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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #341621
Title: Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

Author
item WU, MINGQUAN - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences
item Yang, Chenghai
item SONG, XIAOYU - Beijing Academy Of Agricultural Sciences
item Hoffmann, Wesley
item HUANG, WENJIANG - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences
item NIU, ZHENG - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences
item WANG, CHANGYAO - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences
item WANG, LI - Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/12/2018
Publication Date: 1/31/2018
Citation: Wu, M., Yang, C., Song, X., Hoffmann, W.C., Huang, W., Niu, Z., Wang, C., Wang, L. 2018. Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion. Scientific Reports. 8:1-12.

Interpretive Summary: Airborne imagery has been successfully used for mapping cotton root rot within cotton fields. To better understand the progression of cotton root rot over a large geographic region within the season, this study combined 60 satellite images with 250-m spatial resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite with three satellite images with 10-m spatial resolution from the high resolution Sentinel-2A sensor to generate time series images for monitoring cotton root rot. The phenology of healthy cotton and infected cotton was accurately modeled using the time series image data. The data fusion method proposed in this study will be useful to obtain high resolution time series image data for monitoring the progression of cotton root rot within growing seasons.

Technical Abstract: Airborne imagery has been successfully used for mapping cotton root rot within cotton fields toward the end of the growing season. 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 Sentinel-2 NDVI data to generate a synthetic 10-m 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 for generating synthetic Sentinel-2 NDVI with a correlation coefficient of 0.893. The logistic model could describe the phenology curves of healthy cotton and infected cotton at different greenness phases 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.