Abstract
In order to enhance the simulated accuracy of jujube yields at the field scale, this study attempted to employ SUBPLEX algorithm to assimilate remotely sensed leaf area indices (LAI) of four key growth stages into a calibrated World Food Studies (WOFOST) model, and compare the accuracy of assimilation with the usual ensemble Kalman filter (EnKF) assimilation. Statistical regression models of LAI and Landsat 8 vegetation indices at different developmental stages were established, showing a validated R2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m2 m−2 for emergence, fruit filling, white maturity, and red maturity periods. The results showed that both SUBPLEX and EnKF assimilations significantly improved yield estimation performance compared with un-assimilated simulation. The SUBPLEX (R2 = 0.78 and RMSE = 0.64 t ha−1) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R2 = 0.73 and RMSE = 0.71 t ha−1), especially for high-yield and low-yield jujube orchards. SUBPLEX assimilation produced a relative bias error (RBE, %) that was more concentrated near zero, being lower than 10% in 80.1%, and lower than 20% in 96.1% for SUBPLEX, 72.4% and 96.7% for EnKF, respectively. The study provided a new assimilation scheme based on SUBPLEX algorithm to employ remotely sensed data and a crop growth model to improve the field-scale fruit crops yield estimates.
Highlights
Jujube (Zizyphus jujuba) is a significant economic tree species in China with approximately 3,250,000 hectares in 2017, and its fruit has important nutritional and medical value [1,2]
RemoteTSheness. 2ta01ti9s,t1i1c,axl FrOegRrPeEsEsiRoRnEmVIoEdWels between vegetation index (NDVI or SAVI) and leaf area index (LAI) for th10e ofof u19r jujube key growth periods are shown in Equations (10)–(13), respectively, which were obtained from trheegr3e7sssiaomnspolefs3f7orfieealdc-hmpeearsioudrewd aLsAeIxatrnedmSeAlyVsIigonriNficDaVntI ((pFig
The SUBPLEX algorithm was tested to assimilate remotely-sensed data into the World Food Studies (WOFOST) model to improve the modeling accuracy for jujube yield at the field scale
Summary
Jujube (Zizyphus jujuba) is a significant economic tree species in China with approximately 3,250,000 hectares in 2017, and its fruit has important nutritional and medical value [1,2]. The key input parameters or state variables, such as phenology information [4], leaf area index (LAI) [5,6,7,8,9,10], biomass [11], crop transpiration (ET) [7], and soil moisture (SM) [5,12,13,14,15,16], can be observed from remote sensing data. Data assimilation methods, mainly including variational (calibration) and sequential (update) methods, have been used to integrate remote sensing data and crop models to improve the prediction accuracy of canopy state variables and yields at the field, regional, and national scale [17]
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