数字农科院2.0

Assimilation Of Remotely-Sensed Lai Into W.ofost Model With The S ubplex Algorithm For Improving The Field-Scale Jujube Yield Forecasts

文献类型： 外文期刊

作者： Bai, TC; Wang, SG; Meng, WB; Zhang, NN; Wang, T; Chen, YQ; Mercatoris, B

作者机构：

关键词： assimilation method; SUBPLEX; remote sensing; WOFOST model; jujube yield estimation

期刊名称： REMOTE SENSING

ISSN：

年卷期： 2019 年 11 卷 16 期

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： 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 R-2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m(2) 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 (R-2 = 0.78 and RMSE = 0.64 t ha(-1)) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R-2 = 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.

分类号：

  • 相关文献

[1]Improving Jujube Fruit Tree Yield E.stimation At The Field S cale By Assimilating A Single Landsat Remotely-Sensed Lai Into The Wofost Model. Bai, TC, Zhang, NN, Mercatoris, B, Chen, YQ. 2019

[2]Net Surface Shortwave Radiation Retrieval Using Random Forest Method With Modis/Aqua Data. Li, Zhao-Liang,Wu, Hua,Wu, Hua,Wu, Hua,Ying, Wangmin,Ying, Wangmin,Li, Zhao-Liang. 2019

[3]Estimation of evapotranspiration using all-weather land surface temperature and variational trends with warming temperatures for the River Source Region in Southwest China. Ma Y., Zhou J., Liu S., Zhang W., Zhang Y., Xu Z., Song L., Zhao H.. 2022

[4]A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data. Rui Yao,Lunche Wang,Xin Huang,Liang Sun,Ruiqing Chen,Xiaojun Wu,Wei Zhang,Zigeng Niu. 2021

[5]Development Of A Low-Cost Quadrotor U.av Based On Adrc F or Agricultural Remote Sensing. Xue, Xinyu,Zhang, Songchao,Zhang, Songchao,Sun, Tao,Chen, Chen,Sun, Zhu. 2019

[6]Rapid Detection Of Chlorophyll Content A.nd Distribution In Citrus O rchards Based On Low-Altitude Remote Sensing And Bio-Sensors. Deng, Lie,He, Shaolan,Li, Wentao,Lyu, Qiang,Zheng, Yongqiang,Yi, Shilai,Xie, Rangjin,Wang, Kejian,Ma, Yanyan. 2018

[7]Evaluation Of Machine Learning Algorithms In Spatial Downscaling Of Modis Land Surface Temperature. Wu, Hua,Duan, Si-Bo,Li, Zhao-Liang,Wu, Hua,Li, Zhao-Liang,Li, Wan,Li, Wan,Ni, Li,Wu, Hua. 2019

[8]Water quality related to Conservation Reserve Program (CRP) and cropland areas: Evidence from multi-temporal remote sensing. Yin, Dameng,Wang, Le,Zhu, Zhenduo,Clark, Susan Spierre,Cao, Ying,Besek, Jordan,Dai, Ning. 2021

[9]A Framework For Assessing The E.ffects Of Afforestation And S outh-To-North Water Transfer On Nitrogen And Phosphorus Uptake By Plants In A Critical Riparian Zone. Zhao, HG, Huang, YC, You, SC, Wu, YF, Zheng, FX. 2019

[10]Estimation Of Daily Evapotranspiration Using M.odis Data To Calculate I nstantaneous Decoupling Coefficient And Resistances. Jiang, YZ, Jiang, XG, Tang, RL, Li, ZL, Zhang, YZ, Huang, C, Ru, C. 2017

[11]The Influences Of The South-To-North Water Transfer Project On Nitrogen Removal By Soil In A Terminal Reservoir In A Critical State In China. Xue, WL, Li, FH, Zhao, HG. 2019

[12]Estimating Grassland Lai Using The R.andom Forests Approach And L andsat Imagery In The Meadow Steppe Of Hulunber, China. Li, ZW, Xin, XP, Tang, H, Yang, F, Chen, BR, Zhang, BH. 2017

[13]Perspective Of Chinese Gf-1 High-Resolution S.atellite Data In Agricultural R emote Sensing Monitoring. Zhou, QB, Yu, QY, Liu, J, Wu, WB, Tang, HJ. 2017

[14]An Assessment Of The Spatial And Temporal Distribution Of Soil Salinity In Combination With Field And Satellite Data: A Case Study In Sujawal District. Solangi, KA, Siyal, AA, Wu, YY, Abbasi, B, Solangi, F, Lakhiar, IA, Zhou, GY. 2019

[15]Distributed Correlation Model Mining From R.emote Sensing Big Data B ased On Gene Expression Programming. Yang, LC, Qin, ZH. 2018

[16]Estimation Of Daily Evapotranspiration Using I.nstantaneous Decoupling Coefficient From T he Modis And Field Data. Jiang, YZ, Jiang, XG, Tang, RL, Li, ZL, Zhang, YZ, Huang, C, Ru, C. 2018

[17]Comparative Study On Crop Recognition O.f Landsat-Oli And Rapideye D ata. Liu, J, Wang, LM, Yao, BM, Yang, FG, Yang, LB, Dong, QH. 2017

[18]Assessing The Effect Of Desertification C.ontrolling Projects And Policies I n Northern Shaanxi Province, China By Integrating Remote Sensing And Farmer Investigation Data. Xu, DY, Song, AL, Song, X. 2017

[19]The Estimation Of Crop Emergence I.n Potatoes By Uav R gb Imagery. Li, B, Xu, XM, Han, JW, Zhang, L, Bian, CS, Jin, LP, Liu, JG. 2019

[20]Net Surface Shortwave Radiation Retrieval U.sing Random Forest Method W ith Modis/Aqua Data. Ying, WM, Wu, H, Li, ZL. 2019

作者其他论文 更多>>