Improving Wheat Yield Estimates by Integrating a Remotely Sensed Drought Monitoring Index Into the Simple Algorithm for Yield Estimate Model

Water stress is an important factor to be considered when using crop growth models for crop yields estimation. In this article, we propose a simple algorithm for yield estimate (SAFY) V model to estimate yields of winter wheat by integrating the time-series remotely sensed drought monitoring index, vegetation temperature condition index (VTCI), into SAFY model, and the fixed effective light-use efficiency parameter (elue(0)) in the SAFY model is modified to a new varied parameter (E) in the SAFY-V model. The parameter E can accurately describe the changes of water stress at the four growth stages of winter wheat and has a high correlation with the field measured yields (R-2 values are 0.28, 0.34, 0.31 and 0.31, respectively). The SAFY-V model integrating the time series leaf area index (LAIs) and VTCIs which has the highest accuracy on dry aerial mass estimation compared with the SAFY model and SAFY-WB model (a combination of the SAFY model and water balance model), can better alleviate the yield underestimation and overestimation and greatly improve the estimation accuracy of winter wheat yield especially in rain-fed farmlands (R-2 = 0.48, MAE = 1.05 t/ha and NMAE = 15.6%), and the accuracy of winter wheat yields estimates at the county scale was also satisfactory (R-2 = 0.49, MAE = 0.73 t/ha and NMAE = 16.1% for five years). The proposed SAFY-V model of this article has few model parameters and low computational cost, which provides a significant reference for crop yield estimation at a regional scale.

Automated summary

The proposed SAFY-V model, integrating VTCI, can better estimate winter wheat yields, especially in arid areas, thereby improving estimation accuracy.