Chlorophyll is the dominant pigment in plant photosynthesis.Leaf chlorophyll content(ChlLeaf)is directly related to the photosynthetic capacity and plays an important role in global carbon cycle modeling and agricultural monitoring.GF-6 satellite is China's first high-spatial-resolution satellite for precision agriculture.The GF-6 Wide Field of View(WFV)camera with a 4-day revisit cycle and 16-meter spatial resolution has two red-edge bands that are sensitive to variations in ChlLeaf and shows great potential for ChlLeaf monitoring at fine temporal-spatial resolution.However,a few studies focusing on vegetation parameter quantitative inversion from GF-6 WFV data and the applicability of GF-6 WFV for ChlLeaf retrieval have yet to be validated.In this study,we proposed a ChlLeaf retrieval algorithm for GF-6 WFV based on Chlorophyll Sensitive Index(CSI)and constructed a CSI-based empirical regression model using the relationship between ChlLeaf and CSI using PROSAIL and PROSPECT+4-scale model simulations.The inversion accuracy of the CSI-based empirical regression model was then compared with other vegetation index-based empirical regression models,such as MTCI,CIre,TCARI/OSAVI.First,the PROSAIL and PROSPECT+4-scale models were used to generate simulated the canopy reflectance of croplands,broadleaf forests,and needleleaf forests,and the canopy reflectance simulations were resampled to GF-6 WFV multispectral reflectance using the spectral response function of GF-6 WFV.Then,CSI derived from simulated GF-6 WFV reflectance was used to construct the CSI-based empirical model for ChlLeaf retrieval via regression analysis.Finally,the accuracy of the CSI-based retrieval model was evaluated using ground-measured ChlLeaf data and the existing MODIS ChlLeaf product.Results showed that CSI was more linearly related to ChlLeaf and less sensitive to LAI variations than MTCI,CIre,and TCARI/OSAVI.CSI achieved improved ChlLcaf retrieval accuracy with R2=0.62 and RMSE=10.31 μg cm-2,higher than CIre(R2=0.34,RMSE=14.83 μg cm-2),MTCI(R2=0.25,RMSE=15.3 μg cm-2),TCARI/OSAVI(R2=0.01 and RMSE=21.34 μg cm-2).Under different LAI and ChlLeaf conditions,the variations of the CSI-based model in RMSE are the lowest,suggesting that CSI offered a more stable approach to retrieving ChlLeaf compared with the other three vegetation indices.A comparison of the GF-6 WFV ChlLeaf time series and the MODIS ChlLeaf product at the Beijing forest site indicated that GF-6 WFV could provide a high spatial resolution ChlLeaf dataset,which can derive information on ChlLeaf variations at a fine temporal-spatial resolution.In conclusion,the GF-6 WFV data have good potential for the accurate retrieval of ChlLeaf at regional scales.The CSI-based GF-6 ChlLeaf can achieve high retrieval accuracy and portray the spatial and time-series variation characteristics of ChlLeaf,which provide the data support and scientific basis for the further research and application of GF-6 WFV in the ecological monitoring of agriculture and vegetation.
维普
万方数据
