An Orthogonal Fisher Transformation-Based Unmixing Method Toward Estimating Fractional Vegetation Cover in Semiarid Areas

Remote estimation of fractional vegetation cover (FVC) in arid and semiarid areas is crucial for understanding their roles in global climate changes and maintaining their ecological sustainability. Among the existing algorithms for remote estimation of FVC, the linear spectral mixture analysis (LSMA) has been widely adopted owing to its simplicity and flexibility. However, the spectral variability of endmembers is still a big challenge that would largely decrease the estimation accuracy of LSMA. In this letter, we proposed a novel unmixing algorithm by integrating an orthogonal Fisher transformation into the LSMA (fLSMA). Two evaluation experiments were conducted: one was based on simulations; the other was based on a field survey in Xilingol grassland, China. The proposed fLSMA yielded remarkably higher accuracies and precisions than the conventional LSMA (cLSMA), weighted SMA (wSMA) in the first experiment. In the second experiment, a root-mean-square error (RMSE) of 0.11 was derived for the fLSMA, compared with the RMSE values larger than 0.36 for the cLSMA and wSMA. Although the performance of fLSMA was somehow similar to the multiple endmember SMA (MESMA) in the two evaluation experiments, the fLSMA was much less time-consuming than the MESMA in massive computations. The results indicate the potential of the proposed fLSMA in long-term monitoring of FVC in semiarid areas based on satellite observations.