基于STFCNN时空融合与VPM模型的农田总初级生产力反演研究OA

Aiming to address the issue of low spatial resolution in MODIS GPP products,which makes it difficult to meet the needs of refined monitoring of ecosystem productivity at the farmland scale,focusing on a maize-growing area in Shouyang County,Shanxi Province,the MODIS and Landsat 8 remote sensing data from the 2014-2015 growing season was used,a supervised-training fully-connected neural neural network(STFCNN)was constructed to achieve high spatiotemporal resolution reconstruction of the enhanced vegetation index(EVI)and surface water index(LSWI).The fused vegetation index was then used to drive a vegetation photosynthesis model(VPM)for GPP inversion.The fusion performance of STFCNN with STARFM,ESTARFM,FSDAF,and STNLFFM models was compared using indicators such as root mean square error(RMSE),coefficient of determination(R2),structural similarity index(SSIM),universal image quality index(UIQI),spectral angle mapping(SAM),and global relative error(ERGAS).Eddy covariance observation data were used to verify the model inversion results.The results showed that STFCNN exhibited the best fusion accuracy in both EVI and LSWI spatiotemporal fusion.The RMSE of EVI fusion was 0.02~0.09,R2 was 0.70~0.95,and UIQI was 0.82~0.98;the RMSE of LSWI fusion was 0.024~0.046,and R2 was 0.54~0.88,which was better than that of other models overall.Spatial detail comparison and scatter analysis showed that STFCNN can effectively preserve farmland boundary and texture structure information.Its fusion results had high consistency with real Landsat images.Pixel-level verification yielded R2 of 0.985 and RMSE of 0.003,showing the best spectral reconstruction ability and spatial detail recovery ability.Based on the fusion image-driven VPM model,the correlation of the inverted GPP with the measured GPP was increased from 0.168 2 in the MODIS GPP product to 0.345 2,which significantly enhanced the response to changes in photosynthesis during the peak vegetation growth period and more accurately characterized the spatiotemporal changes of farmland GPP.The GPP of Shouyang County in 2014-2015 generally showed a spatial distribution pattern of"high in the northwest and low in the southeast",with an average GPP range of 585~827gC/m2.The result showed that STFCNN can fully integrate the high temporal resolution advantage of MODIS data and the high spatial resolution advantage of Landsat data,and can provide high spatiotemporal resolution data support for regional agricultural ecosystem productivity monitoring and carbon cycle research.