数字农科院2.0

Improvement Of Sugarcane Yield Estimation By Assimilating Uav-Derived Plant Height Observations

文献类型： 外文期刊

作者： Yu, DY; Zha, YY; Shi, LS; Jin, XL; Hu, S; Yang, Q; Huang, K; Zeng, WZ

作者机构：

关键词： Yield Estimation; Sugarcane Growth; Plant Height; Data Assimilation; Uav

期刊名称： EUROPEAN JOURNAL OF AGRONOMY

ISSN： 1161-0301

年卷期： 2020 年 121 卷

页码：

收录情况： JCR（2021版）

摘要： Crop yield estimation plays a significant role in agricultural development and management decision making. Simulation of crop growth at field scales requires high-resolution remote sensing (RS) data and an applicable model. However, RS data based on satellite sensors are affected by atmospheric interference, limiting its use in precision agriculture. The objective of this study is to assimilate plant height data into the crop model to improve yield estimation and optimize agricultural water management. The plant height data are monitored regularly in 60 plots at Chongzuo experimental station (Guangxi, China) from an Unmanned Aerial Vehicle (UAV) system and ground-based measurements through a two-year sugarcane experiment (2016 and 2017). A SWAP-PH model (i. e., SWAP model considering plant height simulation) coupled with an iterative ensemble smoother algorithm is constructed to simulate plant height and crop growth procedure. We analyze its performance under the effects of different observation errors, ensemble sizes and development stages on crop growth and yield estimation, and compare the plant height assimilation results from two types of observation platforms. Our results reveal that the crop growth simulation at field scales is improved by incorporating the plant height data into the model. Moreover, incorporating the plant height measurements in the late growth stage with an absolute error of 1-2 cm and ensemble size over 50 could achieve an acceptable crop yield estimation result. In addition, assimilating the UAV-derived measurements could contribute to better yield estimation results than ground-measured plant height data. Finally, we propose a supplemental irrigation and drainage strategy in July and August to maximize the sugarcane yield in Guangxi region.

分类号：

  • 相关文献

[1]Towards improved accuracy of UAV-based wheat ears counting: a transfer learning method of the ground-based fully convolutional network. Juncheng Ma,Yunxia Li,Hongjie Liu,Yongfeng Wu1,Lingxian Zhang. 2021

[2]Finer Classification Of Crops By Fusing Uav Images And Sentinel-2A Data. Zhao, LC, Shi, Y, Liu, B, Hovis, C, Duan, YL, Shi, ZC. 2019

[3]Time-Series Multispectral Indices From Unmanned A.erial Vehicle Imagery Reveal S enescence Rate In Bread Wheat. Hassan, MA, Yang, MJ, Rasheed, A, Jin, XL, Xia, XC, Xiao, YG, He, ZH. 2018

[4]Assessment Of Water And Nitrogen Use Efficiencies Through Uav-Based Multispectral Phenotyping In Winter Wheat. Yang, MJ, Hassan, MA, Xu, KJ, Zheng, CY, Rasheed, A, Zhang, Y, Jin, XL, Xia, XC, Xiao, YG, He, ZH. 2020

[5]Establishment of a high-throughput ffeld defoliation data survey strategy combined with genome-wide association studies to reveal the genetic basis of defoliation in cotton. . 2025

[6]YOLO-light-pruned: A lightweight model for monitoring maize seedling count and leaf age using near-ground and UAV RGB images. Tiantian Jiang,Liang Li,Zhen Zhang,Xun Yu,Yanqin Zhu,Liming Li,Yadong Liu,YaliBai, Ziqian Tang , Shuaibing Liu,Yan Zhang,Zheng Duan,Dameng Yin,Xiuliang Jin. 2025

[7]Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Gossypium hirsutum).. . 2025

[8]Screening Verticillium wilt-resistant germplasm by monitoring the time-series chlorophyll content of cotton canopies via a UAV-based high-throughput platform. . 2025

[9]Estimation Of Maize Yield By Assimilating Biomass And Canopy Cover Derived From Hyperspectral Data Into The Aquacrop Model. Jin, Xiuliang,Li, Shaokun,Jin, Xiuliang,Li, Zhenhai,Feng, Haikuan,Ren, Zhibin. 2020

[10]Editorial For The Special Issue "Estimation Of Crop Phenotyping Traits Using Unmanned Ground Vehicle And Unmanned Aerial Vehicle Imagery". Atzberger, Clement,Jin, Xiuliang,Jin, Xiuliang,Li, Zhenhai. 2020

[11]Assimilation Of Remote Sensing Into Crop Growth Models: Current Status And Perspectives. Huang, JX, Gomez-Dans, JL, Huang, H, Ma, HY, Wu, QL, Lewis, PE, Liang, SL, Chen, ZX, Xue, JH, Wu, YT, Zhao, F, Wang, J, Xie, XH. 2019

[12]Joint Assimilation Of Leaf Area I.ndex And Soil Moisture F rom Sentinel-1 And Sentinel-2 Data Into The Wofost Model For Winter Wheat Yield Estimation. Pan, HZ, Chen, ZX, de Wit, A, Ren, JQ. 2019

[13]Assimilation Of Temporal-Spatial Leaf Area I.ndex Into The Ceres-Wheat M odel With Ensemble Kalman Filter And Uncertainty Assessment For Improving Winter Wheat Yield Estimation. Li, H, Jiang, ZW, Chen, ZX, Ren, JQ, Liu, B, Hasituyu. 2017

[14]Estimating winter wheat yield by assimilation of remote sensing data with a four-dimensional variation algorithm considering anisotropic background error and time window. Shangrong Wu,Peng Yang,Zhongxin Chen,Jianqiang Ren,He Li,Liang Sun. 2021

[15]Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imageryʺ . Jin, XL, Li, ZH, Atzberger, C. 2020

[16]Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm. Shangrong Wu,Peng Yang,Jianqiang Ren,Zhongxin Chen,He Li. 2021

[17]Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model. . 2019

[18]Application of Crop Model Data Assimilation With a Particle Filter for Estimating Regional Winter Wheat Yields. Jiang, Zhiwei,Chen, Jin,Jiang, Zhiwei,Chen, Zhongxin,Liu, Jia,Ren, Jianqiang,Li, Zongnan,Sun, Liang,Li, He. 2014

[19]ASSIMILATION OF FIELD MEASURED LAI INTO CROP GROWTH MODEL BASED ON SCE-UA OPTIMIZATION ALGORITHM. Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Ren, Jianqiang,Yu, Fushui,Chen, Zhongxin,Du, Yunyan,Qin, Jun. 2009

[20]The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation. Jiang, Zhiwei,Chen, Jin,Jiang, Zhiwei,Chen, Zhongxin,Ren, Jianqiang,Li, Zongnan,Sun, Liang,Jiang, Zhiwei,Chen, Zhongxin,Ren, Jianqiang,Li, Zongnan,Sun, Liang. 2014

作者其他论文 更多>>