Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin
This paper describes a remote sensing based vegetation-phenology approach to accurately separate out and quantify groundwater irrigated areas from surface-water irrigated areas in the Krishna River basin (265 752 km2), India, using MODIS 250-m every 8-day near continuous time series for 2000-2001. T...
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| Format: | Conference Paper |
| Language: | Inglés |
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2009
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| Online Access: | https://hdl.handle.net/10568/38473 |
| _version_ | 1855534031456698368 |
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| author | Gumma, Murali K. Thenkabail, Prasad S. Velpuri, Naga Manohar |
| author_browse | Gumma, Murali K. Thenkabail, Prasad S. Velpuri, Naga Manohar |
| author_facet | Gumma, Murali K. Thenkabail, Prasad S. Velpuri, Naga Manohar |
| author_sort | Gumma, Murali K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This paper describes a remote sensing based vegetation-phenology approach to accurately separate out and quantify groundwater irrigated areas from surface-water irrigated areas in the Krishna River basin (265 752 km2), India, using MODIS 250-m every 8-day near continuous time series for 2000-2001. Temporal variations in the Normalized Difference Vegetation Index (NDVI) pattern, depicting phenology, obtained for the irrigated classes enabled demarcation between: (a) irrigated surface-water double crop, (b) irrigated surface-water continuous crop, and (c) irrigated groundwater mixed crops. The NDVI patterns were found to be more consistent in areas irrigated with groundwater due to the continuity of water supply. Surface water availability, however, was dependent on canal water release that affected time of crop sowing and growth stages, which was in turn reflected in the NDVI pattern. Double-cropped (IDBL) and light irrigation (IL) have relatively late onset of greenness, because they use canal water from reservoirs that drain large catchments and take weeks to fill. Minor irrigation and groundwater-irrigated areas have early onset of greenness because they drain smaller catchments where aquifers and reservoirs fill more quickly. Vegetation phonologies of nine distinct classes consisting of irrigated, rainfed, and other land-use classes were derived using MODIS 250-m near continuous time-series data that were tested and verified using groundtruth data, Google Earth very high resolution (sub-metre to 4 m) imagery, and state-level census data. Fuzzy classification accuracies for most classes were around 80% with class mixing mainly between various irrigated classes. The areas estimated from MODIS were highly correlated with census data (R-squared value of 0.86). |
| format | Conference Paper |
| id | CGSpace38473 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2009 |
| publishDateRange | 2009 |
| publishDateSort | 2009 |
| record_format | dspace |
| spelling | CGSpace384732024-01-17T12:58:34Z Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin Gumma, Murali K. Thenkabail, Prasad S. Velpuri, Naga Manohar vegetation phenology river basins maps land cover land use groundwater irrigation surface irrigation canals reservoirs irrigated land time series analysis remote sensing This paper describes a remote sensing based vegetation-phenology approach to accurately separate out and quantify groundwater irrigated areas from surface-water irrigated areas in the Krishna River basin (265 752 km2), India, using MODIS 250-m every 8-day near continuous time series for 2000-2001. Temporal variations in the Normalized Difference Vegetation Index (NDVI) pattern, depicting phenology, obtained for the irrigated classes enabled demarcation between: (a) irrigated surface-water double crop, (b) irrigated surface-water continuous crop, and (c) irrigated groundwater mixed crops. The NDVI patterns were found to be more consistent in areas irrigated with groundwater due to the continuity of water supply. Surface water availability, however, was dependent on canal water release that affected time of crop sowing and growth stages, which was in turn reflected in the NDVI pattern. Double-cropped (IDBL) and light irrigation (IL) have relatively late onset of greenness, because they use canal water from reservoirs that drain large catchments and take weeks to fill. Minor irrigation and groundwater-irrigated areas have early onset of greenness because they drain smaller catchments where aquifers and reservoirs fill more quickly. Vegetation phonologies of nine distinct classes consisting of irrigated, rainfed, and other land-use classes were derived using MODIS 250-m near continuous time-series data that were tested and verified using groundtruth data, Google Earth very high resolution (sub-metre to 4 m) imagery, and state-level census data. Fuzzy classification accuracies for most classes were around 80% with class mixing mainly between various irrigated classes. The areas estimated from MODIS were highly correlated with census data (R-squared value of 0.86). 2009 2014-06-13T11:42:10Z 2014-06-13T11:42:10Z Conference Paper https://hdl.handle.net/10568/38473 en Limited Access Gumma, Murali Krishna; Thenkabail, P. S.; Velpuri, N. M. 2009. Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin. In Bloschl, G.; van de Giesen, N.; Muralidharan, D.; Ren, L.; Seyler, F.; Sharma, U.; Vrba, J. (Eds.). Improving integrated surface and groundwater resources management in a vulnerable and changing world: proceedings of Symposium JS.3 at the Joint Convention of the International Association of Hydrological Sciences (IAHS) and the International Association of Hydrogeologists (IAH), Hyderabad, India, 6-12 September 2009. Wallingford, UK: International Association of Hydrological Sciences (IAHS) pp.271-281. (IAHS Publication 330) |
| spellingShingle | vegetation phenology river basins maps land cover land use groundwater irrigation surface irrigation canals reservoirs irrigated land time series analysis remote sensing Gumma, Murali K. Thenkabail, Prasad S. Velpuri, Naga Manohar Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin |
| title | Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin |
| title_full | Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin |
| title_fullStr | Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin |
| title_full_unstemmed | Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin |
| title_short | Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin |
| title_sort | vegetation phenology to partition groundwater from surface water irrigated areas using modis 250 m time series data for the krishna river basin |
| topic | vegetation phenology river basins maps land cover land use groundwater irrigation surface irrigation canals reservoirs irrigated land time series analysis remote sensing |
| url | https://hdl.handle.net/10568/38473 |
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