Monitoring the Plant Density of Cotton with Remotely Sensed Data

Abstract : PDC (Plant Density of Cotton) was an essential parameter for estimating the cotton yield and developing the zone-management measurements. This paper proposed a new method to retrieve PDC from the satellite remote sensing data. The thirteen fields of Xinjiang Production and Construction Corps (XPCC) (total 630 hm2) were selected as the study area, where the sowing date, emergence date, and PDC were investigated. Based on the investigation data the linear models to estimate PDC are established using EVI and DEVI respectively. The results indicated that the difference of seedling size caused by the emergence time decreased the estimation accuracy of PDC. To improve the estimation accuracy the partition functions were established in terms of sowing date. DEVI is capable of reducing the influence of soil background significantly and it can bring the monitoring time forward from June 9th to May 24th in this research. The results indicated that the optimal time monitoring PDC would be from squaring to full-flowering of cotton growing period. A demonstration to monitor PDC was taken on June 9th in the 148th farm of XPCC. It can be concluded that the emergence time and the non-cotton background were the main factors affecting the monitoring accuracy of PDC, and the partition function with the emergence time could improve the estimation accuracy, and DEVI could make the monitoring time forward, and the optimal monitoring time was from the squaring stage to the full-flowering stage. This research provides an efficient, rapid and intact way to monitor PDC, and it is significant for operational application at a regional scale.
Type de document :
Communication dans un congrès
Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-345 (Part II), pp.90-101, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18336-2_11〉
Liste complète des métadonnées

Littérature citée [22 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01562789
Contributeur : Hal Ifip <>
Soumis le : lundi 17 juillet 2017 - 09:50:38
Dernière modification le : mardi 18 juillet 2017 - 15:37:11

Fichier

978-3-642-18336-2_11_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Junhua Bai, Jing Li, Shaokun Li. Monitoring the Plant Density of Cotton with Remotely Sensed Data. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-345 (Part II), pp.90-101, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18336-2_11〉. 〈hal-01562789〉

Partager

Métriques

Consultations de la notice

25

Téléchargements de fichiers

12