Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

Authors

Yang Yang, USDA, Agricultural Research ServiceFollow
Martha C. Anderson, USDA, Agricultural Research ServiceFollow
Feng Gao, USDA, Agricultural Research ServiceFollow
Brian D. Wardlow, University of Nebraska - LincolnFollow
Christopher R. Hain, Earth Science BranchFollow
Jason A. Otkin, University of Wisconsin-MadisonFollow
Joseph Alfieri, USDA, Agricultural Research ServiceFollow
Yun Yang, USDA, Agricultural Research ServiceFollow
Liang Sun, USDA, Agricultural Research ServiceFollow
Wayne Dulaney, USDA, Agricultural Research ServiceFollow

Document Type

Article

Date of this Version

2018

Citation

Remote Sensing of Environment 210 (2018) 387–402

Comments

© 2018 The Authors.

Open access

https://doi.org/10.1016/j.rse.2018.02.020

Abstract

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (f_RET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, f_RET, and f_RET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input f_RET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting f_RET alignment to a regionally reported emergence date prior to anomaly computation improves r² correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning f_RET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models.