Where, c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, c3 = 0.010328, d1 =
1.4328, d2 = 0.1893, and d3 = 0.00131. An EDDI of 0 on any day of the
year during a specific period has a median temperature value of 0.
Negative Evaporative Demand Drought Index (EDDI) states have more
moisture, whereas positive EDDI states have less moisture, resulting in
dry circumstances. Thus, the EDDI value rises with drought severity.
EDDI variability depends on the length of data collection. For n = 30,
values vary from -2 to +2.
Crop Moisture Index (CMI)
CMI assesses weekly crop conditions using precipitation and temperature
data. As a short-term index, CMI provides valuable insights into rapidly
changing crop moisture conditions (Palmer, 1968). CMI is particularly
useful in agricultural settings, as it assesses short-term crop moisture
conditions and is sensitive to weekly changes. The Crop Moisture Index
(CMI) assesses weekly crop conditions using hydrological parameters.
Palmer (1968) derived it from PDSI calculating algorithms. CMI value
ranges from -3.0 (dry conditions harmful to crops) to +3.0 (excessively
wet conditions).
Normalized Difference Vegetation Index
(NDVI)
NDVI uses satellite imagery to assess vegetation health by measuring the
difference and sum of near-infrared (NIR) and red light (R) reflectance.
While NDVI was calculated for this study, it was excluded from the
correlation analysis due to its sensitivity to cloud cover, which
introduces noise in time series data. NDVI values, ranging from -1.0 to
+1.0, were derived from MODIS satellite data using standard
preprocessing steps (Rouse Jr et al., 1974).
The NDVI utilizes satellite imagery to assess vegetation health by
measuring the difference and sum of near-infrared (NIR) and red light
(R) reflected by vegetation (Rouse Jr et al., 1974). MODIS MOD13Q1 data
was used for the index quantification. They were put through five steps,
one after the other: (a) mosaicking, (b) projecting the tiles, (c)
raster clipping based on the study area using ArcGIS, (d) resampling
based on the other raster file to make sure the analysis would work, and
finally (e) masking. NDVI is computed using the following formula: