Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach

Leaf chlorophyll content (Chl(Leaf)), which is responsible for light harvesting for photosynthesis, is an important parameter for carbon cycle modeling and agriculture monitoring at regional and global scales. Since the spectral signals of chlorophyll content and leaf area are highly coupled, it is required to remove the effect of the LAI on the retrieval of Chl(Leaf) from satellite data. In this paper, an approach for the retrieval of Chl(Leaf) was proposed. A 2-dimensional matrix-based relationship between Chl(Leaf) and two VIs was established using simulated datasets from the PROSAIL model. The matrix was formed by dividing the two-VI space into m x n cells and assigning the Chl(Leaf) value to each cell. Based on the matrix, the Chl(Leaf) can be retrieved using the two VIs from observations. Three matrices of different VI pairs for retrieving Chl(Leaf) were tested using the PROSAIL simulated data. The results show that the matrix formed with two new VIs, RERI[705] and RERI[783], works best. The results from the matrices of two VIs are better than those from individual VIs as well as from VI ratios. The matrices were successfully used to retrieve the Chl(Leaf) of winter wheat from Sentinel-2 images. The Chl(Leaf) estimations using the RERI[705]-RERI[783] matrix achieves an accuracy of R-2 = 0.70, RMSE = 10.4 mu g/cm(2), and NRMSE = 11.9%. The estimations using the TCARI-OSAVI and the R-740/R-705-R-865/R-665 matrices are also in good agreement with the measured Chl(Leaf) (R-2 > 0.48, RMSE < 13.1 mu g/cm(2), and NRMSE < 15.1%). The matrix-based VI combination approach has the potential for the operational retrieval of Chl(Leaf) from multispectral satellite data.