Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003

Dheeravath, Venkateswarlu; Thenkabail, Prasad S.; Chandrakantha, G.; Noojipady, Praveen; Reddy, G. P. Obi; Biradar, Chandrashekhar; Gumma, Murali Krishna; Velpuri, Manohar

doi:10.1016/j.isprsjprs.2009.08.004

Cited by 82 publications

(68 citation statements)

References 31 publications

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“…Irrigated areas derived by Siebert and Döll [49] and national statistics provided by Ministry of Agriculture [68] based primarily on remote sensing approaches. The Thenkabail et al [69] estimates were significantly higher than Siebert et al [52] mainly as a result of the huge differences for India and China. Overall, there is remarkably high correlation (R 2 value of 0.97) for the 1:1 line for a country-bycountry comparison of irrigated cropland areas between the two approaches.…”

Section: Figure 6bmentioning

confidence: 75%

“…However, the irrigated area maps and statistics on groundwater irrigation are sketchy and/or missing [61]. Indeed, overwhelming evidence [59][60][61][62] shows that much of the potential is already exploited but these do not show up as irrigated area maps [63]. Massive exploitation of surface water from minor reservoirs and/or tanks is a missing link in many irrigated area statistics.…”

Section: Uncertainties In Cropland Area Estimatesmentioning

confidence: 99%

“…In order to determine the magnitude of differences that occur between remote sensing and census based field data, we made a comparison of irrigated area statistics derived from MODIS 500 m [63] with that of the census based national statistics [68] (Figure 7). This resulted in 42% of the districts of India having a 1:1 or near 1:1 match between MODIS based areas and the census based areas, 19% of the districts had MODIS based areas nearly twice as the census based areas, and the rest 39% of the districts had MODIS based areas nearly thrice as the census based areas (Figure 7).…”

Section: Figure 6bmentioning

confidence: 99%

See 2 more Smart Citations

A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

Thenkabail

Hanjra²,

Dheeravath³

et al. 2010

Remote Sensing

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This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world's leading researchers on the subject. A comparison at country scale of global cropland area estimated by these studies had a high R 2 -value of 0.89-0. of blue water and the rest from rain that falls over irrigated croplands). However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40-62 percent. The weaknesses, trends, and future directions to precisely estimate the

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Section: Figure 6bmentioning

confidence: 75%

Section: Uncertainties In Cropland Area Estimatesmentioning

confidence: 99%

Section: Figure 6bmentioning

confidence: 99%

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A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

Thenkabail

Hanjra²,

Dheeravath³

et al. 2010

Remote Sensing

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“…Irrigation map for the year 2009. A procedure to discriminate irrigated from nonirrigated crops using remote sensing information is to compare the empirical spectral signature of crops, obtained by ground control measurements, against the spectral signature of vegetation in the area of interest gathered by satellite sensors (47). The irrigation map presented in Fig.…”

Section: Methodsmentioning

confidence: 99%

Long-lasting transition toward sustainable elimination of desert malaria under irrigation development

Baeza¹,

Bouma

Dhiman

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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In arid areas, people living in the proximity of irrigation infrastructure are potentially exposed to a higher risk of malaria due to changes in ecohydrological conditions that lead to increased vector abundance. However, irrigation provides a pathway to economic prosperity that over longer time scales is expected to counteract these negative effects. A better understanding of this transition between increased malaria risk and regional elimination, in particular whether it is slow or abrupt, is relevant to sustainable development and disease management. By relying on space as a surrogate for stages of time, we investigate this transition in a semidesert region of India where a megairrigation project is underway and expected to cover more than 1,900 million hectares and benefit around 1 million farmers. Based on spatio-temporal epidemiological cases of Plasmodium vivax malaria and land-use irrigation from remote sensing sources, we show that this transition is characterized by an enhanced risk in areas adjacent to the trunk of the irrigation network, despite a forceful and costly insecticide-based control. Moreover, this transition between climate-driven epidemics and sustained low risk has already lasted a decade. Given the magnitude of these projects, these results suggest that increased health costs have to be planned for over a long time horizon. They further highlight the need to integrate assessments of both health and environmental impacts to guide adaptive mitigation strategies. Our results should help to define and track these transitions in other arid parts of the world subjected to similar tradeoffs.vector-borne diseases | agricultural development | epidemic malaria | irrigation gradient | environmental health

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“…Studies using NDVI have been used to distinguish irrigated from non-irrigated land use in semi-arid and arid regions in India and Turkey [5,7,17]. NDVI has also been used to classify land use and land cover in which irrigated area is one of multiple classification categories [4,18,19].…”

Section: Introductionmentioning

confidence: 99%

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Composting Algorithms, Spectral Indices, and Sensors

et al. 2017

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There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world's diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho's Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region.

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Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003

Cited by 82 publications

References 31 publications

A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

Long-lasting transition toward sustainable elimination of desert malaria under irrigation development

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Composting Algorithms, Spectral Indices, and Sensors

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