Forecasting vegetation greenness with satellite and climate data
IEEE Geoscience and Remote Sensing Letters
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Abstract
A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R2 values range from 0.97-0.80 for 2-12 week forecasts, with higher R2 associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Forecasting vegetation greenness with satellite and climate data |
| Series title | IEEE Geoscience and Remote Sensing Letters |
| DOI | 10.1109/LGRS.2003.821264 |
| Volume | 1 |
| Issue | 1 |
| Year Published | 2004 |
| Language | English |
| Publisher | IEEE |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 4 p. |
| First page | 3 |
| Last page | 6 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |