Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm

Abstract

Timely and accurate estimation of winter wheat yield at a regional scale is crucial for national food policy and security assessments. Near-infrared reflectance is not sensitive to the leaf area index (LAI) and biomass of winter wheat at medium to high canopy cover (CC), and most of the vegetation indices displayed saturation phenomenon. However, LAI and biomass at medium to high CC can be efficiently estimated using imaging data from radar with stronger penetration, such as RADARSAT-2. This study had the following three objectives: (i) to combine vegetation indices based on our previous studies for estimating CC and biomass for winter wheat using HJ-1A/B and RADARSAT-2 imaging data; (ii) to combine HJ-1A/B and RADARSAT-2 imaging data with the AquaCrop model using the particle swarm optimization (PSO) algorithm to estimate winter wheat yield; and (iii) to compare the results from the assimilation of HJ-1A/B+RADARSAT-2 imaging data, HJ-1A/B imaging data, and RADARSAT-2 imaging data into the AquaCrop model using the PSO algorithm. Remote sensing data and concurrent LAI, biomass, and yield of sample fields were acquired in Yangling District, Shaanxi, China, during the 2014 winter wheat growing season. The PSO optimization algorithm was used to integrate the AquaCrop model and remote sensing data for yield estimation. The modified triangular vegetation index 2 (MTVI2)×radar vegetation index (RVI) and the enhanced vegetation index (EVI)×RVI had good relationships with CC and biomass, respectively. The results indicated that the predicted and measured yield (R2=0.31 and RMSE=0.94ton/ha) had agreement when the estimated CC from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model. When the estimated biomass from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model, the predicted yield showed agreement with the measured yield (R2=0.42 and RMSE=0.81ton/ha). These results show that using the biomass as the dynamic input variable provides a better yield estimation than using the CC as the dynamic variable. The predicted biomass and yield were more accurately estimated by combining the HJ-1A/B and RADARSAT-2 data with the AquaCrop model than by combining the only HJ-1A/B or RADARSAT-2 data with the AquaCrop model using the PSO algorithm. The results indicated that the PSO-based assimilation method could be used to estimate the winter wheat yield from the spot to the regional scale.