Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images
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 mon...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/10/1960 |
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| author | Amit Kumar Sharma Laurence Hubert-Moy Sriramulu Buvaneshwari Muddu Sekhar Laurent Ruiz Hemanth Moger Soumya Bandyopadhyay Samuel Corgne |
| author_facet | Amit Kumar Sharma Laurence Hubert-Moy Sriramulu Buvaneshwari Muddu Sekhar Laurent Ruiz Hemanth Moger Soumya Bandyopadhyay Samuel Corgne |
| author_sort | Amit Kumar Sharma |
| collection | DOAJ |
| description | 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 calculating 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. |
| first_indexed | 2024-03-10T11:19:28Z |
| format | Article |
| id | doaj.art-d6dc460c34f9436cbc0d6cd419e3692b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:19:28Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d6dc460c34f9436cbc0d6cd419e3692b2023-11-21T20:12:44ZengMDPI AGRemote Sensing2072-42922021-05-011310196010.3390/rs13101960Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series ImagesAmit Kumar Sharma0Laurence Hubert-Moy1Sriramulu Buvaneshwari2Muddu Sekhar3Laurent Ruiz4Hemanth Moger5Soumya Bandyopadhyay6Samuel Corgne7L’Unité Mixte de Recherche Littoral, Environnement, Géomatique, Télédétection, le Centre National de la Recherche Scientifique, University of Rennes, 35043 Rennes, FranceL’Unité Mixte de Recherche Littoral, Environnement, Géomatique, Télédétection, le Centre National de la Recherche Scientifique, University of Rennes, 35043 Rennes, FranceIndo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, IndiaIndo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, IndiaIndo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, IndiaIndo-French Cell for Water Science, Indian Institute of Science, Bangalore 560012, IndiaEarth Observation and Disaster Management Programme Office, Indian Space Research Organisation, Headquarter, Bangalore 560094, IndiaL’Unité Mixte de Recherche Littoral, Environnement, Géomatique, Télédétection, le Centre National de la Recherche Scientifique, University of Rennes, 35043 Rennes, FranceGroundwater 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 calculating 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.https://www.mdpi.com/2072-4292/13/10/1960groundwater irrigationoptical remote sensingNDVIsupport vector machine classifierKabini critical zone observatory |
| spellingShingle | Amit Kumar Sharma Laurence Hubert-Moy Sriramulu Buvaneshwari Muddu Sekhar Laurent Ruiz Hemanth Moger Soumya Bandyopadhyay Samuel Corgne Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images Remote Sensing groundwater irrigation optical remote sensing NDVI support vector machine classifier Kabini critical zone observatory |
| title | Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images |
| title_full | Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images |
| title_fullStr | Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images |
| title_full_unstemmed | Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images |
| title_short | Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images |
| title_sort | identifying seasonal groundwater irrigated cropland using multi source ndvi time series images |
| topic | groundwater irrigation optical remote sensing NDVI support vector machine classifier Kabini critical zone observatory |
| url | https://www.mdpi.com/2072-4292/13/10/1960 |
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