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Condition assessment of pearl millet/ bajra crop in different vigour zones using Radar Vegetation Index

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Abstract

The study was focused on assessing the condition of pearl millet crop in critical growth stages using both polarimetric Radarsat-2 and dual-polarized Sentinel-1 datasets. The results revealed that bajra having a close structured phenology like maize and Jowar, exhibited significant changes in RVI due to differences in the crop calendar dates. For bajra, polarimetric RVI generated from information rich Radarsat-2 was observed to have a higher level of saturation till 6 kgm−2 biomass with a R2 of 0.7. In all instances, RVI exhibited a significant relationship with VWC and plant volume with a R2 above 0.7 due to its higher sensitivity towards crop dielectric constant. Unlike NDVI, RVI increased with an increase in Leaf Area Index till 5.8 even during panicle initiation stage. Backscatter and truncated RVI almost follow a similar trend of RVI response for various crop growth parameters. Hence, in case of regional analysis and high cost of Radarsat-2 dataset, one can use freely available sentinel data for RVI analysis due to its wider swath coverage. The observed high correlation of crop age with RVI (R2 = 0.6) proved to be the best tool for predicting sowing dates in staggered sowing zones.

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Acknowledgements

We would like to thank European Space Agency (ESA) for providing Sentinel-1 datasets and SNAP software for RVI analysis. The research was done under SUFALAM project and we are also thankful to Indian Institute of Remote Sensing (IIRS) for providing necessary resources for carrying out this research.

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  1. Agriculture and Soils Department, Indian Institute of Remote Sensing, ISRO, Dehradun, India

    Shanmugapriya Selvaraj, Dipanwita Haldar & Hari Shanker Srivastava

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  1. Shanmugapriya Selvaraj
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  2. Dipanwita Haldar
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  3. Hari Shanker Srivastava
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Correspondence to Shanmugapriya Selvaraj.

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Selvaraj, S., Haldar, D. & Srivastava, H.S. Condition assessment of pearl millet/ bajra crop in different vigour zones using Radar Vegetation Index. Spat. Inf. Res. 29, 631–643 (2021). https://doi.org/10.1007/s41324-021-00380-y

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