Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data

Thenkabail, Prasad S.; Schull, Mitchell A.; Turral, Hugh

doi:10.1016/j.rse.2004.12.018

Cited by 249 publications

(220 citation statements)

References 20 publications

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“…The cloud removal algorithm of Thenkabail et al [14] was not used because we assumed that the composite NDVI values eliminated cloud interference by making use of at least one cloud-free day in computing the data series.…”

Section: Methodsmentioning

confidence: 99%

“…tral matching techniques (SMTs), followed by class identification and merging using ground observations, Google Earth imagery, and other secondary data to produce maps of LULC classes for each year of data [14,15]. Areal coverage by LULC class was used to measure changes over the period 1982-2005.…”

Section: Lulc Classification Using Satellite Imagerymentioning

confidence: 99%

“…SCS is based on Pearson's correlation coefficient (r) applied to an NDVI time series [14]: The NDVI may range in value from −1.0 to +1.0. A value of +1 indicates healthy vegetation; values close to zero indicate poor vegetation.…”

Section: Lulc Classification Using Satellite Imagerymentioning

confidence: 99%

“…Although r can range from −1.0 to +1.0, negative values are not meaningful here [14]. The higher the value of r, the greater the similarity of the spectral or temporal NDVI profile between the historical and recent time series land-use and land--cover class [15].…”

Section: Lulc Classification Using Satellite Imagerymentioning

confidence: 99%

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Land Use and Land Cover Change (LULC) in the Lake Malawi Drainage Basin, 1982-2005

Chavula

Brezonik

Bauer

2011

IJG

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Changes in land use and land cover (LULC) in the drainage basin of Lake Malawi over the period 1982 -2005 were estimated from satellite imagery, and possible relationships were evaluated among the four major land-cover classes: cropland, forest, water, and savanna/shrub/woodland. AVHRR and MODIS sensors gave different values of areal extent of the four classes, limiting the feasibility of establishing consistent temporal trends over the entire period of the study, but forest land showed the least change among three land cover types, and extent of water bodies remained virtually unaltered over the period. AVHRR results show that cropland was mainly derived from savanna/shrub/woodland, which declined by almost 90% over the period 1982-1995.

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Section: Methodsmentioning

confidence: 99%

Section: Lulc Classification Using Satellite Imagerymentioning

confidence: 99%

Section: Lulc Classification Using Satellite Imagerymentioning

confidence: 99%

Section: Lulc Classification Using Satellite Imagerymentioning

confidence: 99%

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Land Use and Land Cover Change (LULC) in the Lake Malawi Drainage Basin, 1982-2005

Chavula

Brezonik

Bauer

2011

IJG

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“…WA+ needs land use maps focused on crop types (e.g., rainfed potatoes, irrigated maize) and the source of water consumed (e.g., surface water and groundwater). Some of the first maps dedicated for agricultural water management were prepared by Thenkabail et al (2005), Cheema and Bastiaanssen (2010), Yalew et al (2012) and Kiptala et al (2013). Furthermore, land use classifications for WA+ at river basin scale require a pixel size of 30-100 m that can be delivered by Landsat-8 and Proba-V satellite data, respectively.…”

Section: Land Usementioning

confidence: 99%

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

Karimi

Bastiaanssen

2015

Hydrol. Earth Syst. Sci.

124

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Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95 % (SD 5 %) and rainfall with an overall absolute accuracy of 82 % (SD 15 %). Land use can be identified with an overall accuracy of 85 % (SD 7 %). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

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Assessing variability of evapotranspiration over the Ganga river basin using water balance computations

Syed

Webster

Famiglietti

2014

Water Resources Research

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A thorough assessment of evapotranspiration (ET) pervades several important issues of the 21 st century including climate change, food-security, land-management, flood and drought prediction, and water resources assessment and management. Such a proper assessment is of particular importance in the Ganga river basin (GRB) with its backdrop of a rapidly increasing population pressure and unregulated use of water resources. Spatially averaged ET over the GRB is computed as the residual of atmospheric and terrestrial water budget computations using a combination of model simulations and satellite and groundbased observations. The best estimate of monthly ET is obtained as the monthly mean of atmospheric and terrestrial water balance computations for the period 1980-2007. The mean monthly average of ET from these various estimates is 72.3 6 18.8 mm month 21 . Monthly variations of ET peak between July and August and reach a minimum in February. For the entire study period, the rate of change of ET across the GRB is 211 mm yr 22 (i.e., mm/yr/yr). Alongside a notable influence of the 1997-1998 El Niño, results allude to the existence of interim periods during which ET trends varied significantly. More specifically, during the period of 1998-2002, the rate of decline increased to 255.8 mm yr 22 , which is almost 5 times the overall trend. Based on the correlation between ET and independent estimates of near-surface temperature and soil moisture, we can infer that the ET over the GRB is primarily limited by moisture availability. The analysis has important potential for use in large-scale water budget assessments and intercomparison studies. The analysis also emphasizes the importance of synergistic use of mutliplatform hydrologic information.

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Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data

Cited by 249 publications

References 20 publications

Land Use and Land Cover Change (LULC) in the Lake Malawi Drainage Basin, 1982-2005

Land Use and Land Cover Change (LULC) in the Lake Malawi Drainage Basin, 1982-2005

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

Assessing variability of evapotranspiration over the Ganga river basin using water balance computations

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