A field-scale, ET-based crop stress indicator for yield estimates: an application across the Corn Belt, USA

Abstract:

Evapotranspiration (ET) is an indicator of soil moisture availability and vegetation health. The Evaporative Stress Index (ESI) is an ET-based crop stress indicator that describes temporal anomalies in a normalized evapotranspiration metric as derived from satellite remote sensing. ESI has demonstrated the capacity to explain regional yield variability in water-limited regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to interannual phenological variability. This investigation selected three study sites across the U.S. Corn Belt –Mead, NE, Ames, IA and Champaign, IL - to investigate the potential operational value of 30-m resolution, phenologically corrected ESI datasets for yield prediction. The analysis was conducted over an 8-year period from 2010-2017, which included both drought and pluvial conditions as well as a broad range in yield values. Yield anomalies for corn and soybean were correlated with ESI computed using three temporal alignments: (1) annual ET curves aligned based on calendar date, (2) crop emergence date, and (3) time axis defined with growing degree days (GDD). Results show that field-scale ESI has the advantage of being able to resolve different crop types with varying phenology. Moreover, incorporating phenological information can improve yield-correlations, especially when the time axis is defined by GDD rather than the calendar date. Peak correlations occur in the silking stage for corn and the reproductive stage for soybean – phases when these crops are particularly sensitive to soil moisture deficiencies. The ESI-based yield estimates correlate well with the USDA National Agricultural Statistics Service (NASS) county-level crop yield data, with correlation coefficients ranging from 0.78 to 0.94. This approach prototypes a methodology that benefits operational yield monitoring using remote sensing based ET estimates.