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[1]LU Zhong,LEI Guoping,MA Quanlai,et al.Regional Drought Monitoring Based on Reconstructed Landsat 8 Data and Temperature Vegetation Index[J].Research of Soil and Water Conservation,2018,25(05):371-377,384.

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Regional Drought Monitoring Based on Reconstructed Landsat 8 Data and Temperature Vegetation Index

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 25 Number of periods: 2018 05 Page number: 371-377,384 Column: Public date: 2018-09-06

Title:: Regional Drought Monitoring Based on Reconstructed Landsat 8 Data and Temperature Vegetation Index

Author(s):: LU Zhong¹, LEI Guoping^1,2, MA Quanlai¹, GUO Jingpeng¹, WANG Juwu¹; 1. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China;
2. Land Management Institute, Northeastern University, Shenyang 110004, China

Keywords:: S-G filter; Landsat 8 time series data; soil moisture; regional drought monitoring

CLC:: TP79

DOI:: -

Abstract:: In order to estimate the range of large area quickly and accurately in the soil moisture information, realize the monitoring of regional drought in northern Nenjiang, based on the time series data of Landsat 8, we calculated the normalized difference vegetation index (NDVI) time series data and surface temperature index (LST) time series data. The Savitzky-Golay (S-G) filter was used to reconstruct the time series data to compensate for the noise caused by cloud and atmosphere. According to the reconstructed NDVI and LST data, the temperature vegetation index (TVDI) was calculated; on the basis of the relationship between TDVI and soil moisture, soil moisture inversion model was constructed. Finally, combining with the local field data verified the accuracy of the model of soil moisture. The results show that:(1) S-G filtering can effectively compensate for the defects caused by cloud, and improve the quality of Landsat 8 time series data; (2) the temperature vegetation drought index can reflect the soil moisture data effectively; after S-G filtering data, the inversion accuracy of soil moisture is higher(RMSE=2.43%); (3) drought monitoring after S-G filtering Landsat 8 time series data can be achieved within the study area, and provide reference for regional drought monitoring.

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