|Moran, Mary - Susan|
|Lo Seen, D.|
|Heilman, Philip - Phil|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2001
Publication Date: 11/1/2001
Citation: Nouvellon, Y., Moran, M.S., Lo Seen, D., Bryant, R., Ni, W., Beque, A., Chehbouni, A., Emmerich, W.E., Heilman, P., Qi, J. 2001. Coupling a grassland ecosystem model with Landsat imagery for a 10-year simulation of carbon and water budgets. Remote Sensing of Environment. 78:131-149. Interpretive Summary: Some ranchers are using computer models to simulate grassland growth and assist them with day-to-day management decisions. These models have proven useful, but are often inaccurate. In this study, we combined a grassland simulation model with pictures of the grassland acquired with satellite-based sensors. The satellite pictures provided regional information for the model and improved forecasts of grassland growth. These promising results suggest that an approach which combines a simple grassland growth model with satellite images could provide ranch-scale information about grass and soil conditions for day-to-day range management.
Technical Abstract: In this study, high-spatial, low-temporal scale visible remote sensing data were used to calibrate an ecosystem model for semiarid perennial grasslands. The model was driven by daily meteorological data and simulated plant growth and water budget on the same time step. The model was coupled with a canopy reflectance model to yield the time courses of shortwave radiometric profiles. Landsat TM and ETM+ images from 10 consecutive years were used to refine the model on a spatially distributed basis. A calibration procedure, which minimized the difference between the NDVI simulated from the coupled model and measured by the TM and ETM+ sensors, yielded the spatial distribution of an unknown parameter and initial condition. Accuracy of model products, such as daily aboveground biomass, LAI and soil water content, was assessed by comparing them with field measurements. The promising results suggest that this approach could provide spatially distributed information about both vegetation and soil conditions for day-to-day grassland management.