A new continuous fusion method of remote sensing data

4th International Conference on Water Resource and Environment

地球科学;生态环境科学

Yao, X.L.^1,2 ; Sun, S.Y.^1 ; Li, X.J.^2 ; Liu, R.^1

China Science Map-universe Technology Co. Ltd, Jia 11 Anxiangbeili, Beijing

100875, China^1

College of Resource Environment and Tourism, Capital Normal University, No. 105 Xisanhuan North Road, Beijing

100048, China^2

关键词: Agricultural drought;    Cumulative distribution;    Cumulative probability distribution curves;    Fusion algorithms;    Lagrange interpolations;    Remote sensing data;    Remote sensing fusion;    Research interests;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/191/1/012130/pdf
DOI  :  10.1088/1755-1315/191/1/012130

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Remote sensing soil moisture is one of the important indexes for monitoring agricultural drought in large scale farmland area. The time series length and update speed of remote sensing data have been the important factors restricting their application. Because of the difference between the sensor and the inversion method, remote sensing data from different sources cannot be directly compared and analysed. Therefore, data fusion becomes a hotspot of research interest and key issue in the application of remote sensing data nowadays. Based on cumulative distribution matching principle, Lagrange interpolation can establish this correlation between any quantile on different cumulative probability distribution curves. Based on the above, a continuous fusion algorithm of multi-source remote sensing soil moisture is built in this study. Using this new fusion method, SMOS and CCI data are fused to real-time remote sensing soil moisture data product with long time series. The verification application result in Songnen plain indicates that this Lagrange interpolation continuous fusion method can improve the fusion accuracy of multi-source remote sensing soil moisture significantly. The low-value region of the cumulative probability distribution curve is the key data segment to characterize agricultural drought. According to this continuous fusion method, fused SMOS and CCI are almost completely coincident at each quantile in the low-value region of the curve. This remote sensing fusion data combining the advantages of CCI and SMOS provides reliable data support for the next study of agricultural drought evaluation.