为进一步研究适用于风云三号(FY-3)土壤水分遥感产品的降尺度方法,分别将基于不规则三角形特征空间的Chauhan模型与Piles模型等土壤水分降尺度方法应用于低分辨率风云三号B星(FY-3B)土壤水分产品,得到高分辨率土壤水分,结合地面观测数据...为进一步研究适用于风云三号(FY-3)土壤水分遥感产品的降尺度方法,分别将基于不规则三角形特征空间的Chauhan模型与Piles模型等土壤水分降尺度方法应用于低分辨率风云三号B星(FY-3B)土壤水分产品,得到高分辨率土壤水分,结合地面观测数据,对不同降尺度方法进行对比分析。结果表明,不同降尺度方法后土壤水分与FY-3B土壤水分空间分布一致,其中,Chauhan模型降尺度后土壤水分与地面观测值相关性最好,均方根误差RMSE低于0.08 cm 3·cm-1,FY-3B土壤水分产品本身的精度以及降尺度模型是影响降尺度结果的重要因素。展开更多
In this paper, we mainly pay attention to the weighted sampling and reconstruction algorithm in lattice-invariant signal spaces. We give the reconstruction formula in lattice-invariant signal spaces, which is a genera...In this paper, we mainly pay attention to the weighted sampling and reconstruction algorithm in lattice-invariant signal spaces. We give the reconstruction formula in lattice-invariant signal spaces, which is a generalization of former results in shift-invariant signal spaces. That is, we generalize and improve Aldroubi, Groechenig and Chen＇s results, respectively. So we obtain a general reconstruction algorithm in lattice-invariant signal spaces, which the signal spaces is sufficiently large to accommodate a large number of possible models. They are maybe useful for signal processing and communication theory.展开更多
文摘为进一步研究适用于风云三号(FY-3)土壤水分遥感产品的降尺度方法,分别将基于不规则三角形特征空间的Chauhan模型与Piles模型等土壤水分降尺度方法应用于低分辨率风云三号B星(FY-3B)土壤水分产品,得到高分辨率土壤水分,结合地面观测数据,对不同降尺度方法进行对比分析。结果表明,不同降尺度方法后土壤水分与FY-3B土壤水分空间分布一致,其中,Chauhan模型降尺度后土壤水分与地面观测值相关性最好,均方根误差RMSE低于0.08 cm 3·cm-1,FY-3B土壤水分产品本身的精度以及降尺度模型是影响降尺度结果的重要因素。
文摘In this paper, we mainly pay attention to the weighted sampling and reconstruction algorithm in lattice-invariant signal spaces. We give the reconstruction formula in lattice-invariant signal spaces, which is a generalization of former results in shift-invariant signal spaces. That is, we generalize and improve Aldroubi, Groechenig and Chen＇s results, respectively. So we obtain a general reconstruction algorithm in lattice-invariant signal spaces, which the signal spaces is sufficiently large to accommodate a large number of possible models. They are maybe useful for signal processing and communication theory.
