冬小麦叶面积指数(leaf area index,LAI)是进行作物长势判断和产量估测的重要农学指标之一,高光谱遥感技术为大面积、快速监测植被LAI提供了有效途径。在探讨利用最小二乘支持向量机(least squares support vector machines,LS-SVM)方...冬小麦叶面积指数(leaf area index,LAI)是进行作物长势判断和产量估测的重要农学指标之一,高光谱遥感技术为大面积、快速监测植被LAI提供了有效途径。在探讨利用最小二乘支持向量机(least squares support vector machines,LS-SVM)方法和高光谱数据对不同条件下冬小麦LAI的估算能力。在用主成分分析法(principal component analysis,PCA)对PHI航空数据降维的基础上,利用实测LAI数据和高光谱反射率数据,构建LS-SVM模型,采用独立变量法,分别估算不同株型品种、不同生育时期、不同氮素和水分处理条件下的冬小麦LAI,并与传统NDVI模型反演结果对比。结果显示,每种条件下的LS-SVM模型都具有比NDVI模型更高的决定系数和更低的均方根误差值,即反演精度高于相应的NDVI模型。NDVI模型对不同株型品种、不同氮素和水分条件下冬小麦LAI估算精度不稳定,LS-SVM则表现出较好的稳定性。表明LS-SVM方法利用高光谱反射率数据对于不同条件下的冬小麦LAI反演具有良好的学习能力和普适性。展开更多
The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and moni...The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.展开更多
文摘冬小麦叶面积指数(leaf area index,LAI)是进行作物长势判断和产量估测的重要农学指标之一,高光谱遥感技术为大面积、快速监测植被LAI提供了有效途径。在探讨利用最小二乘支持向量机(least squares support vector machines,LS-SVM)方法和高光谱数据对不同条件下冬小麦LAI的估算能力。在用主成分分析法(principal component analysis,PCA)对PHI航空数据降维的基础上,利用实测LAI数据和高光谱反射率数据,构建LS-SVM模型,采用独立变量法,分别估算不同株型品种、不同生育时期、不同氮素和水分处理条件下的冬小麦LAI,并与传统NDVI模型反演结果对比。结果显示,每种条件下的LS-SVM模型都具有比NDVI模型更高的决定系数和更低的均方根误差值,即反演精度高于相应的NDVI模型。NDVI模型对不同株型品种、不同氮素和水分条件下冬小麦LAI估算精度不稳定,LS-SVM则表现出较好的稳定性。表明LS-SVM方法利用高光谱反射率数据对于不同条件下的冬小麦LAI反演具有良好的学习能力和普适性。
基金supported by the Big Earth Data Science Engineering Program of the Chinese Academy of Sciences Strategic Priority Research Program(XDA19090000 and XDA19030000)。
文摘The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.
