Retrieval and validation of leaf chlorophyll content using GF-6 WFV Imageries
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.
万方数据
维普
