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Article

Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model

1
Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer 843300, China
2
TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030 Gembloux, Belgium
3
Institute of Agricultural Resources and Regional Planning of CAAS, No.12 Zhongguancun South St., Haidian District, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Received: 15 April 2019 / Revised: 6 May 2019 / Accepted: 8 May 2019 / Published: 10 May 2019
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. View Full-Text
Keywords: Assimilation; leaf area index; jujube yield estimation; WOFOST model Assimilation; leaf area index; jujube yield estimation; WOFOST model
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MDPI and ACS Style

Bai, T.; Zhang, N.; Mercatoris, B.; Chen, Y. Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model. Remote Sens. 2019, 11, 1119. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091119

AMA Style

Bai T, Zhang N, Mercatoris B, Chen Y. Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model. Remote Sensing. 2019; 11(9):1119. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091119

Chicago/Turabian Style

Bai, Tiecheng, Nannan Zhang, Benoit Mercatoris, and Youqi Chen. 2019. "Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model" Remote Sensing 11, no. 9: 1119. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091119

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