Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance

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Detection and attribution of vegetation greening trend in China over the last 30 years (2015, methodology): “(a), GIMMS LAI dataset; (b) GLOBMAP LAI dataset; (c) GLASS LAI dataset.”
Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016 (2018, methodology): “Therefore, we will further analyze VIIRS products using the intercomparison with other global moderate resolution LAI/FPAR products such as GEOV1 [5], GLASS [6] and so on.”
Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources (2020, methodology): “…staple crops (i.e. rice, wheat and maize) in China from 2000 to 2015 using the remotely sensed Global Land Surface Satellite (GLASS) LAI product (Xiao et al 2014); (2) to evaluate the accuracy of the crop phenology-based approach in mapping crop harvesting areas by comparing the obtained…”
Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance (2016, methodology): “For each BELMANIP site, 3 × 3 pixels of the preprocessed AVHRR reflectance data and the MOD15 and CYCLOPES LAI values projected into the 3 × 3 pixels of the preprocessed AVHRR reflectance data using the nearest-neighbor sampling technique were extracted.”