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Identifying seasonal groundwater-irrigated cropland using multi-source NDVI time-series images

Sharma, AK and Hubert-Moy, L and Buvaneshwari, S and Sekhar, M and Ruiz, L and Moger, H and Bandyopadhyay, S and Corgne, S (2021) Identifying seasonal groundwater-irrigated cropland using multi-source NDVI time-series images. In: Remote Sensing, 13 (10).

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Official URL: https://doi.org/10.3390/rs13101960


Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study�s main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calcu-lating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014�2016 using ground observations. Kappa coefficients ranged from 0.81�0.96 for 2014�2015 and 0.62�0.89 for 2015�2016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6 to 16.5 of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Item Type: Journal Article
Publication: Remote Sensing
Publisher: MDPI AG
Additional Information: The copyright for this article belongs to Authors
Keywords: Crops; Groundwater; Irrigation; Remote sensing; Satellites; Tropical engineering; Tropics; Watersheds, Area classification; Ground observations; Groundwater irrigation; Irrigated cropland; Normalized difference vegetation index; Optical remote sensing; Optical satellite images; Spatial resolution, Support vector machines
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 23 Aug 2021 09:32
Last Modified: 23 Aug 2021 09:32
URI: http://eprints.iisc.ac.in/id/eprint/69253

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