Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

Abstract

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, were derived: (1) a narrow-band NDVI (NDVI 680,800 , 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 CO 2 flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were 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 were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against f APAR green (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APAR green (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 between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were 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 APAR green rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between 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 tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems.

Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

“…Different viewing and solar zenith angles, in this case, would result from directly comparing data captured at different times of day, such that shadows and effects of surface anisotropy would affect the angular dependent signal due to the geometric configuration of the sun, sensor and target [40]. Independent field measurements [31] have shown a clear diurnal pattern for NDVI for prairie grassland, and this could have partly explained these differences between the two MODIS sensors having different overpass times.…”

“…The Pinhorn Grazing Reserve is dominated by Bouteloua gracilis with some Stipa comata and Agropyron species due to differing edaphic conditions. Management regimes consisted of light, rotational grazing typical of the region (e.g., [31]). Average annual precipitation in these areas is around 325 mm per year [8].…”

“…This index compares the low reflectance of photosynthetic materials in the red wavelengths to the high reflectance in the near-infrared to produce an estimation of plant biophysical parameters, including biomass, leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (f APAR ). Numerous studies have demonstrated that NDVI strongly correlates with green biomass and leaf area index, particularly for grassland ecosystems [19,25,26,30,31]. In grassland ecosystems, NDVI also provides a strong indicator of photosynthetic carbon uptake, typically measured via gas exchange or eddy covariance [16,26,[31][32][33], indicating its potential as a useful metric for carbon sequestration.…”

“…Numerous studies have demonstrated that NDVI strongly correlates with green biomass and leaf area index, particularly for grassland ecosystems [19,25,26,30,31]. In grassland ecosystems, NDVI also provides a strong indicator of photosynthetic carbon uptake, typically measured via gas exchange or eddy covariance [16,26,[31][32][33], indicating its potential as a useful metric for carbon sequestration. Furthermore, in prairies, NDVI often scales with metrics of species richness and evenness [16,17], so may also provide a useful indicator of biodiversity or other ecosystem co-benefits.…”

Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy

“…The following bands were considered: B3 (green: 0.53-0.59 µm), B4 (red: 0.64-0.67 µm), B5 (NIR: 0.85-0.88 µm), B6 (SWIR1: 1.58-1.65 µm). Due to the well-known linear or approximately linear relationship between fAPAR and NDVI [19,[72][73][74][75][76], we also used B4 and B5 to compute NDVI [77] and additionally use it as a predictor variable (Equation (4)). In addition to the ESU measurements over the forest, additional values over bare areas and prairie were used to better constrain the model for low fAPAR values.…”