
[27] Costa JF. Reducing the impact of outliers in ore reserves estimation. Mathematical Geology, 2003, 35: 323-345
[28] Yang H (杨红), Xu C-C (徐唱唱), Sai M (赛曼), et al. Effects of land use on soil moisture, pH and electrical conductivity. Acta Agriculturae Zhejiangensis (浙江农业学报), 2016, 28(11): 1922-1927 (in Chinese)
[29] Liao H-K (廖洪凯), Li J (李娟), Long J (龙健), et al. Effects of land use and abandonment on soil labile organic carbon in the Karst region of southwest China. Environmental Science (环境科学), 2014, 35(1): 240-247 (in Chinese)
[30] Zhang X (张璇), Xie L-Y (谢立勇), Guo L-Y (郭李萍), et al. Modelling the changes of soil organic carbon under different management practices using Daycent model in North China. Chinese Journal of Applied Ecology (应用生态学报), 2016, 27(2): 539-548 (in Chinese)
[31] Zhang J-X (张景雄). Spatial Information Scale, Uncertainty and Fusion. Wuhan: Wuhan University Press, 2008 (in Chinese)
[32] Wu Y-J (吴运金), He Y (何跃), Deng S-P (邓绍坡), et al. Identification of outliers of heavy metals in soil of a contaminated site and separation of point source effects. Journal of Ecology and Rural Environment (生态与农村环境学报), 2013, 29(6): 789-795 (in Chinese)
[33] Wen W, Wang YF, Yang L, et al. Mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess Plateau of China: A comparative study based on three Kriging methods and a soil land inference model (SoLIM). Environmental Earth Sciences, 2015, 73: 239-251
[34] Kerry R, Oliver MA. Average variograms to guide soil sampling. International Journal of Applied Earth Observation and Geoinformation, 2004, 5: 307-325
[35] Blöschl G. Scaling issues in snow hydrology. Hydrological Processes, 1999, 13: 2149-2175
[36] Chen C (陈朝), Lyu C-H (吕昌河), Fan L (范兰), et al. Effects of land use change on soil organic carbon: A review. Acta Ecologica Sinica (生态学报), 2011, 31(18): 5358-5371 (in Chinese)
[37] Hardy MA. Trans, He G-Y (贺光烨). Dummy Variable Regression. Shanghai: Shanghai People’s Press, 2016 (in Chinese)
[38] Qiao Y-N (乔依娜). Analysis of Influencing Factors and the Content of Soil Available Fe, Mn, Cu and Zn at Agriculture Landscape in Hilly Region. Master Thesis. Chongqing: Southwest University, 2015 (in Chinese)

综合土地利用及空间异质性的土壤有机碳空间插值模型

Spatial interpolation model of soil organic carbon density considering land-use and spatial heterogeneity.

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	于海霞, 王禹骁. 东江流域生态系统服务价值的时空演变及空间分异机制 [J]. 应用生态学报, 2023, 34(9): 2498-2506.
[2]	祁育汀, 张平, 刘雷, 马雪楠, 王欢, 赵娟. 关中平原城市群土地利用结构多情景优化和生态系统服务价值预测 [J]. 应用生态学报, 2023, 34(9): 2507-2517.
[3]	马扩, 郝丽娜, 童新, 段利民, 曹文梅, 康雪儿, 刘小勇, 刘廷玺. 科尔沁沙丘-草甸相间地区生态安全格局的时空演变 [J]. 应用生态学报, 2023, 34(8): 2215-2225.
[4]	周文武, 舒清态, 王书伟, 杨正道, 罗绍龙, 胥丽, 肖劲楠. 基于多源遥感数据协同的滇西北森林郁闭度估测 [J]. 应用生态学报, 2023, 34(7): 1806-1816.
[5]	付建新. 1980—2020年汾河流域“三生空间”土地利用功能变化及其驱动力 [J]. 应用生态学报, 2023, 34(7): 1901-1911.
[6]	李久林, 胡大卫, 储金龙, 尹海伟. 基于未来情景模拟的安徽省潜山市生态网络构建与优化 [J]. 应用生态学报, 2023, 34(6): 1474-1482.
[7]	徐彩瑶, 任燕, 孔凡斌. 浙江省土地利用变化对生态系统固碳服务的影响及其预测 [J]. 应用生态学报, 2023, 34(6): 1610-1620.
[8]	苟国花, 樊军, 王茜, 周明星, 杨学亭. 基于最小数据集的青藏高原南北部不同土地利用方式土壤质量评价 [J]. 应用生态学报, 2023, 34(5): 1360-1366.
[9]	王佳楠, 贾燕锋, 范昊明. 辽西低山棕壤丘陵区小流域泥沙连通性与土壤侵蚀的空间分布特征 [J]. 应用生态学报, 2023, 34(3): 726-732.
[10]	叶静, 关瑜, 陈影. 滨海盐碱区土地利用功能权衡与协同关系及分区——以河北省黄骅市为例 [J]. 应用生态学报, 2023, 34(2): 423-432.
[11]	郝嘉元, 智烈慧, 李晓文, 董世魁, 李巍. 青藏高原土地利用格局和生态系统服务的时空演变特征及关系 [J]. 应用生态学报, 2023, 34(11): 3053-3063.
[12]	谈旭, 王承武. 伊犁河谷生态系统服务价值时空演变及其驱动因素 [J]. 应用生态学报, 2023, 34(10): 2747-2756.
[13]	谭昭昭, 陈毓遒, 丁憬枫, 刘梦迪, 王浩, 王江. 浙江东部沿海城市土地利用模拟及生态系统服务价值评估 [J]. 应用生态学报, 2023, 34(10): 2777-2787.
[14]	吴可欣, 税伟, 薛成旨, 黄云慧, 江聪. 珠江源区生境质量对土地利用变化的时空响应 [J]. 应用生态学报, 2023, 34(1): 169-177.
[15]	王茜, 穆琪, 罗漫雅, 赵永华, 杨舒媛, 张磊, 瞿植. 秦岭生态系统服务协同与权衡的时空异质性 [J]. 应用生态学报, 2022, 33(8): 2057-2067.