Mapping winter-wheat biomass and grain yield based on a crop model and UAV remote sensing

ABSTRACT

Timely and accurately estimates of crop biomass and grain yield estimation are crucial for agricultural management. Optical remote sensing techniques can provide crop parameters (e.g., biomass, fractional vegetation cover (FVC)) at regional and larger scales. However, such techniques saturate at high crop canopy cover and cannot detect biomass stored in reproductive organs. The AquaCrop model can be used to estimate FVC, biomass, and grain yield output based on crop growth environmental parameters (e.g., temperature, rainfall, irrigation). In this work, we developed a method for estimating and mapping crop biomass and grain yield using unmanned aerial vehicle (UAV) remote sensing images and AquaCrop. The combination of low-cost UAV remote sensing data and AquaCrop can be used to map wheat biomass and grain yield before harvest. This work investigated whether biomass and grain yield can be predicted using measurements of biomass and FVC in several winter-wheat fields at an early growing stage using ground-based and remote sensing-based methods. FVC, biomass, and grain yield were measured at several key growing stages (jointing (S1), heading (S2), flowering (S3), grain filling (S4)) and harvest (H) during 2014–2015. We specifically evaluated the performances of using field- and UAV-based FVC and biomass measurements from different growing-stage combinations (e.g., S1–S4, S1–S3, and S1–S2) to calibrate the AquaCrop model and estimate biomass and grain yield. The results indicate that winter-wheat biomass and grain yield can be estimated by calibrating AquaCrop using (i) biomass and (ii) biomass and FVC. The results also reveal that the biomass and grain yield estimated using FVC and AquaCrop have poor accuracy compared with the biomass and grain yield estimated using (i) only biomass and (ii) biomass and FVC. The results suggest that the combined use of UAV remote sensing and AquaCrop can be used to obtain maps of biomass and biomass yield. When estimating winter-wheat biomass and grain yield one month (13 May) before harvest (11 June), the predicted biomass and grain yield agreed with the measured values (biomass: n = 96, coefficient of determination (R²) = 0.61, mean absolute error (MAE) = 1.69 t ha^–1, root-mean-square error (RMSE) = 2.10 t ha^–1, normalized RMSE (nRMSE) = 18.5%; grain yield: n = 48, R² = 0.63, MAE = 0.96 t ha^–1, RMSE = 1.16 t ha^–1, nRMSE = 21.9%). When estimating winter-wheat biomass and grain yield one and a half months (26 April) before harvest (11 June), the predicted biomass and grain yield was lower than when estimating winter-wheat biomass and grain yield one month before harvest (biomass: n = 144, R² = 0.55, MAE = 2.52 t ha^–1, RMSE = 3.34 t ha^–1, nRMSE = 23.6%; grain yield: n = 48, R² = 0.34, MAE = 1.68 t ha^–1, RMSE = 2.17 t ha^–1, nRMSE = 35.7%).

Acknowledgements

We wish to thank Bo Xu, Hong Chang, and Weiguo Li for performing the field data collection and farmland management.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the Key-Area Research and Development Program of Guangdong Province [2019B020214002], theNational Natural Science Foundation of China [41601346, 41771370], the National Key Research and Development Program of China [2016YFD0300602, 2017YFD0600903], and the High-resolution Earth Observation Project of China (03-Y20A04-9001-17/18 & 30-Y20A07-9003-17/18).