Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale
Remote sensing (RS) plays a crucial role in water resources management. Irrigated areas have undergone substantial changes globally. This research utilises RS to characterise irrigation from 2010 to 2020 within five canal commands in the Indus Basin Irrigated System (IBIS), the world's largest...
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| Language: | English |
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Elsevier
2025-04-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425000885 |
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| author | Jorge L. Peña-Arancibia Mobin-ud Din Ahmad Yingying Yu |
| author_facet | Jorge L. Peña-Arancibia Mobin-ud Din Ahmad Yingying Yu |
| author_sort | Jorge L. Peña-Arancibia |
| collection | DOAJ |
| description | Remote sensing (RS) plays a crucial role in water resources management. Irrigated areas have undergone substantial changes globally. This research utilises RS to characterise irrigation from 2010 to 2020 within five canal commands in the Indus Basin Irrigated System (IBIS), the world's largest contiguous irrigation system (∼16 million hectares). Cropping systems, water use and supply assessments are conducted primarily through estimations of 30 m actual evapotranspiration (ETa) and seasonal land cover classification maps – for both the wet summer 'Kharif' and dry winter 'Rabi' seasons. ETa estimates are required to match the 10-day period in which supply is adjusted to balance shortages in the canal commands. The multiannual 10-day frequency is achieved through blending of 'low spatial resolution-high temporal frequency' MODIS images (500 m and daily) and 'high spatial resolution-low temporal frequency' Landsat images (30 m and every 16 days). ETa estimates show reasonable spatiotemporal agreement (R2>0.7) when compared against locally calibrated ETa estimates. Seasonal crop maps generated with a Random Forest classification show reasonable accuracy (R2>0.9) when compared against agricultural survey statistics. The crop maps and associated ETa provide valuable insights into cropping and water use dynamics. While Kharif ETa and total cropped area exhibit relatively low year-to-year variability, large shifts from cotton (49% decrease) to rice (125% increase), other crops, and aquaculture are observed in some areas. During Rabi, ETa and total cropped area variations are less pronounced compared to Kharif, with winter cereals dominating the landscape. ETa generally exceeds water supply in the canal commands, with the disparity being higher during Rabi (36% on average), indicating groundwater augmentation as a significant contributor to groundwater depletion. The integration of ETa crop maps and canal water deliveries offers novel and essential knowledge for agriculture and water management policymaking in the IBIS and similar regions, from field to canal command scales. |
| format | Article |
| id | doaj-art-435dfc8488cb4292b86d460400f8fa6a |
| institution | OA Journals |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-435dfc8488cb4292b86d460400f8fa6a2025-08-20T01:57:52ZengElsevierAgricultural Water Management1873-22832025-04-0131110937410.1016/j.agwat.2025.109374Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scaleJorge L. Peña-Arancibia0Mobin-ud Din Ahmad1Yingying Yu2CSIRO Environment, Black Mountain Science and Innovation Park, ACT 2601, Australia; Corresponding authors.CSIRO Environment, Black Mountain Science and Innovation Park, ACT 2601, Australia; Corresponding authors.CSIRO Environment, Ecosciences Precinct, Dutton Park, QLD 4102, AustraliaRemote sensing (RS) plays a crucial role in water resources management. Irrigated areas have undergone substantial changes globally. This research utilises RS to characterise irrigation from 2010 to 2020 within five canal commands in the Indus Basin Irrigated System (IBIS), the world's largest contiguous irrigation system (∼16 million hectares). Cropping systems, water use and supply assessments are conducted primarily through estimations of 30 m actual evapotranspiration (ETa) and seasonal land cover classification maps – for both the wet summer 'Kharif' and dry winter 'Rabi' seasons. ETa estimates are required to match the 10-day period in which supply is adjusted to balance shortages in the canal commands. The multiannual 10-day frequency is achieved through blending of 'low spatial resolution-high temporal frequency' MODIS images (500 m and daily) and 'high spatial resolution-low temporal frequency' Landsat images (30 m and every 16 days). ETa estimates show reasonable spatiotemporal agreement (R2>0.7) when compared against locally calibrated ETa estimates. Seasonal crop maps generated with a Random Forest classification show reasonable accuracy (R2>0.9) when compared against agricultural survey statistics. The crop maps and associated ETa provide valuable insights into cropping and water use dynamics. While Kharif ETa and total cropped area exhibit relatively low year-to-year variability, large shifts from cotton (49% decrease) to rice (125% increase), other crops, and aquaculture are observed in some areas. During Rabi, ETa and total cropped area variations are less pronounced compared to Kharif, with winter cereals dominating the landscape. ETa generally exceeds water supply in the canal commands, with the disparity being higher during Rabi (36% on average), indicating groundwater augmentation as a significant contributor to groundwater depletion. The integration of ETa crop maps and canal water deliveries offers novel and essential knowledge for agriculture and water management policymaking in the IBIS and similar regions, from field to canal command scales.http://www.sciencedirect.com/science/article/pii/S0378377425000885EvapotranspirationCMRSETLandsatMODIS, machine learningIndus basinWater accounting |
| spellingShingle | Jorge L. Peña-Arancibia Mobin-ud Din Ahmad Yingying Yu Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale Agricultural Water Management Evapotranspiration CMRSET Landsat MODIS, machine learning Indus basin Water accounting |
| title | Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale |
| title_full | Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale |
| title_fullStr | Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale |
| title_full_unstemmed | Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale |
| title_short | Remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale |
| title_sort | remote sensing characterisation of cropping systems and their water use to assess irrigation management from field to canal command scale |
| topic | Evapotranspiration CMRSET Landsat MODIS, machine learning Indus basin Water accounting |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425000885 |
| work_keys_str_mv | AT jorgelpenaarancibia remotesensingcharacterisationofcroppingsystemsandtheirwaterusetoassessirrigationmanagementfromfieldtocanalcommandscale AT mobinuddinahmad remotesensingcharacterisationofcroppingsystemsandtheirwaterusetoassessirrigationmanagementfromfieldtocanalcommandscale AT yingyingyu remotesensingcharacterisationofcroppingsystemsandtheirwaterusetoassessirrigationmanagementfromfieldtocanalcommandscale |