Remote Sensing Applications and Decision Support

Regional-scale winter wheat phenology monitoring using multisensor spatio-temporal fusion in a South Central China growing area

[+] Author Affiliations
Shishi Liu

Huazhong Agricultural University, College of Resources and Environment, Wuhan 430070, China

Wenhua Zhao, Huanfeng Shen

Wuhan University, School of Resource and Environmental Science, Wuhan 430079, China

Liangpei Zhang

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

J. Appl. Remote Sens. 10(4), 046029 (Dec 29, 2016). doi:10.1117/1.JRS.10.046029
History: Received March 3, 2016; Accepted December 9, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Topics

MODIS ; Vegetation ; Landsat

Citation

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", J. Appl. Remote Sens. 10(4), 046029 (Dec 29, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046029


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