Article (Scientific journals)
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 et al.
2019In Remote Sensing, 11 (9), p. 1119
Peer Reviewed verified by ORBi
 

Files


Full Text
remotesensing-11-01119.pdf
Publisher postprint (6.76 MB) Creative Commons License - Attribution
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
Assimilation; Jujube yield estimation; Leaf area index; WOFOST model; Above ground biomass; Leaf Area Index; Normalized difference vegetation index; Prediction accuracy; Vegetative development; Wofost models; Yield estimation; Earth and Planetary Sciences (all); General Earth and Planetary Sciences
Abstract :
[en] 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.
Disciplines :
Computer science
Agriculture & agronomy
Author, co-author :
Bai, Tiecheng;  Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer, China ; TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Gembloux, Belgium
Zhang, Nannan;  Southern Xinjiang Research Center for Information Technology in Agriculture, Tarim University, Alaer, China
Mercatoris, Benoît  ;  Université de Liège - ULiège > Ingénierie des biosystèmes (Biose) > Biosystems Dynamics and Exchanges (BIODYNE)
Chen, Youqi;  Institute of Agricultural Resources and Regional Planning of CAAS, Haidian District, Beijing, China
Language :
English
Title :
Improving jujube fruit tree yield estimation at the field scale by assimilating a single Landsat remotely-sensed LAI into the WOFOST model
Publication date :
May 2019
Journal title :
Remote Sensing
eISSN :
2072-4292
Publisher :
MDPI AG, Basel, Che
Volume :
11
Issue :
9
Pages :
1119
Peer reviewed :
Peer Reviewed verified by ORBi
Funding text :
Funding: This research was funded by National Natural Science Foundation of China (41561088 and 61501314) and Science and Technology Nova Program of Xinjiang Production and Construction Corps (2018CB020).
Available on ORBi :
since 30 June 2023

Statistics


Number of views
8 (1 by ULiège)
Number of downloads
20 (0 by ULiège)

Scopus citations®
 
14
Scopus citations®
without self-citations
12
OpenCitations
 
8

Bibliography


Similar publications



Contact ORBi