Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India

Authors

Sneha S. Kulkarni, Indian Institute of Technology-Bombay, Powai, Mumbai & University of Nebraska-LincolnFollow
Brian D. Wardlow, University of Nebraska - LincolnFollow
Yared Bayissa, University of Nebraska - LincolnFollow
Tsegaye Tadesse, University of Nebraska-LincolnFollow
Mark D. Svoboda, National Drought Mitigation Center/University of Nebraska-LincolnFollow
Shirishkumar S. Gedam, Indian Institute of Technology-Bombay, PowaFollow

ORCID IDs

https://orcid.org/0000-0002-4767-581X

https://orcid.org/0000-0002-4102-1137

https://orcid.org/0000-0001-7926-0742

Date of this Version

2020

Citation

Remote Sens. 2020, 12, 2091; doi:10.3390/rs12132091 www.mdpi.com/journal/remotesensing

Comments

2020 by the authors.

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.