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Identification of vegetation responses to soil moisture, rainfall, and LULC over different meteorological subdivisions in India using remote sensing data

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Abstract

Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were analyzed to study the vegetation dynamics over different meteorological subdivisions in India for the period 2000–2016. Soil moisture (SM), rainfall (RF), and land use land cover (LULC) data were analyzed to identify the climatic and anthropogenic drivers that cause vegetation changes at the subdivision scale. Principal component analysis and MK (Mann-Kendall) test showed significant greening trend over semi-arid regions of Northwest India (NWI) and South India (SI) while slight browning trend seen over some of the subdivisions in Indo-Gangetic (IG) plains and Western Ghats (WG). It is found that the NDVI has superior correlation with soil moisture compared with rainfall and the croplands (CL) found to have significant increasing trend over the NWI and SI. Increasing trend in soil moisture over the NWI and SI may have contributed to increase in CL area and the greening trend. Over IG plains, the NDVI showed moderate correlation with SM and RF, and the greening trend (browning trend) in some regions can be attributed to increase in natural vegetation mosaic (decrease of CL). The NDVI has shown browning trend over the core monsoon regions of Madhya Pradesh (an increase of barren lands over west MP and decrease of CL over east MP) and Western Ghats (significant decrease of CL over Konkan and Goa). This study revealed that the soil moisture and LULC changes are the major driving factors for the vegetation changes over majority of the subdivisions in India.

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Acknowledgments

The authors are grateful to the Integrated Climate Data Center (ICDC, icdc.cen.uni-hamburg.de), University of Hamburg, Germany, for providing the NDVI and LULC data. The authors acknowledge the National Aeronautics and Space Administration (NASA) for providing the TRMM rainfall data (https://pmm.nasa.gov/data-access/downloads/trmm). The authors also acknowledge the support and encouragement of the Head CSIR 4PI.

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  1. CSIR Fourth Paradigm Institute (CSIR-4PI), Wind Tunnel Road, Bangalore, 560037, India

    Kantha Rao Bhimala, V. Rakesh, K. Raghavendra Prasad & G. N. Mohapatra

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

    Kantha Rao Bhimala, V. Rakesh, K. Raghavendra Prasad & G. N. Mohapatra

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  1. Kantha Rao Bhimala
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Bhimala, K.R., Rakesh, V., Prasad, K.R. et al. Identification of vegetation responses to soil moisture, rainfall, and LULC over different meteorological subdivisions in India using remote sensing data. Theor Appl Climatol 142, 987–1001 (2020). https://doi.org/10.1007/s00704-020-03360-8

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