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Remote Sens. 2016, 8(3), 260; doi:10.3390/rs8030260

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

1
Institute of Agro-Environmental & Forest Biology (IBAF), National Research Council (CNR), Via Marconi 2, Porano (TR) 05010, Italy
2
Czechglobe, Global Change Research Centre, Bělidla 986/4a, 603 00 Brno, Czech Republic
3
Departments of Earth and Atmospheric Sciences and Biological Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
4
Department of Biology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
5
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2E3, Canada
*
Authors to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly, Compton Tucker, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 8 January 2016 / Revised: 12 March 2016 / Accepted: 14 March 2016 / Published: 19 March 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [3321 KB, uploaded 23 March 2016]   |  

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 (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 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 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 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 APARgreen 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. View Full-Text
Keywords: grassland; NDVI; CO2 flux; optical remote sensing; LUE model; gap filling grassland; NDVI; CO2 flux; optical remote sensing; LUE model; gap filling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Nestola, E.; Calfapietra, C.; Emmerton, C.A.; Wong, C.Y.; Thayer, D.R.; Gamon, J.A. Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements. Remote Sens. 2016, 8, 260.

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