Relationships between the evaporative stress index and winter wheat and spring barley yield anomalies in the Czech Republic

Abstract

There is a growing demand for timely, spatially distributed information regarding crop condition and water use to inform agricultural decision making and yield forecasting efforts. Thermal infrared remote sensing of land-surface temperature has proven valuable for mapping evapotranspiration (ET) and crop stress from field to global scales using energy balance models. This is because canopy temperature is strongly regulated by the transpiration flux, which is reduced under stress conditions. This study investigates the utility of an evaporative stress index (ESI), computed using the thermal-based Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model, for explaining yield variability over the Czech Republic for the period 2002−2014. ESI timeseries, representing standardized anomalies in the actual-to-reference ET ratio and an indicator of vegetation health, are compared with yield data collected for winter wheat and spring barley crops in 32 agricultural districts, comprising a range of climatic conditions within the Czech Republic. Correlations between ESI and yield anomalies vary with climatic region, with strongest correlations identified in the more drought-prone South Moravian districts and weaker relationships in the wetter highlands regions. In most regions, correlations with spring barley yield anomalies exceeded performance for winter wheat. For both crops, correlations peaked during the 1 to 2 mo period prior to the nominal harvest date. These results provide guidance for effective integration of remotely sensed moisture stress indicators within operational yield forecasting systems.

Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

“…More environmental variables are needed for crop yield modeling. Evapotranspiration (ET) and normalized evaporative stress index (ESI) products derived at the Landsat scale by similar data fusion methods may provide the necessary additionally information to capture the temporal variability of crop yield [76][77][78]-this is a topic of current investigation. For large areas, spatial variability of PAR, ET, temperature, seed species, and management approach etc.…”

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

“…Besides weather variables, satellitederived vegetation indices (VIs) significantly improve the skill of yield predictions in many studies (Becker-Reshef et al 2010, Johnson 2014, Guan et al 2017, Peng et al 2018. In addition, a few studies show that satellite-based evapotranspiration (ET) and soil moisture observations possess unique signatures in quantifying environmental stress and might provide additional information than conventional opticalbased VIs (Anderson et al 2016a(Anderson et al , 2016bGuan et al 2017, Mladenova et al 2017, Yang et al 2018.…”

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

“…The ESI also has capabilities for early warning of rapid drought onset (or "flash drought") events (Otkin et al, 2018), conveyed by thermal signals of elevated canopy and soil temperatures that precede visible degradation in the vegetation canopy (Otkin et al, , 2014Anderson et al, 2011Anderson et al, , 2013. Correlations between ESI and reported crop yields have been investigated in the U.S. (Otkin et al, 2016;Mladenova et al, 2017), Brazil (Anderson et al, 2016a), and the Czech Republic (Anderson et al, 2016b), demonstrating capacity to explain regional yield variability in water limited crop growing regions, in many cases providing higher correlations than vegetation index or precipitation anomalies.…”

“…Several workable approaches have been developed including the Surface Energy Balance Algorithm for Land (SEBAL; Bastiaanssen et al, 1998), the Mapping Evapotranspiration with Internalized Calibration (METRIC; Allen et al, 2007), the Two Source Energy Balance model (TSEB; Norman et al, 1995), and the Atmosphere-Land Exchange Inverse model (ALEXI; Anderson et al, 1997Anderson et al, , 2007a and an associated disaggregation algorithm (DisALEXI; Anderson et al, 2004). These TIR-based ET mapping algorithms provide regional and global coverage efficiently and economically, motivating studies relating remote sensing-based ET estimates to crop productivity (Bastiaanssen and Ali, 2003;Mishra et al, 2013;Tadesse et al, 2015;Anderson et al, 2016aAnderson et al, , 2016bMladenova et al, 2017). Anderson et al (2007a) proposed the Evaporative Stress Index (ESI) as a new remote sensing drought indicator, which is based on temporal anomalies in f RET retrieved using TIR imagery from geostationary (GEO) satellites.…”