Appraisal of long-term responsiveness of normalized difference vegetation index to climatic factors using multiscale time–frequency decomposition in an arid environment

Abstract

An arid climate is a unique condition that has a significant impact on the growth of crops and natural vegetation. The normalized difference vegetation index (NDVI) is a crucial remotely sensed measurement of greenness due to its strong correlation with crop and vegetation growth and productivity. In the present study, the spatiotemporal dynamics of NDVI were analyzed from 2000 to 2021 in the segment of the arid western plain zone of Rajasthan, India. NDVI time-series data, as well as data related to climatic factors, viz., precipitation, soil moisture, evapotranspiration, and 2-m air temperature, were collected from Giovanni, the Goddard Earth Science dataset. The Mann–Kendall (MK) trend test and Sen’s slope depicted the long-term continuous time–frequency trend, while Karl Pearson’s correlation analysis depicted the significant relationship between all the factors except 2-m air temperature. The seasonal and mean monthly results of all the factors except 2-m air temperature showed considerable coherence with NDVI. The multiscale time–frequency decomposition or wavelet analysis depicted the fifth to the seventh month and the ninth to the 15th month of the cycle, showing the significance of the cropping pattern and the natural vegetation growth cycle. The cross-wavelet analysis further depicted important coherence, leading, and lagging phases among climatic factors and NDVI. Our research provided significant insights into the long-term variability and coherence of various climatic factors with NDVI that are applicable on regional and global scales.