Improving jujube fruit tree yield estimation at the field scale by assimilating a single Landsat remotely-sensed LAI into the WOFOST model
Bai, Tiecheng | Zhang, Nannan | Mercatoris, Benoît | Chen, Youqi
Английский. peer reviewed
Показать больше [+] Меньше [-]Английский. 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 -2, -3, and -3 days for emergence, flowering, and maturity, as well as an R2 of 0.986 and RMSE of 0.624 t ha-1 for total aboveground biomass (TAGP), R2 of 0.95 and RMSE of 0.19 m2 m-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-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-1 for 2016, and R2 of 0.59 and RMSE of 0.87 t ha-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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