Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin

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dc.creatorGumma, Murali K.
dc.creatorThenkabail, Prasad S.
dc.creatorVelpuri, Naga Manohar
dc.date2009
dc.date2014-06-13T11:42:10Z
dc.date2014-06-13T11:42:10Z
dc.date.accessioned2026-06-27T18:37:42Z
dc.descriptionThis 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).
dc.identifierhttps://hdl.handle.net/10568/38473
dc.identifier.urihttp://hdl.handle.net/123456789/161183
dc.languageen
dc.rightsLimited Access
dc.sourceGumma, 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)
dc.subjectvegetation
dc.subjectphenology
dc.subjectriver basins
dc.subjectmaps
dc.subjectland cover
dc.subjectland use
dc.subjectgroundwater irrigation
dc.subjectsurface irrigation
dc.subjectcanals
dc.subjectreservoirs
dc.subjectirrigated land
dc.subjecttime series analysis
dc.subjectremote sensing
dc.titleVegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin
dc.typeConference Paper

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Instituto Internacional para el Manejo del Agua (IWMI)