Monitoring Leaf Area Index Of Wheat Under Straw Returning Field Based On Fusion Of Planet And Sentinel-2 Images

Leaf area index（LAI）is an essential index of wheat population growth.The traditional method of LAI acquisition is time-consuming and labor-consuming,while satellite remote sensing can achieve large-scale monitoring of wheat LAI.Uneven growth of crops and fragmentations of fields in developing countries increase the mixed pixels in satellite images.At the same time,the red edge information can alleviate the saturation problem caused by the soil background and high LAI in the early stage of crop growth.With the popularization and application of straw returning to the field in the rice-wheat-rotation area,during the growth of wheat,the background has changed from the unique soil background to the "soil-straw" mixed background.However,there is a lack of researches on reducing the influence of mixed background and improving the accuracy of LAI estimation.The slope of the traditional red edge region often focuses on the expression of red edge feature of vegetation spectrum curve when monitoring LAI,without considering the influence of background on the feature itself.This study used Sentinel-2 and Planet image,together with wheat LAI and the spectrum of background acquired from wheat fields,to carry out the study about image fusion and elimination of "straw-soil" background:In order to solve the problem of lacking high-resolution red edge satellite products,this study applies weighted-and-unmixing and super-resolution for multispectral Multiresolution Estimation（SupReME）,named Wu-SupReME.A high-resolution RE product was generated by fusing Sentinel-2 spectral advantage and Planet spatial advantage.The resultant fused image is highly correlated（R2>0.98）with the Sentinel2 image and clearly illustrates the persistent advantages of such products.This fused image was significantly more accurate than the originals when used to predict heterogeneous wheat LAI and,therefore,clearly illustrated the persistence of Sentinel2 spectral and Planet spatial advantage.This study provided method reference for multisource data fusion and image products for accurate parameter inversion in quantitative remote sensing of vegetation.In order to improve the LAI estimation accuracy,which is affected by the mixed"soil-straw" background,this study used the fused images between sowing and emergence to obtain the spectral curve of the "straw-soil" background.The spectral information of wheat canopy obtained by using the fusion image at each growth stage.To remove the influence of the "straw-soil" background,we calculated the slope difference for increment caused by wheat.Based on traditional VIs,using slope difference to optimize VIs can further reduce the influence of mixed background and improve the accuracy of LAI estimation.The results showed that the optimized slope and VIs have higher accuracy than that before optimization.The optimized slope△Kwheat NIR-RE2（R2=0.79,RRMSE=19.68%）and MEIV2（R2=0.80,RRMSE=19.25%）have the best performance separately.The comparison using MEVI2 before and after optimization to draw the LAI distribution map showed that the optimized MEIV2 not only reduces the influence of mixed background but also alleviates the saturation problem of red edge region slope with the increasing LAI.This study proposed a method to calculate slope difference and optimize VIs by using the spectral curves of two objects,including the "wheat-straw-soil" spectral curve and "straw-soil" spectral curve,to remove the influence of "straw-soil" mixture background and improve the accuracy of LAI estimation.This study provides a reference for multi-object spectral information to improve vegetation monitoring.