PT Journal
AU Gu, Y
   Wylie, BK
   Bliss, NB
TI Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO database over the Greater Platte River basin, USA
SO Ecological Indicators
PY 2013
BP 31
EP 36
VL 24
LA FY 2012
DE area; basin; biophysical; change; climate; climate change; composite; correlation; data; database; Difference Vegetation Index; ecological; ecological characteristic; eMODIS NDVI; flux; flux tower; GPP; grassland; Greater Platte River basin; growing season; GSN; imaging; imaging spectroradiometer; journal articles; land; land manager; map; mapping; model; Moderate Resolution Imaging Spectroradiometer; NDVI; Normalized Difference Vegetation Index; pixel; Platte River; production; productivity; rangeland; regional; resolution; river; river basin; season; soil; soil survey; spatially explicit; spectroradiometer; SSURGO grassland productivity; tower; United States; USA; vegetation; vegetation index
AB This study assessed and described a relationship between satellite-derived growing season averaged Normalized Difference Vegetation Index (NDVI) and annual productivity for grasslands within the Greater Platte River Basin (GPRB) of the United States. We compared growing season averaged NDVI (GSN) with Soil Survey Geographic (SSURGO) database rangeland productivity and flux tower Gross Primary Productivity (GPP) for grassland areas. The GSN was calculated for each of nine years (2000-2008) using the 7-day composite 250-m eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. Strong correlations exist between the nine-year mean GSN (MGSN) and SSURGO annual productivity for grasslands (R2 = 0.74 for approximately 8000 pixels randomly selected from eight homogeneous regions within the GPRB; R2 = 0.96 for the 14 cluster-averaged points). Results also reveal a strong correlation between GSN and flux tower growing season averaged GPP (R2 = 0.71). Finally, we developed an empirical equation to estimate grassland productivity based on the MGSN. Spatially explicit estimates of grassland productivity over the GPRB were generated, which improved the regional consistency of SSURGO grassland productivity data and can help scientists and land managers to better understand the actual biophysical and ecological characteristics of grassland systems in the GPRB. This final estimated grassland production map can also be used as an input for biogeochemical, ecological, and climate change models.
ER