National Earth System Science Data Center GLASS Leaf area index (LAI) Download:
Introduction:
The GLASS LAI product from MODIS surface-reflectance time-series data is based on general regression neural networks (GRNNs) (Xiao et al. 2014). Unlike existing neural network methods that use only remote-sensing data acquired at a specific time to retrieve LAI, the preprocessed MODIS reflectance data (Tang et al. 2013) for one-year were inputed into the GRNNs to estimate the one-year LAI profiles. This method was applied to generate LAI product from MODIS surface-reflectance data.The similar algorithm has also been applied to generate the LAI product from the Long-Term Data Record (LTDR) AVHRR surface-reflectance data (Xiao et al. 2016). Because of inadequate information available for atmospheric correction, the AVHRR surface reflectance data is highly noisy. An innovative method has been developed to produce the high-quality surface reflectance NDVI products(Xiao et al. 2017b; Xiao et al. 2015b). Compared to other long-term LAI products, the GLASS LAI products show higher quality and accuracy (Xiao et al. 2017a; Xu et al. 2018).
References:
[1]Xiao, Z.Q., Liang, S., Wang, J.D., Chen, P., Yin, X.J., Zhang, L.Q., & Song, J.L. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223
[2]Tang, H., Yu, K., Hagolle, O., Jiang, K., Geng, X., & Zhao, Y. (2013). A Cloud Detection Method Based on Time Series of MODIS Surface Reflectance Images. International Journal of Digital Earth, DOI: 10.1080/17538947.17532013.17833313
[3]Xiao, Z., Liang, S., Wang, J., Xiang, Y., Zhao, X., & Song, J. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318
[4]Xiao, Z., Liang, S., Tian, X., Jia, K., Yao, Y., & Jiang, B. (2017b). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 5551-5568
[5]Xiao, Z., Liang, S., Wang, T., & Liu, Q. (2015b). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices. Remote Sensing, 7, 9844-9864
[6]Xiao, Z., Liang, S., & Jiang, B. (2017a). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230
[7]Xu, B., Li, J., Park, T., Liu, Q., Zeng, Y., Yin, G., Zhao, J., Fan, W., Yang, L., & Knyazikhin, Y. (2018). An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment, 209, 134-151