Estimation of winter wheat leaf area index by fusing different spatial and temporal resolution remote sensing data

Leaf area index (LAI) with high spatial and temporal resolutions can reflect the dynamic change of crop growth, and be served as a key parameter for crop growth evaluation and yield prediction. By combining the techniques of linear pixel unmixing and data assimilation, the LAI based on SPOT-5 image with high spatial resolution was used to adjust the time-series LAI based on HJ-CCD image with high temporal resolution, and LAI series covering the whole winter wheat growth period and with high spatial and temporal resolutions were generated. The effects of pixel purity and the number of high spatial image on the performance of fusing method were analyzed by comparing the LAI with fusing method and LAI from SPOT-5 image or observed LAI. The results showed that the estimated LAI with fusing method has high consistency with observed LAI and the pixel purity is main obstacle factor. The fusion results based on two scenes of SPOT-5 images are better than that based on one image. These results can provide an important technical support for monitoring of growth in winter wheat.