Location: Hydrology and Remote Sensing LaboratoryTitle: Phenological corrections to a field-scale, ET-based crop stress indicator: an application to yield forecasting across the U.S. Corn Belt
|YANG, YANG - US Department Of Agriculture (USDA)|
|JOHNSON, D. - University Of Florida|
|YANG, YUN - US Department Of Agriculture (USDA)|
|SUN, L. - Chinese Academy Of Agricultural Sciences|
|HAIN, C. - Nasa Marshall Space Flight Center|
|OTKIN, J. - University Of Wisconsin|
|MEYERS, T. - National Oceanic & Atmospheric Administration (NOAA)|
|MOORE, C.E. - University Of Illinois|
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2021
Publication Date: 2/23/2021
Citation: Yang, Y., Anderson, M.C., Gao, F.N., Johnson, D., Yang, Y., Sun, L., Dulaney, W.P., Hain, C., Otkin, J., Prueger, J.H., Meyers, T., Bernacchi, C.J., Moore, C. 2021. Phenological corrections to a field-scale, ET-based crop stress indicator: an application to yield forecasting across the U.S. Corn Belt. Remote Sensing of Environment. 257:112337. https://doi.org/10.1016/j.rse.2021.112337.
Interpretive Summary: Most satellite-based yield prediction methods use remotely sensed vegetation indices describing crop growth and canopy development. However, yield can be very sensitive to short-term soil moisture deficiencies or other stresses during critical stages of crop growth. These stresses may not be reflected in standard vegetation indices. This paper investigates the utility of a field-scale crop water use and stress index for predicting corn and soybean yield, with an application over counties spanning the U.S. Corn Belt. The results demonstrate good prediction ability, particularly when the index is corrected for variations in crop emergence date and growing season temperatures from year to year. In combination with standard vegetation indices, this new field-scale stress index has the potential to improve yield predictions under conditions of short-term rainfall deficits or flash drought.
Technical Abstract: Soil moisture deficiency is a major factor in determining crop yields in water-limited agricultural production regions. Evapotranspiration (ET), which consists of crop water use through transpiration and water loss through direct soil evaporation, is a good indicator of soil moisture availability and vegetation health. ET therefore has been an integral part of many yield estimation efforts. 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). Regression equations derived at the time of peak correlation between ESI and yield anomaly were applied for yield estimation at the county level. Results showed that ESI has good correlations with yield anomalies at the county scale and that phenological corrections to the annual temporal alignment of the ET timeseries improved the correlation, 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. These results demonstrate that remotely sensed ET at high spatiotemporal resolution contains water stress information that enhances the ability to predict crop yield.