Integrating MODIS and Landsat imagery to monitor the small water area variations of reservoirs

Science of Remote Sensing

Xiaofeng Jia¹  Yun Du¹  Xinyan Li²  Zhixiang Yin²  Feng Ling² 

[1] University of Chinese Academy of Sciences, Beijing, 100049, China;Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China;

关键词: Reservoir surface water area;    Spatio-temporal monitoring;    MODIS;    Landsat;    C/M ratio model;   

DOI  :  

来源: DOAJ

【 摘 要 】

Monitoring the spatio-temporal dynamic changes of reservoir surface water area (RSWA) values is crucial when investigating global water resource reserves. Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed imagery has been extensively used for this purpose, but the coarse spatial resolution limits the accuracy when monitoring the small water area variations of reservoirs. In this paper, we introduce a novel approach for the monitoring of RSWA variation by integrating MODIS and Landsat imagery to overcome this problem. In the proposed method, the sub-pixel water fraction information in the MODIS images is explored based on the C/M ratio model, where C/M represents the ratio between the reflectance of a land pixel and that of a water pixel. By using the series of RSWA values extracted from Landsat images as the reference, the C/M values extracted from MODIS images are used to construct regression functions with the overall RSWA for each MODIS pixel. An RSWA time series is then predicted from the extracted daily C/M values with the use of the regression functions constructed in the study. In a case study of Dale Hollow Reservoir in the United States, the performance of the proposed method was assessed, and the resulting Pearson's correlation coefficient between the predicted RSWA and water level (WL) reached 0.88, which was higher than that of the conventional pixel analysis based method and the spectral unmixing based sub-pixel analysis method, confirming the effectiveness of the proposed method.

【 授权许可】

Unknown