邬明权等：Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

作者:Wu, MQ (Wu, Mingquan)[ 1,2 ] ; Yang, CH (Yang, Chenghai)[ 2 ] ; Song, XY (Song, Xiaoyu)[ 2,3 ] ; Hoffmann, WC (Hoffmann, Wesley Clint)[ 2 ] ; Huang, WJ (Huang, Wenjiang)[ 4 ] ; Niu, Z (Niu, Zheng)[ 1 ] ; Wang, CY (Wang, Changyao)[ 1 ] ; Li, W (Li, Wang)[ 1 ] ; Yu, B (Yu, Bo)[ 4 ]

SCIENTIFIC REPORTS

卷: 8

文献号: 2016

DOI: 10.1038/s41598-018-20156-z

出版年: JAN 31 2018

摘要

To better understand the progression of cotton root rot within the season, time series monitoring is required. In this study, an improved spatial and temporal data fusion approach (ISTDFA) was employed to combine 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Different Vegetation Index (NDVI) and 10-m Sentinetl-2 NDVI data to generate a synthetic Sentinel-2 NDVI time series for monitoring this disease. Then, the phenology of healthy cotton and infected cotton was modeled using a logistic model. Finally, several phenology parameters, including the onset day of greenness minimum (OGM), growing season length (GLS), onset of greenness increase (OGI), max NDVI value, and integral area of the phenology curve, were calculated. The results showed that ISTDFA could be used to combine time series MODIS and Sentinel-2 NDVI data with a correlation coefficient of 0.893. The logistic model could describe the phenology curves with R-squared values from 0.791 to 0.969. Moreover, the phenology curve of infected cotton showed a significant difference from that of healthy cotton. The max NDVI value, OGM, GSL and the integral area of the phenology curve for infected cotton were reduced by 0.045, 30 days, 22 days, and 18.54%, respectively, compared with those for healthy cotton.

通讯作者地址: Yang, CH (通讯作者)

USDA ARS, Aerial Applicat Technol Res Unit, 3103 F&B Rd, College Stn, TX 77845 USA.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, POB 9718,20 Datun Rd, Beijing 100101, Peoples R China

[ 2 ] USDA ARS, Aerial Applicat Technol Res Unit, 3103 F&B Rd, College Stn, TX 77845 USA

[ 3 ] Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

[ 4 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Lab Digital Earth Sci, Beijing 100094, Peoples R China