Study On Fusion Method Of High Spatial-temporal Resolution Leaf Area Index Products Using STARFM Model

Compared with manual detection or measurement methods,remote sensing technology has the advantages of objectivity,real-area coverage,and large range.It has played an irreplaceable role in forestry measurement applications such as discrimination of vegetation types,monitoring growth,prevention of diseases and insect pests.Leaf area index(LAI)can be obtained through the inversion of spectra and various vegetation indexes(VI)detected by remote sensing sensors.As the study of carbon cycle of the terrestrial ecosystem and the analysis of global climate change require the accuracy of remote sensing LAI products,especially in the forest area,the LAI parameter has a particularly important influence on the highly accurate carbon cycle simulation results.How to effectively improve the accuracy of the remote sensing LAI product in the forest area has become an important scientific problem in the quantitative vegetation remote sensing research.However,due to the limitation of the sensor itself or the influence of weather,it is often impossible to obtain LAI products with both high spatial and temporal resolution at large scale.In this study,the Global LAnd Surface Satellite(GLASS)LAI product will be used,which has a time resolution is 8d,but the spatial resolution of 8d and a low spatial resolution of 1km.Therefore,fusion technology is used to improve its spatial resolution.The study area is located in the northwestern part of the Inner Mongolia Autonomous Region.First,the back propagation(BP)neural network model of Simulated Annealing(SA)was used to estimate the forest stand LAI of Landsat8 OLI and GF-1WFV data.And then,the spatio temporal adaptive reflection Rate fusion model(Spatio Temporal Adaptive Reflectance Fusion Model,STARFM),was applied to fuse these two LAI results were fused with GLASS LAI products,during the growth season from 2013 to 2017(May to October),with a temporal resolution of 8d and spatial resolution of 30m.After the fusion,the 30m LAI product has a higher similarity with the 30m LAI retrieved by the airborne Lidar based on the path length distribution model,and has a good correlation with the measured LAI verification(R~2=0.79);Meanwhile,the fused LAI products kept the similar inter annual trends with the GLASS LAI products.The results show that:(1)The fused LAI products are true and reliable,and the temporal and spatial resolution has been improved,which provides a reference for future research to improve the temporal and spatial resolution of LAI products;(2)This study uses Landsat 8 OLI,GF-1WFV multi-source sensor image ground reflectivity data complement each other to estimate LAI,expand the scope of data selection,and provide theoretical and technical reference for the future domestic high-scoring data to carry out LAI spatiotemporal fusion research;(3)Improve the fusion process in the research to obtain high LAI products with temporal and spatial resolution can play an important role in exploring the primary productivity of vegetation in the future,and then studying changes in the carbon cycle,and can provide a reference for the application of the STARFM algorithm in different scenarios.