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Synergy of Satellite-Derived Drought Indices for Agricultural Drought Quantification and Yield Prediction
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Author(s): Dipti Ladli (Central University of Jharkhand, India), Kanhaiya Lal (Central University of Jharkhand, India), Kiran Jalem (National Institute of Rural Development and Panchayati Raj, Hyderabad, India)and Avinash Kumar Ranjan (National Institute of Technology, Rourkela, India)
Copyright: 2020
Pages: 27
Source title:
Spatial Information Science for Natural Resource Management
Source Author(s)/Editor(s): Suraj Kumar Singh (Suresh Gyan Vihar University, Jaipur, India), Shruti Kanga (Suresh Gyan Vihar University, Jaipur, India)and Varun Narayan Mishra (Suresh Gyan Vihar University, Jaipur, India)
DOI: 10.4018/978-1-7998-5027-4.ch007
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Abstract
The present study was conducted over Jharkhand state (India) for assessing the drought condition and corresponding yield of paddy (district-level) during Kharif 2018. Vegetation drought indices, namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and vegetation indices (VI) anomaly, were derived from different VI (i.e., NDVI, EVI) to assess the paddy health condition during drought year (2018) and non-drought year (2017). Later, the correlation between the DES-based yield data and derived drought indices (for the year 2017) were made to develop the district-level paddy yield model for the drought year 2018. The key results of the study shown that VCI derived from EVI data was found to be more reasonable to depict the drought condition, wherein ~21% area was under severe drought condition, 43% area under moderate drought condition, and 36% area under no drought condition. In addition, the yield prediction model derived from VCI (EVI-based) was found to be promising for predicting the paddy yield for Kharif 2018 with fair R2 of 0.53.
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