@article {bnh-1844, title = {Global vegetation gross primary production estimation using satellite-derived light-use efficiency and canopy conductance}, journal = {Remote Sensing of Environment}, volume = {163}, year = {2015}, month = {06/2015}, pages = {206-216}, chapter = {206}, abstract = {

Climate and physiological controls of vegetation gross primary production (GPP) vary in space and time. In many ecosystems, GPP is primary limited by absorbed photosynthetically-active radiation; in others by canopy conductance. These controls further vary in importance over daily to seasonal time scales. We propose a simple but effective conceptual model that estimates GPP as the lesser of a conductance-limited (Fc) and radiation-limited (Fr) assimilation rate.\ Fc\ is estimated from canopy conductance while\ Fr\ is estimated using a light use efficiency model. Both can be related to vegetation properties observed by optical remote sensing. The model has only two fitting parameters: maximum light use efficiency, and the minimum achieved ratio of internal to external CO2\ concentration. The two parameters were estimated using data from 16 eddy covariance flux towers for six major biomes including both energy- and water-limited ecosystems. Evaluation of model estimates with flux tower-derived GPP compared favourably to that of more complex models, for fluxes averaged; per day (r2\ =\ 0.72, root mean square error, RMSE\ =\ 2.48\ μmol\ C\ m2\ s-\ 1, relative percentage error, RPE\ =\ -\ 11\%), over 8-day periods (r2\ =\ 0.78 RMSE\ =\ 2.09\ μmol\ C\ m2\ s-\ 1,RPE\ =\ -\ 10\%), over months (r2\ =\ 0.79, RMSE\ =\ 1.93\ μmol\ C\ m2\ s-\ 1, RPE\ =\ -\ 9\%) and over years (r2\ =\ 0.54, RMSE\ =\ 1.62\ μmol\ C\ m2\ s-\ 1, RPE\ =\ -\ 9\%). Using the model we estimated global GPP of 107\ Pg\ C\ y-\ 1\ for 2000{\textendash}2011. This value is within the range reported by other GPP models and the spatial and inter-annual patterns compared favourably. The main advantages of the proposed model are its simplicity, avoiding the use of uncertain biome- or land-cover class mapping, and inclusion of explicit coupling between GPP and plant transpiration.

}, keywords = {Photosynthesis; Canopy conductance; Light use efficiency; FLUXNET; MODIS; Gross primary production; GPP; Vegetation}, doi = {doi:10.1016/j.rse.2015.03.016}, url = {http://www.sciencedirect.com/science/article/pii/S0034425715001121}, author = {Marta Yebra and Albert van Dijk and Ray Leuning and Guerschman, Juan Pablo} } @article {BF-2406, title = {Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors}, journal = {Remote Sensing of Environment}, volume = {113}, year = {2009}, month = {05/2009}, pages = {928 - 945}, abstract = {Quantitative estimation of fractional cover of photosynthetic vegetation (fPV), non-photosynthetic vegetation (fNPV) and bare soil (fBS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving fPV, fNPV and fBS across the ~ 2 million km2 Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for fPV, fNPV and fBS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000{\textendash}2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of fNPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of fPV, fNPV and fBS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world. }, doi = {10.1016/j.rse.2009.01.006}, author = {Guerschman, Juan Pablo and Hill, Michael J. and Luigi J. Renzullo and Damian J. Barrett and Marks, Alan S. and Botha, Elizabeth J.} }