Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012–2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, re derived: (1) a narrow-band NDVI (NDVI680,800, derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO2 flux, and NDVI values re compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tor flux data. All three NDVIs provided good estimates of dry green biomass and re able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods re considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods re also calibrated against fAPARgreen (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APARgreen (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships beten fluxes and optical measurements, suggesting a slight change in efficiency beten the first and second half of the growing season. Consequently, the best results re obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APARgreen rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations beten these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tor to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems.
['Enrica Nestola', 'Carlo Calfapietra', 'Craig A. Emmerton', 'Christopher Yee Wong', 'Donnette R. Thayer', 'John A. Gamon']
I am writing a research paper on a idea Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements
[{"from": "human", "value": "I am writing a research paper on a idea Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012\u20132013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, re derived: (1) a narrow-band NDVI (NDVI680,800, derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO2 flux, and NDVI values re compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tor flux data. All three NDVIs provided good estimates of dry green biomass and re able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods re considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods re also calibrated against fAPARgreen (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APARgreen (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships beten fluxes and optical measurements, suggesting a slight change in efficiency beten the first and second half of the growing season. Consequently, the best results re obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APARgreen rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations beten these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tor to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems."}]