Rice flooding disaster diagnosis analysis and growth monitoring based on HJ-CCD data

It has great significance to study quick monitoring of rice flood disaster and applying timely remedial measures in the disaster area.LAI is a very important physiological parameter in crop growth characterization index, which can reflect the crop growing information objectively.The existing methods of flood monitoring using remote sensing technology rarely consider the damage and the post disaster growth of rice.The HJ-CCD data take advantage of high temporal resolution and high spatial resolution remote sensing image, which can be used for gathering rice growing information during the critical period.The growth situation after rice flood disaster in Anhui Province was monitored using 3 screens HJ-CCD data as the data source on 16th July, 19th August, 26th August, 2009, respectively.The semi-empirical function model based on Beer-Lambert laws was constructed for this inversion LAI.And LAI were acquired in each stage after flood disaster, the trend of growth diagnosis dynamic change was analyzed and assessed by rice flood disaster evaluation indicator.At the same time, the 40 field investigate data were used to verify the model and the R2=0.4251, RMSE=2.053.The results show that LAI can be well evaluated the degree of rice flood disaster growth based on HJ-CCD data, and it is effective for monitoring and diagnosing rice flood disaster.The results provide a theoretical basis for rice flood disaster research, post disaster rehabilitation and recovery, and provide a theoretical basis for the implementation of targeted remedial measures at the same time.