National Earth System Science Data Center GLASS Fractional Vegetation Coverage (FVC) Download:
Introduction:
The GLASS FVC product algorithm was based on the machine learning methods using the training samples generated from global distributed high spatial resolution satellite data (Jia et al. 2015). Initially, the GLASS FVC product algorithm for MODIS data was generated using the general regression neural networks (GRNNs) method with training samples data Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) data (Jia et al. 2015). However, in the process of generating the long term global GLASS FVC product, it was found that the computational efficiency of the GRNNs method was not satisfactory. Therefore, four machine learning methods including back-propagation neural networks (BPNNs), GRNNs, support vector regression (SVR), and multivariate adaptive regression splines (MARS), were evaluated. The MARS method was found to be a suitable algorithm for generating the long term GLASS FVC product from MODIS data (Yang et al. 2016).
The GLASS FVC algorithm for AVHRR data was also developed to be in concert with the GLASS MODIS FVC product. It was based on the GLASS MODIS FVC product to achieve continuity of FVC estimates from both AVHRR and MODIS data.
Extensive validation experiments have been conducted using the estimates from high-resolution satellite data and ground measurements (Jia et al. 2016; Jia et al. 2018). The details of the algorithms and the validation results are recently summarized (Jia et al. 2019)
References:
[1]Jia, K., Liang, S., Liu, S.H., Li, Y.W., Xiao, Z.Q., Yao, Y.J., Jiang, B., Zhao, X., Wang, X.X., Xu, S., & Cui, J. (2015). Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 53, 4787-4796
[2]Jia, K., Liang, S., Gu, X., Baret, F., Wei, X., Wang, X., Yao, Y., Yang, L., & Li, Y. (2016). Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data. Remote Sensing of Environment, 177, 184-191
[3]Jia, K., Liang, S.L., Wei, X.Q., Yao, Y.J., Yang, L.Q., Zhang, X.T., & Liu, D.Y. (2018). Validation of Global LAnd Surface Satellite (GLASS) fractional vegetation cover product from MODIS data in an agricultural region. Remote Sensing Letters, 9, 847-856
[4]Jia, K., Yang, L., Liang, S., Xiao, Z., Zhao, X., Yao, Y., Zhang, X., Jiang, B., & Liu, D. (2019). Long-Term Global Land Surface Satellite (GLASS) Fractional Vegetation Cover Product Derived From MODIS and AVHRR Data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, doi:10.1109/jstars.2018.2854293
[5]Yang, L., Jia, K., Liang, S., Liu, J., & Wang, X. (2016). Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data. Remote Sensing, 8, 682