Comparing water-vegetative indices for rice (Oryza sativa L.)–wheat (Triticum aestivum L.) drought assessment

Drought indices (DI) are an useful tool for assessing different sectarian droughts. Standardized Precipitation Index (SPI) has been used worldwide to assess/monitor the onset, active phase, cessation and severity of drought. Normalized Difference Vegetation Index (NDVI) provides a comprehensive vegetation dynamics, which directly linked with rainfall received in a particular region. Indo-Gangetic Region (IGR), providing employment and livelihood to tens of millions of rural families directly or indirectly and rice (Oryza sativa L.)–wheat (Triticum aestivum L.) (RW) system of the Indo-Gangetic Plains (IGP) contributes 80% of the total cereal production and is critical to food security of the region. This study tries to verify the applicability of water-vegetative indices viz., SPI, Rainfall Index (RI) and NDVI for drought assessment of rice–wheat system productivity over IGR-India. The relationship between monsoon rainfall and NDVI shows that at around 1100 mm rainfall, the NDVI reached saturation point and no further significant increase in NDVI with increase of rainfall is noticed. Even though, there was a positive correlation of seasonal monsoon rainfall and average NDVI, conflicting results are noticed between monthly distribution of rainfall and monthly anomaly of NDVI over IGR States. It is noticed that June dif NDVI (actual NDVI–mean NDVI) contributes more to rice productivity followed by July. However, the combined effect of June, July and August, explains 15% of the variation of Kharif Rice Productivity Index (KRPI). As far as wheat is concerned, statistically significant relation was found between Wheat Productivity Index (WPI) and anomaly NDVI during December–March. This explains 35% of the variability in WPI.

► As rainfall increases from 500 mm, NDVI increases and reaches up to a range of 0.55 at 1100 mm; however beyond 1100 mm, no significant increase.
► June dif-NDVI contributes more to rice productivity followed by July.
► Combined effect of June, July and August, explains 15% of the variation of KPRI.
► The KPRI variation for most of the States is <15%, the multiple regression equation can be used as an indicator of regional rice production.
► As far as wheat is concerned, statistically significant relation was found between WPI and dif-NDVI during December to March.