Synergistic Use of GaoFen-2 and UAV-Derived Data for Mapping Leaf Area Index

Grassland degradation is a serious ecological and economical problem in northern grassland of China. Leaf area index (LAI), the key indicators of grassland vegetation growth, is important access points for studying the problem. Satellite remote sensing technology is useful for LAI dynamic monitoring over a wide range of grasslands. However, due to the scale mismatch between satellite observation and ground survey, significant uncertainties and biases exist in mapping grassland LAI from satellite data. The rapid development of unmanned aerial vehicle (UAV) technology offers the possibility of solving this problem.In this paper, we developed a method of UAV and satellite synergies for estimating grassland LAI, which filled the gap between satellite and ground surveys and successfully mapped LAI in northern grassland of China. First, based on the simulations generated by PROSAIL and ground survey data, the grassland LAI was estimated from UAV hyperspectral data and aggregated as satellite-scale sample set (LAI
up
). LAI
up
would be used for modeling of satellite-based LAI estimation. Then, the scale effect between UAV data and Chinese GaoFen-2 multi-spectral data was analyzed and spatial information was extracted. The spatial information would be incorporated into the LAI retrieval from satellite data to minimize the scale bias. Finally, the grassland LAI of the entire experimental area was mapped and analyzed.