A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium

Thenkabail, Prasad S.; Biradar, Chandrashekhar; Noojipady, Praveen; Dheeravath, Venkateswarlu; Li, Yuan Jie; Velpuri, Manohar; Reddy, G. P. Obi; Cai, Xueliang; Gumma, Murali Krishna; Turral, Hugh; Vithanage, Jagath; Schull, Mitchell A.; Dutta, Ratna

doi:10.5337/2011.0024

Cited by 44 publications

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“…A MFDC consists of a large number of bands in a single file that would help obtain information of all bands with a single click at any point or pixel. Thenkabail et al provide a detailed discussion on MFDC concept [37]. In this study, a MFDC of 51 layers, for a total volume of 35 GB, came from 6 non thermal bands from each of six Landsat ETM+ tiles and 45 NDVI bands from each of four MOD09Q1 tiles.…”

Section: Mega-file Data Cube (Mfdc) Creationmentioning

confidence: 99%

“…The overestimation is obvious in 1:1 plot (Figure 5(C)). The map of irrigated area of Ghana was compared with other studies such as the global irrigated area map (GIAM) [37] and FAO (Table 5). …”

Section: Comparisons With National Statistics and Other Studiesmentioning

confidence: 99%

“…For example, irrigated crops are grown in very flat slopes (e.g., valley bottoms) where as inland valley forests are mostly along the valley slopes. Geographic information systems (GIS) spatial modeling approaches were adopted to resolve mixed classes [37]. Overlay, matrix, recode, sieve and proximity analysis [31] based on the theory of map algebra and Boolean logic [42][43][44] were some of the spatial modeling techniques used.…”

Section: Resolving the Mixed Classesmentioning

confidence: 99%

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Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

et al. 2011

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Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m OPEN ACCESSRemote Sens. 2011, 3 817 time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.

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Section: Mega-file Data Cube (Mfdc) Creationmentioning

confidence: 99%

Section: Comparisons With National Statistics and Other Studiesmentioning

confidence: 99%

Section: Resolving the Mixed Classesmentioning

confidence: 99%

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Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

et al. 2011

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“…The map is also compared with global land cover datasets, revealing large discrepancies. Thenkabail et al (2008Thenkabail et al ( , 2009a developed the Global Irrigated Area Map (GIAM) with 28 classes and a spatial resolution of 10 km, around year 1999. Temporal series of remote sensing data were used in this work.…”

Section: Irrigation Mappingmentioning

confidence: 99%

“…These methods provide information about areas equipped for irrigation, about crop dominance and irrigation source, and about existence or absence of irrigation, but none of the methods quantifies the actual amount of water received by the crops through irrigation, or blue water. In particular, the source of irrigation water was determined by Thenkabail et al (2008Thenkabail et al ( , 2009a in their Global Irrigated Area Map (GIAM), where irrigated areas were classified as a function of three sources of irrigation supply: surface water, groundwater and conjunctive use (due to usage of stored rain water).…”

Section: Introductionmentioning

confidence: 99%

Irrigation assessment via remote sensing and land surface model data

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The future of global water stress: An integrated assessment

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We assess the ability of global water systems, resolved at 282 assessment subregions (ASRs), to the meet water requirements under integrated projections of socioeconomic growth and climate change. We employ a water resource system (WRS) component embedded within the Massachusetts Institute of Technology Integrated Global System Model (IGSM) framework in a suite of simulations that consider a range of climate policies and regional hydroclimate changes out to 2050. For many developing nations, water demand increases due to population growth and economic activity have a much stronger effect on water stress than climate change. By 2050, economic growth and population change alone can lead to an additional 1.8 billion people living under at least moderate water stress, with 80% of these located in developing countries. Uncertain regional climate change can play a secondary role to either exacerbate or dampen the increase in water stress. The strongest climate impacts on water stress are observed in Africa, but strong impacts also occur over Europe, Southeast Asia, and North America. The combined effects of socioeconomic growth and uncertain climate change lead to a 1.0-1.3 billion increase of the world's 2050 projected population living with overly exploited water conditions-where total potential water requirements will consistently exceed surface water supply. This would imply that adaptive measures would be taken to meet these surface water shortfalls and include: water-use efficiency, reduced and/or redirected consumption, recurrent periods of water emergencies or curtailments, groundwater depletion, additional interbasin transfers, and overdraw from flow intended to maintain environmental requirements.

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A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium

Cited by 44 publications

References 0 publications

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Irrigation assessment via remote sensing and land surface model data

The future of global water stress: An integrated assessment

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