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Open AccessArticle

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

1
Centre of Studies in Resources Engineering, Indian Institute of Technology-Bombay, Powai, Mumbai, Maharashtra 400076, India
2
National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
3
Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
4
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583-0988, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(13), 2091; https://doi.org/10.3390/rs12132091
Received: 10 May 2020 / Revised: 14 June 2020 / Accepted: 21 June 2020 / Published: 30 June 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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. View Full-Text
Keywords: drought; drought monitoring; CDI_M; PCA; SPI; SSI; LST; NDVI drought; drought monitoring; CDI_M; PCA; SPI; SSI; LST; NDVI
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Kulkarni, S.S.; Wardlow, B.D.; Bayissa, Y.A.; Tadesse, T.; Svoboda, M.D.; Gedam, S.S. Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India. Remote Sens. 2020, 12, 2091.

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