Background: The Agricultural drought during kharif seasons of the year 2019 to 2023 in Tamil Nadu state in India was analyzed in using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Monthly precipitation data from 1991 to 2023 were obtained for the study, with the main objective of evaluating the duration, spatial extent, severity and lag time of meteorological and agricultural drought in the study area.&nbsp; 
 
Methods:&nbsp;The Enhanced Vegetation Index (EVI) was generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) data. The pixel reliability layer was introduced based on the calculation of the cloud coverage at the moment of image acquisition. The Standardized Precipitation Index SPI in one month time scale plays very significant role in any vulnerability studies for accurate prediction of any events. 
 
Result: In the present study, the EVI for Kharif season was considered for five years i.e., 2019-2023 and it was correlated with the SPI at various time scales. The correlation coefficient of SPI was 0.39 with 0.1% level of significance, in other words, EVI 2022 was well correlated with SPI-6. Also, the relation between EVI and precipitation with 9 months interval was also tested. The 12 months interval of precipitation have more stress on vegetation and it can negatively impact the agriculture activities leading to crop failures.

Assessment of Agricultural Drought in Tamil Nadu using Remote Sensing Techniques

Background: Precision farming has significantly advanced the agricultural sector by enabling real-time canopy monitoring and crop condition evaluation, thus facilitating precise management strategies to enhance yields. This study investigated the use of drone-derived vegetation indices (VIs) for assessing spatial variability in crop conditions, offering a more cost effective and practical alternative to satellite data.&nbsp; 
 
Methods: The field experiment was conducted during the Kuruvai season (July - November 2023) on a short-duration rice variety CO 55. Several vegetation indices viz., BGI, CI, EVI, GNDVI, MCARI, MSAVI, NDRE and NDVI were calculated to predict chlorophyll content and correlated with the measured SPAD values.&nbsp; 
 
Result: The results showed that indices like MCARI, GNDVI, and NDVI had strong positive correlations with SPAD values, with MCARI exhibiting the highest correlation (R = 0.914) and an R&sup2; value of 0.836. The findings underscore the effectiveness of using drone-derived indices for precise chlorophyll estimation, which is crucial for variable rate fertilizer application in precision agriculture.

Assessment of Agricultural Drought in Tamil Nadu using Remote Sensing Techniques

Spatial Chlorophyll Estimation from Rice Field using Drone-derived Spectral Indices