81 A correlation analysis comparing satellite-based drought indicators with yields of major Brazilian crops

Monday, 11 January 2016
Martha C. Anderson, USDA/ARS, Beltsville, MD; and C. Zolin, P. Sentelhas, C. Hain, M. T. Yilmaz, F. Gao, J. Otkin, and R. Tetrault

To effectively meet growing global food demands, society will require a better understanding of factors that are currently limiting agricultural yields and where production can be viably expanded with minimal environmental consequences. Remote sensing can help to inform these analyses, providing valuable spatiotemporal information about yield-limiting moisture conditions and crop response to drought under current climate conditions. In this paper we study correlations for the period 2002-2013 between yield estimates for major crops grown in Brazil and satellite indicators of crop water use or evapotranspiration (as conveyed by the Evaporative Stress Index; ESI), water supply (rainfall from the Tropical Rainfall Mapping Mission; TRMM) and biomass accumulation (leaf area index; LAI from the Moderate Resolution Imaging Spectroradiometer - MODIS). Correlations with yield data reported at both the state and municipality levels were computed as a function of satellite index composite date through the growing season to evaluate strength and timing of peak correlations for each index class. Correlation patterns were in general similar between all indices, both spatially and temporally. Spatial variability in correlation strength was largely driven by variability in yield over the period of record, with strongest correlations found in the south and northeast where severe flash droughts have occurred over the past decade. Peak correlations tended to occur during sensitive crop growth stages. At regional scales using state-level yield, the ESI provided higher and somewhat earlier peak yield correlations for most crops and regions in comparison with TRMM and LAI anomalies. These results provide insight into when and where different remote sensing drought and vegetation indicators are likely to add significant value to yield forecasts, setting the stage for future work involving assimilation into crop modeling systems.
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