Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model
2019
Tiecheng Bai | Nannan Zhang | Benoit Mercatoris | Youqi Chen
Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into a calibrated WOFOST model to predict yields for jujube fruit trees at the field scale. Field experiments were conducted in three growth seasons to calibrate input parameters for WOFOST model, with a validated phenology error of &minus:2, &minus:3, and &minus:3 days for emergence, flowering, and maturity, as well as an R2 of 0.986 and RMSE of 0.624 t ha&minus:1 for total aboveground biomass (TAGP), R2 of 0.95 and RMSE of 0.19 m2 m&minus:2 for LAI, respectively. Normalized Difference Vegetation Index (NDVI) showed better performance for LAI estimation than a Soil-adjusted Vegetation Index (SAVI), with a better agreement (R2 = 0.79) and prediction accuracy (RMSE = 0.17 m2 m&minus:2). The assimilation after forcing LAI improved the yield prediction accuracy compared with unassimilated simulation and remotely sensed NDVI regression method, showing a R2 of 0.62 and RMSE of 0.74 t ha&minus:1 for 2016, and R2 of 0.59 and RMSE of 0.87 t ha&minus:1 for 2017. This research would provide a strategy to employ remotely sensed state variables and a crop growth model to improve field-scale yield estimates for fruit tree crops.
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