Application of a
satellite-based climate-variability impact index for crop
yield forecasting in drought-stricken regions
Ping Zhang1*, Bruce Anderson2,
Mathew Barlow3, Bin Tan1 and Ranga B.
1Earth Resource Technology Inc., Annapolis Junction, MD, 20701, USA.
2Department of Geography, Boston University, Boston, MA, 02215, USA.
3Environmental, Earth and Atmospheric Sciences, University of
Massachusetts Lowell, Lowell, MA, 01854, USA.
*Corresponding author. E-mail:
Ping.email@example.com. Tel: 301-614-6698.
15 January, 2010
quantitative index is applied to monitor crop growth and
predict agricultural yield in drought-stricken regions. This
Climate-Variability Impact Index (CVII), defined as the
monthly contribution to overall anomalies in growth during a
given year, is derived from 1 km MODIS Leaf Area Index. The
CVII integrated over the growing season represents the
percentage of the climatological production either gained or
lost due to climatic variability during a given year and is
positively correlated with crop yields. In two test cases
presented here, a statistical model is trained using the
historical CVII and yield records and is then applied to
predict crop yields for Illinois in 2005 as well as North
and South Dakota in 2006. The model predictions are
consistent with USDA’s estimates obtained after harvesting.
Since the CVII are available in near real-time, the model
predictions can also be obtained monthly before the end of
the growing season. The in-season CVII model shows
predictability comparable to the concurrent NASS estimates.
In addition, these model forecasts improve as more CVII
series are added in the late season. Finally, this research
highlights the need for explicit monitoring of vegetation
growth when estimating yield as drought-monitoring indices
such as the Standardized Precipitation Index can both
overestimate and underestimate implied changes in vegetation
in drought-stricken regions.
Remote sensing, leaf area index, crop monitoring, early
yield forecast, drought index, climate impacts.