Abstract
Conference Title: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium Conference Start Date: 2018, July 22 Conference End Date: 2018, July 27 Conference Location: Valencia, Spain As an important land surface parameter for crop growth model, leaf area index (LAI) is desired to be estimated accurately on regional scale. Before applying LAI estimation model built at local scale to the regional scale, the mismatch between multi-scale sensors should be corrected. We firstly applied the kernel-driven BRDF model to describe the two-dimensional reflection characteristics of Landsat 5-TM data based on the kernel weights from MODIS MCD43A1 products and the angle vectors obtained from TM Collection 1 Level-1 dataset. Then the point spread function (PSF) was used to simulate the spatial response of the sensor. Results show that there is a good agreement between TM observations and corrected MODIS values (Red band: $\mathrm{R}^{2}= 0.736,\ \text{RMSE}=2.65\mathrm{e}-4$ , Near-infrared band: $\mathrm{R}^{2}=0.539, \text{RMSE}=5.39\mathrm{e}-4$ ). If we apply the LAI estimation model that has been validated at local scale (TM) directly to the data at large scale (MODIS) without any correction, more than 50% uncertainty of LAI estimation would be introduced. This study implies that the estimation of LAI in winter wheat could be significantly improved by correcting the differences between multi-scale sensors.