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Basmati rice varietal differentiate using Sentinel-1 and Sentinel-2 data in Google Earth Engine

By: Contributor(s): Description: pp1188-1999Subject(s): In: Current ScienceSummary: A study on Basmati rice variety differentiation was carried out in two districts of Haryana, using Sentinel-1 SAR and Sentinel-2 MSI time series data and a Random Forest (RF) classifier on the Google Earth Engine cloud computing platform. The fortnightly composite stack of VH-VV difference and Sentinel-2 multispectral bands and their Red-edge indices, Chlorophyll Index (CIgreen and CIred-edge), was analysed. Basmati varieties could be distinguished into long- and shortduration and early Basmati. The accuracy of the RF classifier model was validated by computing the AUC and receiver operating characteristic curves. AUC was > 0.9, ensuring the best model fitting and high accuracy. The results revealed that long-duration Basmati varieties are being replaced by shorter-duration varieties.
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Item type Current library Vol info Status Barcode
Journal Article SNDT Juhu Available jp826.2
Periodicals SNDT Juhu Vol 128 No 12 Available JP826

A study on Basmati rice variety differentiation was carried out in two districts of Haryana, using Sentinel-1
SAR and Sentinel-2 MSI time series data and a Random Forest (RF) classifier on the Google Earth Engine
cloud computing platform. The fortnightly composite
stack of VH-VV difference and Sentinel-2 multispectral bands and their Red-edge indices, Chlorophyll
Index (CIgreen and CIred-edge), was analysed. Basmati
varieties could be distinguished into long- and shortduration and early Basmati. The accuracy of the RF
classifier model was validated by computing the AUC
and receiver operating characteristic curves. AUC was
> 0.9, ensuring the best model fitting and high accuracy.
The results revealed that long-duration Basmati varieties are being replaced by shorter-duration varieties.

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