Time-series analysis of MODIS (LST and NDVI) and TRMM rainfall for drought assessment over India

被引：9
作者：
Thanabalan, P. [1 ,2 ]
Vidhya, R. [1 ]
Kankara, R. S. [2 ]
Manonmani, R. [1 ]
机构：
[1] Anna Univ, Inst Remote Sensing, Dept Civil Engn, Chennai 600025, Tamilnadu, India
[2] Minist Earth Sci, Natl Ctr Coastal Res, NIOT Campus, Chennai 600100, Tamilnadu, India
来源：
APPLIED GEOMATICS | 2023年 / 15卷 / 02期
关键词：
MODIS LST and NDVI; TRMM-rainfall; Time-lag correlation; LSNR model; Drought assessment; AGRICULTURAL DROUGHT; PRECIPITATION; TEMPERATURE; BIOMASS; WATER;
D O I：
10.1007/s12518-023-00505-y
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
In this study, an attempt has been made using rainfall, LST, and NDVI combination of LSNR model which is used to infer drought condition in different monsoon period and to predict the seasonal changes of drought condition. The Indian monsoon pattern with different seasonal changes has been studied for the year 2009 to 2013 using optical and passive remote sensing data, and cross correlation with different time lag is carried out. The cross correlation between LST and NDVI time-lag deviation responses describe that May month LST having influence with September NDVI (90 days before onset) in other words 2-3 months. The correlation performed with a combination of rainfall and NDVI are not at significant level. The passive Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture data also clearly examined the drought and normal years, as the soil moisture is highly sensitive to rainfall and temperature to assess drought condition. The relationship between Tropical Rainfall Meteorological Mission (TRMM) rainfall records is compared with observed Indian Meteorological Department (IMD) datasets for the same time period to confirm the drought severity. This will help in being prepared for the drought condition well before it actually sets in and is useful for planner in agricultural operations.
引用
收藏
页码：383 / 405
页数：23
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