Developing a remote sensing-based combined drought indicator approach for agricultural drought monitoring over Marathwada, India

Sneha S. Kulkarni, Brian D. Wardlow, Yared A. Bayissa, Tsegaye Tadesse, Mark D. Svoboda, Shirishkumar S. Gedam

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015-2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001-2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r > 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.

Original languageEnglish (US)
Article number2091
JournalRemote Sensing
Volume12
Issue number13
DOIs
StatePublished - Jul 1 2020

Keywords

  • CDI_M
  • Drought
  • Drought monitoring
  • LST
  • NDVI
  • PCA
  • SPI
  • SSI

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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