In order to improve the precision of soil organic carbon (SOC) estimates, the sources of uncertainty in soil organic carbon density (SOCD) estimates and SOC stocks were examined using 363 soil profiles in Hebei Provin...In order to improve the precision of soil organic carbon (SOC) estimates, the sources of uncertainty in soil organic carbon density (SOCD) estimates and SOC stocks were examined using 363 soil profiles in Hebei Province, China, with three methods: the soil profile statistics (SPS), GIS-based soil type (GST), and kriging interpolation (KI). The GST method, utilizing both pedological professional knowledge and GIS technology, was considered the most accurate method of the three estimations, with SOCD estimates for SPS 10% lower and KI 10% higher. The SOCD range for GST was 84% wider than KI as KI smoothing effect narrowed the SOCD range. Nevertheless, the coefficient of variation for SOCD with KI (41.7%) was less than GST and SPS. Comparing SOCD’s lower estimates for SPS versus GST, the major sources of uncertainty were the conflicting area of proportional relations. Meanwhile, the fewer number of soil profiles and the necessity of using the smoothing effect with KI were its sources of uncertainty. Moreover, for local detailed variations of SOCD, GST was more advantageous in reflecting the distribution pattern than KI.展开更多
Soil organic carbon(SOC) is an important component of farming systems and global carbon cycle. Accurately estimating SOC stock is of great importance for assessing soil productivity and modeling global climate change....Soil organic carbon(SOC) is an important component of farming systems and global carbon cycle. Accurately estimating SOC stock is of great importance for assessing soil productivity and modeling global climate change. A newly built 1:50 000 soil database of Zhejiang Province containing 2 154 geo-referenced soil profiles and a pedological professional knowledge-based(PKB) method were used to estimate SOC stock up to a depth of 100 cm for the Province. The spatial patterns of SOC stocks stratified by soil types,watershed(buffer analysis), topographical factors, and land use types were identified. Results showed that the soils in Zhejiang covered an area of 100 740 km2 with a total SOC stock of 831.49 × 106 t and a mean SOC density of 8.25 kg m-2, excluding water and urban areas. In terms of soil types, red soils had the highest SOC stock(259.10 × 106t), whereas mountain meadow soils contained the lowest(0.15 × 106t). In terms of SOC densities, the lowest value(5.11 kg m-2) was found in skel soils, whereas the highest value(45.30 kg m-2) was observed in mountain meadow soils. Yellow soils, as a dominant soil group, determined the SOC densities of different buffer zones in Qiantang River watershed because of their large area percentage and wide variation of SOC density values.The area percentages of various soil groups significantly varied with increasing elevation or slope when overlaid with digital elevation model data, thus influencing the SOC densities. The highest SOC density was observed under grassland, whereas the lowest SOC density was identified under unutilized land. The map of SOC density(0–100 cm depth) and the spatial patterns of SOC stocks in the Province would be helpful for relevant agencies and communities in Zhejiang Province, China.展开更多
准确预测未采样区域SOC密度,是研究SOC演变趋势和探索土壤固碳作用对缓解全球气候变化的基础。采用泛克里格法(Universal Kriging,UK)和土壤类型法(pedological professional knowledge-based method,PKB),分别对长兴县水稻土有机碳密...准确预测未采样区域SOC密度,是研究SOC演变趋势和探索土壤固碳作用对缓解全球气候变化的基础。采用泛克里格法(Universal Kriging,UK)和土壤类型法(pedological professional knowledge-based method,PKB),分别对长兴县水稻土有机碳密度进行了预测,其中,UK直接以长兴水稻土剖面资料为源数据、PKB以长兴水稻土剖面数据和长兴1∶5万数字土壤图为源数据进行预测。根据平均绝对误差(MAE)及均方根误差(RMSE)大小,评价了两种方法在县域尺度土壤有机碳密度空间预测效果。结果表明:UK的MAE(31.2)、RMSE(52.5)均大于PKB的MAE(24.7)、RMSE(43.1),说明PKB法的预测效果较好,UK法相对较差。研究表明,对土壤类型、土壤母质,以及剖面点位置等信息的综合考虑能使PKB法更好地表达土壤属性的空间特征,也更适于县域尺度土壤有机碳密度的空间预测。展开更多
基金Project supported by the Knowledge Innovation Project in Leading Edge Fields, Chinese Academy of Sciences(No. ISSASIP0201), the National Key Basic Research Support Foundation of China (No. G1999011810) and the KnowledgeInnovation Project in Resource and
文摘In order to improve the precision of soil organic carbon (SOC) estimates, the sources of uncertainty in soil organic carbon density (SOCD) estimates and SOC stocks were examined using 363 soil profiles in Hebei Province, China, with three methods: the soil profile statistics (SPS), GIS-based soil type (GST), and kriging interpolation (KI). The GST method, utilizing both pedological professional knowledge and GIS technology, was considered the most accurate method of the three estimations, with SOCD estimates for SPS 10% lower and KI 10% higher. The SOCD range for GST was 84% wider than KI as KI smoothing effect narrowed the SOCD range. Nevertheless, the coefficient of variation for SOCD with KI (41.7%) was less than GST and SPS. Comparing SOCD’s lower estimates for SPS versus GST, the major sources of uncertainty were the conflicting area of proportional relations. Meanwhile, the fewer number of soil profiles and the necessity of using the smoothing effect with KI were its sources of uncertainty. Moreover, for local detailed variations of SOCD, GST was more advantageous in reflecting the distribution pattern than KI.
基金supported by the National Natural Science Foundation of China(No.30771253)the Key Project of Science Technology Department of Zhejiang Province,China(No.2006C22026)
文摘Soil organic carbon(SOC) is an important component of farming systems and global carbon cycle. Accurately estimating SOC stock is of great importance for assessing soil productivity and modeling global climate change. A newly built 1:50 000 soil database of Zhejiang Province containing 2 154 geo-referenced soil profiles and a pedological professional knowledge-based(PKB) method were used to estimate SOC stock up to a depth of 100 cm for the Province. The spatial patterns of SOC stocks stratified by soil types,watershed(buffer analysis), topographical factors, and land use types were identified. Results showed that the soils in Zhejiang covered an area of 100 740 km2 with a total SOC stock of 831.49 × 106 t and a mean SOC density of 8.25 kg m-2, excluding water and urban areas. In terms of soil types, red soils had the highest SOC stock(259.10 × 106t), whereas mountain meadow soils contained the lowest(0.15 × 106t). In terms of SOC densities, the lowest value(5.11 kg m-2) was found in skel soils, whereas the highest value(45.30 kg m-2) was observed in mountain meadow soils. Yellow soils, as a dominant soil group, determined the SOC densities of different buffer zones in Qiantang River watershed because of their large area percentage and wide variation of SOC density values.The area percentages of various soil groups significantly varied with increasing elevation or slope when overlaid with digital elevation model data, thus influencing the SOC densities. The highest SOC density was observed under grassland, whereas the lowest SOC density was identified under unutilized land. The map of SOC density(0–100 cm depth) and the spatial patterns of SOC stocks in the Province would be helpful for relevant agencies and communities in Zhejiang Province, China.
文摘准确预测未采样区域SOC密度,是研究SOC演变趋势和探索土壤固碳作用对缓解全球气候变化的基础。采用泛克里格法(Universal Kriging,UK)和土壤类型法(pedological professional knowledge-based method,PKB),分别对长兴县水稻土有机碳密度进行了预测,其中,UK直接以长兴水稻土剖面资料为源数据、PKB以长兴水稻土剖面数据和长兴1∶5万数字土壤图为源数据进行预测。根据平均绝对误差(MAE)及均方根误差(RMSE)大小,评价了两种方法在县域尺度土壤有机碳密度空间预测效果。结果表明:UK的MAE(31.2)、RMSE(52.5)均大于PKB的MAE(24.7)、RMSE(43.1),说明PKB法的预测效果较好,UK法相对较差。研究表明,对土壤类型、土壤母质,以及剖面点位置等信息的综合考虑能使PKB法更好地表达土壤属性的空间特征,也更适于县域尺度土壤有机碳密度的空间预测。
