Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products – a case study in the Hunan province of China. Issue 19 (2nd October 2018)

• Record Type: Journal Article
• Authors: Wang, Chenzhi; Zhang, Zhao; Chen, Yi; Tao, Fulu; Zhang, Jing; Zhang, Wen
• Is Part Of: International journal of remote sensing. Volume 39:Issue 19(2018)
• Page Start: 6405
• Page End: 6428
• Publication Date: 2018-10-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1460504
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Many studies have demonstrated the remarkable potential of assimilating remotely sensing leaf area index (LAI) products into crop models in estimating regional crop yield. To ensure the temporal consistency between crop models and remote-sensing system, it is prerequisite to derive the crop phenology information from the LAI products. However, previous studies mainly detected the phenology through the vegetation index (VI). Although some pieces of research applied LAI in phenology monitoring for trees and shrubs, fewer focused on crops, especially those with two or three growing seasons annually. Thus, which smoothing algorithm methods are suitable to obtain phenology of double-cropping rice and their difference in smoothing for crops are still unknown. Based on the Global Land Surface Satellite (GLASS)LAI products, we applied four favourite smoothing algorithms (Asymmetric Gaussian fitting, Double Logistic fitting, Savitzky–Golay filter, and Wavelet-based Filter method) to reduce noise and reconstruct the LAI profile and then detected the phenological information of double-cropping rice in Hunan Province. Compared with ground actual observations, we found that two fitting methods are not suitable to smooth double-cropping rice LAI, while the wavelet method performed the best. Based on the wavelet method, we estimated the phenological information of double-cropping rice at different regional scales as well and the results reflected that the accuracy of regional estimation is also acceptable. This study implied that the wavelet method is rather suitable to detect phenological information of crops from LAI products, which provides narrow gaps between two growing season. Our contribution can benefit researchers who focus on agriculture or remote sensing, especially those who would like to assimilate remotely sensed information into crop growth models.