African Journal of Tropical Agriculture ISSN 2375-091X Vol. 8 (3), pp. 001-013, March, 2020. © International Scholars Journals

Full Length Research Paper

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. Myneni2

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.

Accepted 05 January, 2020


A 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.

Key words: Remote sensing, leaf area index, crop monitoring, early yield forecast, drought index, climate impacts.