29 December 2016 Regional-scale winter wheat phenology monitoring using multisensor spatio-temporal fusion in a South Central China growing area
Shishi Liu, Wenhua Zhao, Huanfeng Shen, Liangpei Zhang
Author Affiliations +
Abstract
Monitoring crop phenology is essential for evaluating crop productivity and crop management. Remote sensing provides an efficient way to monitor crop phenological metrics at a large-scale. However, the widely used AVHRR and MODIS images are less reliable at a small-scale and in areas with heterogeneous land covers, such as the patchy cropland in South Central China. Therefore, this study analyzed the spatial and temporal variations of winter wheat phenology in South Central China, using enhanced vegetation index (EVI) time series predicted by a spatio-temporal fusion method that combines information from Landsat and MODIS images. The 13-year predicted EVI showed a close correspondence with the EVI derived from original Landsat images. Start of season (SOS), peak greenness, and end of season (EOS) were derived from the predicted EVI time series. The comparison with ground observations showed that the differences between the predicted phenological metrics and observations were usually within seven days. The length of the growing season demonstrated high spatial heterogeneity over the study area and the spatial patterns varied from year to year. The phenological dates did not show obvious increasing or decreasing trends through 13 years. The length of the growing season in the study area was positively correlated with precipitation, but the duration from SOS to peak greenness and the duration from peak greenness to EOS were strongly and negatively correlated with hours of sunshine.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2016/$25.00 © 2016 SPIE
Shishi Liu, Wenhua Zhao, Huanfeng Shen, and Liangpei Zhang "Regional-scale winter wheat phenology monitoring using multisensor spatio-temporal fusion in a South Central China growing area," Journal of Applied Remote Sensing 10(4), 046029 (29 December 2016). https://doi.org/10.1117/1.JRS.10.046029
Received: 3 March 2016; Accepted: 9 December 2016; Published: 29 December 2016
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CITATIONS
Cited by 11 scholarly publications.
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KEYWORDS
Earth observing sensors

Landsat

Image fusion

MODIS

Data fusion

Vegetation

Meteorology

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