太湖地区水稻土有机碳空间表征尺度效应研究
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摘要
土壤有机碳库在保持土壤肥力、提高土壤质量以及减缓气候变化中均起着重要作用,因此,土壤有机碳储量及源汇问题一直是国内外研究的热点问题。水稻土是自然土壤在人为水耕熟化过程中形成的特殊的人为湿地土壤,属《中国土壤系统分类》中独特的水耕人为土亚纲,是国际上公认为中国特色的土壤。根据《全国第二次土壤普查数据》,中国水耕地土壤的有机碳含量平均是旱耕地的137%,水稻土水耕熟化过程中有机碳的积累是普遍趋势。在农业土壤固碳潜力中,水稻土明显占较高的份额。因而对水稻土有机碳储量的准确估算和空间表征分布进行研究,对中国水稻生产经营方式下农业的碳汇效应在全球农业与碳循环中具有重要意义。
对于土壤数据的空间表征,随着观察尺度变化,其比例尺和精度也在变化。在离散采样点数据有限的情况下,如何对区域尺度水稻土储量和空间分布进行精确表征有待进一步研究。利用土壤图可以实现对水稻土有机碳储量和空间分布的表征,但目前关于制图尺度对水稻土有机碳储量估算的影响还不十分清楚。空间插值技术和土壤类型法均可以实现土壤数据由点到面的尺度转换,但在区域尺度土壤属性由点到面的尺度转换中,哪种方法较好有待进一步探讨。本文以太湖地区水稻土为研究对象,基于“土壤类型GIs连接法”,将全国第二次土壤普查的1107个水稻土剖面数据分别连接到6个不同制图比例尺(1：5万、1：20万、1：50万、1：100万、1：400万、1：1400万)土壤图,建立了6个不同制图尺度的太湖地区水稻土剖面数据库,研究了制图尺度的变化对水稻土有机碳储量的估算和表征影响,并将土壤类型法与GIs技术整合来预测水稻土有机碳的空间分布,从而实现水稻土有机碳数据由点到面的尺度转换。主要结论研究如下：
(1)太湖地区不同制图尺度水稻土(0～100 cm)有机碳储量的估算结果有一定差异。从区域尺度来看,水稻土有机碳储量随制图比例尺的下降,呈现先上升,后下降再上升的变化规律；从不同地形区来看,在不同制图尺度下,四大土区所占研究区水稻土有机碳储量的排序：低洼圩田土区>冲积平原土区>太湖平原土区>低山丘陵土区。
(2)不同制图尺度水稻土有机碳总储量的估算结果主要受潴育型水稻土、潜育型水稻土、脱潜型水稻土和渗育型水稻土控制,这4个水稻土亚类的碳储量占不同制图尺度碳储量的83%以上。但不同制图尺度影响最大的是潜育型水稻土和潴育型水稻土,尤其在1：1400万制图尺度下这两个水稻土亚类的有机碳量明显高于其它尺度,这主要是由于土壤图比例尺减小的土壤图图斑概化过程所导致的。从1：5万到1：20万主要受青泥土图斑概化的影响,从1：50万到1：100万主要受以黄泥土为主的土属图斑概化的影响,而从1：400万到1：1400万水稻土碳储量的变化主要由于脱潜型水稻土、渗育型水稻土、漂洗型水稻土和淹育型水稻土的制图单元均被概化到潴育型水稻土和潜育型水稻土制图单元。
(3)在制图尺度由1：5万降低至1：1400万的过程中,水稻土有机碳储量、密度及面积均有较大变化,图斑概化过程对有机碳储量估算结果影响较大。对有机碳储量的估算,在考虑估算结果的同时也要考虑有机碳的空间分布,1：5万土壤数据库有最为详尽的有机碳属性数据和空间数据,利用“PKB”连接法,与其它5个制图尺度相比,不仅能够充分反应水稻土的有机碳的空间分布,更能够精确的估算水稻土有机碳储量。
(4)利用普通克里格法、泛克里格法、“PKB”法三种方法对太湖地区长兴县水稻土有机碳密度空间分布(0～100 cm)的预测中,“PK_B”法的预测效果最好,对总方差的解释程度为87%,预测结果的平均绝对误差及均方根误差也最小,对有机碳密度的空间分布表达较为细致,能很好的反应该地区丘陵、平原之间的差异及其它地形单元内的局部变异；其次是普通克里格法,泛克里格法的预测结果最不理想,这两种方法只能从宏观上反映水稻土有机碳密度的空间分布,平滑效应强烈。
Soil organic carbon (soil organic carbon, SOC) play an important role in conserving soil fertility, improve soil quality, and changing global climate. SOC stocks and carbon fixation is a global environmental problem which has been given wide attention. Paddy soil is hydroponic natural soil in the artificial aging process of the formation of a special man-made wetland soil, is a Chinese Soil Taxonomy. In a unique man-made hydroponics soil subclass, is internationally recognized as the soil with Chinese characteristics. According to the second national soil survey data, the Chinese water organic carbon content of cultivated soil is dry an average of 137% of cultivated land, paddy soil during the maturation hydroponics organic carbon accumulation is a common trend. In the agricultural soil carbon sequestration potential in paddy soil significantly higher share of the total. Therefore, accurately quantifying SOC stock and describing its spatial distribution in large regional-scale is considered essential to modeling the global carbon cycle and helpful to slow the pace of climate change.
The precision of spatial characterization of soil attribute is commonly affected by the map scale. Spatial patterns of SOC can be captured by assigning soil attribute data to polygons of a digital soil map. However, the specific influences of map scale on SOC estimates remain unclear. And the interpolation method as another possible way of scale transfer from discrete sampling points to regional scale that needs to be considered when making decisions. In this study, effects of soil mapping scale on the estimates of SOC storage (to a depth of 100 cm) in taihu region were discussed based on 1107 soil profiles and soil maps with six scales ranging from 1:50,000 to 1:14,000,000. Moreover, universal kriging (UK), ordinary kriging (OK), pedological professional knowledge-based (PKB) method along with the auxiliary topographic factors extracted from geographic coordinates were applied to predict the spatial patterns of SOC density (to a depth of 100 cm) for changxing county.
Digital soil map of different mapping scale had different influence on the estimation of SOC storage. And SOC density values of the main soil types had fundamental influences on the SOC storage estimates. From the point of view of soil region, the SOC stocks increased at first then fell at 1:1,000,000 soil map, then decreased as the map scale decreased.
From the point of view of soil terrain, in different mapping scales, four soil area in the study area of paddy soil organic carbon storage in the order:polder soil region> alluvial plain soil region> Taihu lake plain soil region>Low mountain and hilly soil region.
Estimates of SOC stock should consider not only the total SOC stock but also the SOC stock at different spatial locations; from this point of view, the 1:50,000 scale soil map has the most detailed spatial information of SOC among the five soil map scales considered in this study, and the PKB method can reflect the spatial variability of SOCD within a map unit to some degree.Therefore, the 1:50,000 soil map combined with the PKB method is probably the best one for SOC stock estimates in Taihu Lake region.
The total SOC storage of the Taihu Lake region was mainly controlled by the hydromorphic paddy soil, the gleyed paddy soil, the degleyed paddy soil and the percolated paddy soil, for the SOC storage of these four paddy soil subtypes accounted for more than 83% of the total SOC storage under each mapping scale. The gleyed paddy soil and the hydromorphic paddy soil had the greatest influence on different mapping scale and their SOC storage under the 1:14,000,000 mapping scale was apparently higher than any other scale. Such influence of soil map scale on estimation of SOC stocks in taihu region is resulted mainly from the map generalization process.
After three methods were applied to predict the spatial patterns of SOC density (to a depth of 100 cm), the results showed that mean absolute error (MAE) and root mean square error (RMSE), is the smallest and 87% of the total variation can be explained by PKB method, it is the best one for predicting the spatial patterns of SOC density in taihu region.The OK method resulted in a lower MAE, RMSE and a wider range of SOC density compared with UK method. Moreover, the SOC density map can reflect not only the differences between the peaks and river valleys, but also the variations among different land use types.
对于土壤数据的空间表征,随着观察尺度变化,其比例尺和精度也在变化。在离散采样点数据有限的情况下,如何对区域尺度水稻土储量和空间分布进行精确表征有待进一步研究。利用土壤图可以实现对水稻土有机碳储量和空间分布的表征,但目前关于制图尺度对水稻土有机碳储量估算的影响还不十分清楚。空间插值技术和土壤类型法均可以实现土壤数据由点到面的尺度转换,但在区域尺度土壤属性由点到面的尺度转换中,哪种方法较好有待进一步探讨。本文以太湖地区水稻土为研究对象,基于“土壤类型GIs连接法”,将全国第二次土壤普查的1107个水稻土剖面数据分别连接到6个不同制图比例尺(1：5万、1：20万、1：50万、1：100万、1：400万、1：1400万)土壤图,建立了6个不同制图尺度的太湖地区水稻土剖面数据库,研究了制图尺度的变化对水稻土有机碳储量的估算和表征影响,并将土壤类型法与GIs技术整合来预测水稻土有机碳的空间分布,从而实现水稻土有机碳数据由点到面的尺度转换。主要结论研究如下：
(1)太湖地区不同制图尺度水稻土(0～100 cm)有机碳储量的估算结果有一定差异。从区域尺度来看,水稻土有机碳储量随制图比例尺的下降,呈现先上升,后下降再上升的变化规律；从不同地形区来看,在不同制图尺度下,四大土区所占研究区水稻土有机碳储量的排序：低洼圩田土区>冲积平原土区>太湖平原土区>低山丘陵土区。
(2)不同制图尺度水稻土有机碳总储量的估算结果主要受潴育型水稻土、潜育型水稻土、脱潜型水稻土和渗育型水稻土控制,这4个水稻土亚类的碳储量占不同制图尺度碳储量的83%以上。但不同制图尺度影响最大的是潜育型水稻土和潴育型水稻土,尤其在1：1400万制图尺度下这两个水稻土亚类的有机碳量明显高于其它尺度,这主要是由于土壤图比例尺减小的土壤图图斑概化过程所导致的。从1：5万到1：20万主要受青泥土图斑概化的影响,从1：50万到1：100万主要受以黄泥土为主的土属图斑概化的影响,而从1：400万到1：1400万水稻土碳储量的变化主要由于脱潜型水稻土、渗育型水稻土、漂洗型水稻土和淹育型水稻土的制图单元均被概化到潴育型水稻土和潜育型水稻土制图单元。
(3)在制图尺度由1：5万降低至1：1400万的过程中,水稻土有机碳储量、密度及面积均有较大变化,图斑概化过程对有机碳储量估算结果影响较大。对有机碳储量的估算,在考虑估算结果的同时也要考虑有机碳的空间分布,1：5万土壤数据库有最为详尽的有机碳属性数据和空间数据,利用“PKB”连接法,与其它5个制图尺度相比,不仅能够充分反应水稻土的有机碳的空间分布,更能够精确的估算水稻土有机碳储量。
(4)利用普通克里格法、泛克里格法、“PKB”法三种方法对太湖地区长兴县水稻土有机碳密度空间分布(0～100 cm)的预测中,“PK_B”法的预测效果最好,对总方差的解释程度为87%,预测结果的平均绝对误差及均方根误差也最小,对有机碳密度的空间分布表达较为细致,能很好的反应该地区丘陵、平原之间的差异及其它地形单元内的局部变异；其次是普通克里格法,泛克里格法的预测结果最不理想,这两种方法只能从宏观上反映水稻土有机碳密度的空间分布,平滑效应强烈。
Soil organic carbon (soil organic carbon, SOC) play an important role in conserving soil fertility, improve soil quality, and changing global climate. SOC stocks and carbon fixation is a global environmental problem which has been given wide attention. Paddy soil is hydroponic natural soil in the artificial aging process of the formation of a special man-made wetland soil, is a Chinese Soil Taxonomy. In a unique man-made hydroponics soil subclass, is internationally recognized as the soil with Chinese characteristics. According to the second national soil survey data, the Chinese water organic carbon content of cultivated soil is dry an average of 137% of cultivated land, paddy soil during the maturation hydroponics organic carbon accumulation is a common trend. In the agricultural soil carbon sequestration potential in paddy soil significantly higher share of the total. Therefore, accurately quantifying SOC stock and describing its spatial distribution in large regional-scale is considered essential to modeling the global carbon cycle and helpful to slow the pace of climate change.
The precision of spatial characterization of soil attribute is commonly affected by the map scale. Spatial patterns of SOC can be captured by assigning soil attribute data to polygons of a digital soil map. However, the specific influences of map scale on SOC estimates remain unclear. And the interpolation method as another possible way of scale transfer from discrete sampling points to regional scale that needs to be considered when making decisions. In this study, effects of soil mapping scale on the estimates of SOC storage (to a depth of 100 cm) in taihu region were discussed based on 1107 soil profiles and soil maps with six scales ranging from 1:50,000 to 1:14,000,000. Moreover, universal kriging (UK), ordinary kriging (OK), pedological professional knowledge-based (PKB) method along with the auxiliary topographic factors extracted from geographic coordinates were applied to predict the spatial patterns of SOC density (to a depth of 100 cm) for changxing county.
Digital soil map of different mapping scale had different influence on the estimation of SOC storage. And SOC density values of the main soil types had fundamental influences on the SOC storage estimates. From the point of view of soil region, the SOC stocks increased at first then fell at 1:1,000,000 soil map, then decreased as the map scale decreased.
From the point of view of soil terrain, in different mapping scales, four soil area in the study area of paddy soil organic carbon storage in the order:polder soil region> alluvial plain soil region> Taihu lake plain soil region>Low mountain and hilly soil region.
Estimates of SOC stock should consider not only the total SOC stock but also the SOC stock at different spatial locations; from this point of view, the 1:50,000 scale soil map has the most detailed spatial information of SOC among the five soil map scales considered in this study, and the PKB method can reflect the spatial variability of SOCD within a map unit to some degree.Therefore, the 1:50,000 soil map combined with the PKB method is probably the best one for SOC stock estimates in Taihu Lake region.
The total SOC storage of the Taihu Lake region was mainly controlled by the hydromorphic paddy soil, the gleyed paddy soil, the degleyed paddy soil and the percolated paddy soil, for the SOC storage of these four paddy soil subtypes accounted for more than 83% of the total SOC storage under each mapping scale. The gleyed paddy soil and the hydromorphic paddy soil had the greatest influence on different mapping scale and their SOC storage under the 1:14,000,000 mapping scale was apparently higher than any other scale. Such influence of soil map scale on estimation of SOC stocks in taihu region is resulted mainly from the map generalization process.
After three methods were applied to predict the spatial patterns of SOC density (to a depth of 100 cm), the results showed that mean absolute error (MAE) and root mean square error (RMSE), is the smallest and 87% of the total variation can be explained by PKB method, it is the best one for predicting the spatial patterns of SOC density in taihu region.The OK method resulted in a lower MAE, RMSE and a wider range of SOC density compared with UK method. Moreover, the SOC density map can reflect not only the differences between the peaks and river valleys, but also the variations among different land use types.
引文
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