Open AccessJournal Article
Spatial-temporal Variations of Net Primary Productivity of Sichuan Vegetation Based on CASA Model
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TL;DR: Li et al. as discussed by the authors used the data of MODIS to modify the three methods of photosynthetically active radiation (PAR)extraction, regional temperature inversion and water stress coefficient calculation based on CASA model and the results were applied to estimate seasonal dynamics and spatial variations of NPP of Sichuan vegetation from 2000 to 2011.
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Abstract: 【Objective】The aim of this study was to estimate the net primary productivity(NPP) and spatial-temporal variations of Sichuan vegetations and to provide data support for better understanding of vegetation productivity of this region.【Method】The data of MODIS was used to modify the three methods of photosynthetically active radiation(PAR)extraction,regional temperature inversion and water stress coefficient calculation based on CASA model and the results were applied to estimate seasonal dynamics and spatial variations of NPP of Sichuan vegetation from 2000 to 2011.【Results】① The total NPP of Sichuan vegetations varied from 285 to 340gC/(m2·a),averaging about 303.27gC/(m2·a).② There was clear seasonal changes in NPP of Sichuan and summer was main accumulation stage for NPP.Autumn NPP were increased year by year due to warming-extended phenology.③ The spatial distribution of NPP in Sichuan showed a rising tendency from northwest to southeast and elevational differenced in NPP was pronounced.④ NPP in the study was mainly driven by precipitation.In addition,average temperature and PAR had significant effects(P0.001).【Conclusion】Changes in NPP of Sichuan vegetation were caused mainly by growing season in resent 10 years.The spatial-temporal variations of NPP generally increased with increasing temperature,moisture,PAR,and elevation.It was relatively reliable to use the modified CASA model to estimate vegetation NPP.
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A Factor Analysis Backpropagation Neural Network Model for Vegetation Net Primary Productivity Time Series Estimation in Western Sichuan
TL;DR: Li et al. as discussed by the authors adopted Factor Analysis Backpropagation neural network model (FA-BP model) to acquire a high-accuracy and high-reliability NPP result without missing or empty areas by using a series of easily accessible datasets, such as meteorological data and remote sensing data.