Application study on drought monitoring with time-series NOAA/AVHRR data

Xiaoxiang Zhu; Bolin Zhao; Yuanjing Zhu; Wenjian Zhang; Yeping Zhang; Ruixia Liu

doi:10.1117/12.694010

15 December 2006 Application study on drought monitoring with time-series NOAA/AVHRR data

Xiaoxiang Zhu, Bolin Zhao, Yuanjing Zhu, Wenjian Zhang, Yeping Zhang, Ruixia Liu

Proceedings Volume 6412, Disaster Forewarning Diagnostic Methods and Management; 641207 (2006) https://doi.org/10.1117/12.694010
Event: SPIE Asia-Pacific Remote Sensing, 2006, Goa, India

Abstract

In this paper, 30 years conventional data of China are processed, the anomaly of precipitation, land surface temperature and air temperature are calculated and their relations are analyzed by using regressive statistics analysis and Singular Value Decomposition (SVD). The result shows that precipitation anomaly has a good negative correlation to both surface temperature anomaly and air temperature anomaly. Moreover, 20 years satellite brightness temperature anomaly and the same period precipitation anomaly are also calculated and analyzed; the similar result is obtained. It indicates that brightness temperature anomaly is an important factor for drought monitoring by using remote sensing data. Moreover, compared with historic data, the change of Normalized Difference Vegetation Index (NDVI) is another factor for drought monitoring. Drought index is formed by these two factors normalization and mean in weight. This remote sensing method on drought was used to some experiments and the results show that the drought distribution on space is very similar, compared with conventional drought index. Now this method is being used in operational system on drought monitoring in National Satellite Meteorological Center (NSMC), China meteorology administration (CMA).

Citation Download Citation

Xiaoxiang Zhu, Bolin Zhao, Yuanjing Zhu, Wenjian Zhang, Yeping Zhang, and Ruixia Liu "Application study on drought monitoring with time-series NOAA/AVHRR data", Proc. SPIE 6412, Disaster Forewarning Diagnostic Methods and Management, 641207 (15 December 2006); https://doi.org/10.1117/12.694010

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