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A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data

1,2,3, 1,2,*, 2, 1,3, 4 and 4,5
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, W. Beichen Road, Beijing 100101, China
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geoscience and Infophysics, Central South University, Changsha 410083, China
National Disaster Reduction Center of China, Beijing 100124, China
China Transport Telecommunications and Information Center, Beijing 100011, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1297;
Received: 1 March 2018 / Revised: 11 April 2018 / Accepted: 20 April 2018 / Published: 23 April 2018
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
PDF [24186 KB, uploaded 3 May 2018]


In this study, modified perpendicular drought index (MPDI) models based on the red-near infrared spectral space are established for the first time through the analysis of the spectral characteristics of GF-1 wide field view (WFV) data, with a high spatial resolution of 16 m and the highest frequency as high as once every 4 days. GF-1 data was from the Chinese-made, new-generation high-resolution GF-1 remote sensing satellites. Soil-type spatial data are introduced for simulating soil lines in different soil types for reducing errors of using same soil line. Multiple vegetation indices are employed to analyze the response to the MPDI models. Relative soil moisture content (RSMC) and precipitation data acquired at selected stations are used to optimize the drought models, and the best one is the Two-band enhanced vegetation index (EVI2)-based MPDI model. The crop area that was statistically significantly affected by drought from a local governmental department, and used for validation. High correlations and small differences in drought-affected crop area was detected between the field observation data from the local governmental department and the EVI2-based MPDI results. The percentage of bias is between −21.8% and 14.7% in five sub-areas, with an accuracy above 95% when evaluating the performance via the data for the whole study region. Generally the proposed EVI2-based MPDI for GF-1 WFV data has great potential for reliably monitoring crop drought at a relatively high frequency and spatial scale. Currently there is almost no drought model based on GF-1 data, a full exploitation of the advantages of GF-1 satellite data and further improvement of the capacity to observe ground surface objects can provide high temporal and spatial resolution data source for refined monitoring of crop droughts. View Full-Text
Keywords: crop drought; EVI2-based MPDI; GF-1 WFV data; relative soil water content; FVC crop drought; EVI2-based MPDI; GF-1 WFV data; relative soil water content; FVC

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Chang, S.; Wu, B.; Yan, N.; Zhu, J.; Wen, Q.; Xu, F. A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data. Sensors 2018, 18, 1297.

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