Your search found 18 records
1 Biradar, Chandrashekhar; Thenkabail, Prasad; Turral, Hugh; Noojipady, Praveen; Jie, Li Yuan; Velpuri, Manohar; Dheeravath, Venkateswarlu; Vithanage, Jagath; Schull, M.; Cai, X. L.; Gumma, Murali Krishna; Rishiraj, D. 2006. A global map of rainfed cropland areas at the end of last millennium using remote sensing and geospatial techniques. In Li, D.; Xia, L. (Eds.). Geoinformatics 2006: GNSS and Integrated Geospatial applications. Proceedings of SPIE Vol.6418, Wuhan, China, 28-29 October 2006. Bellingham, Washington, USA: SPIE – The International Society for Optical Engineering. 64181Q. 5p.
(Location: IWMI-HQ Call no: IWMI 621.3678 G000 BIR Record No: H039737)
2 Biggs, Trent; Gaur, Anju; Scott, C.; Thenkabail, Prasad; Gangadhara Rao, Parthasaradhi; Gumma, Murali Krishna; Acharya, Sreedhar; Turral, Hugh. 2007. Closing of the Krishna Basin: irrigation, streamflow depletion and macroscale hydrology. Colombo, Sri Lanka: International Water Management Institute (IWMI). 38p. (IWMI Research Report 111) [doi: https://doi.org/10.3910/2009.111]
River basins ; Physical geography ; Climate ; Stream flow ; Hydrology ; Rainfall runoff relationships ; Evapotranspiration ; Irrigation programs ; Water allocation ; Water transfer ; Environmental effects ; Water quality / India / Krishna River / Andhra Pradesh / Maharashtra / Karnataka
(Location: IWMI HQ Call no: IWMI 551.483 G635 BIG Record No: H040373)
(1.33MB)
Discharge from the Krishna River into the ocean decreased by 75 percent from 1960-2005, and was zero during a recent multi-year drought. This paper describes the physical geography and hydrology of the Krishna Basin, including runoff production and a basic water account based on hydronomic zones. More than 50 percent of the basin's irrigated area is groundwater irrigation, which is not currently included in inter-state allocation rules. Future water allocation will require inclusion of the interactions among all irrigated areas, including those irrigated by groundwater and surface water.
3 Thenkabail, Prasad S.; Biradar, C. M.; Noojipady, P.; Cai, Xueliang; Dheeravath, Venkateswarlu; Li, Y. J.; Velpuri, M.; Gumma, Murali Krishna; Pandey, Suraj. 2007. Sub-pixel area calculation methods for estimating irrigated areas. Sensors, 7: 2519-2538.
(Location: IWMI HQ Call no: IWMI 631.7.1 G000 THE Record No: H040450)
4 Gaur, Anju; Biggs, Trent W.; Gumma, Murali Krishna; Gangadhara Rao, Parthasaradhi; Turral, Hugh. 2008. Water scarcity effects on equitable water distribution and land use in a major irrigation project: case study in India. Journal of Irrigation and Drainage Engineering, 134(1): 26-35.
Reservoirs ; Dams ; Canals ; Water distribution ; Irrigation programs ; Crop production ; Case studies / India / Nagarjuna Sagar / Krishna River Basin
(Location: IWMI HQ Call no: IWMI 631.7 G635 GAU Record No: H041182)
In many river basins, upstream development and interannual variations in rainfall can cause both episodic and chronic shortages in water supplies downstream. Continued rapid development of surface and groundwater throughout the Krishna Basin in southern India resulted in historically low inflows to the main canals of the Nagarjuna Sagar irrigation project _8,955 km2_ during a recent drought _2002–2004_. This paper presents an integrated approach to assess how cropping patterns and the spatial equity of canal flow changed with water supply shocks in the left canal command area _3,592 km2_ of Nagarjuna Sagar. We combined 3 years _2000– 2003_ of canal release data with census statistics and high temporal resolution _8–10 days_ moderate resolution imaging spectrometer _MODIS_ 500-m resolution satellite imagery. The impact of water scarcity on land use pattern, delineated by MODIS images with moderate spatial resolution, was comparable with the census statistics, while the MODIS data also identified areas with changes and delays in the rice crop area, which is critical in assessing the impact of canal operations. A 60% reduction in water availability during the drought resulted in 40% land being fallowed in the left-bank canal command area. The results suggest that head reach areas receiving high supply rates during a normal year experienced the highest risks of fluctuations in water supply and cropped area during a water short year compared to downstream areas, which had chronically low water supply, and better adaptive responses by farmers. Contrary to expectations, the spatial distribution of canal flows among the three major zones of the command area was more equitable during low-flow years due to decreased flow at the head reach of the canal and relatively smaller decreases in tail-end areas. The findings suggested that equitable allocations could be achieved by improving the water distribution efficiency of the canal network during normal years and by crop diversification and introduction of alternative water sources during water shortage years. The study identified areas susceptible to decreases in water supplies by using modern techniques, which can help in decision-making processes for equitable water allocation and distribution and in developing strategies to mitigate the effects of water supply shocks on cropping patterns and rural livelihoods.
5 Gumma, Murali Krishna; Thenkabail, Prasad S.; Gautam, N. C.; Gangadhara Rao, Parthasaradhi; Manohar, Velpuri. 2008. Irrigated area mapping using AVHRR, MODIS and LANDSAT ETM+ data for the Krishna River Basin, India. Technology Spectrum, 2(1): 1-11.
River basins ; Water scarcity ; Irrigation programs ; Irrigated land ; Remote sensing ; Mapping ; Time series analysis / India / Krishna River Basin
(Location: IWMI HQ Call no: IWMI 631.7.1 G635 GUM Record No: H041432)
Net irrigated area in the Krishna river basin is varying quiet frequently due to water scarcity. Accurate area and extent of irrigated area in the Krishna River Basin is not available. State Irrigation Department projects large area under irrigation in the Krishna River Basin, which is attributed to its prestigious irrigation projects. However, the irrigation projects do not fulfill the demand in the basin consequently the tail Enders grow dry crops. Remote sensing replaces costly and tedious data collection on the ground, which is non-destructive. The aim of the present study is to prepare a comprehensive land use/land cover (LU/LC) map using continuous time-series data of multiple resolutions. A methodology is developed to map irrigated area categories using LANDSAT ETM+ along with coarse resolution time series imagery from AVHRR and MODIS, SRTM elevation, and other secondary data. Major stress was towards discrimination of ground-water irrigated area from surface-water irrigated area, determining of cropping patterns in irrigated area using MODIS NDVI time- series, and use of non-traditional methods of accuracy assessment using, ancillary datasets like SRTM-DEM, precipitation and state census statistics. A regression of the 9 class areas against agricultural census data explained 73% and 74% of the variance in groundwater and surface water irrigated area, respectively.
6 Platonov, Alexander; Thenkabail, Prasad; Biradar, Chandrashekhar M.; Cai, Xueliang; Gumma, Murali Krishna; Dheeravath, Venkateswarlu; Cohen, Y.; Alchanatis, V.; Goldshlager, N.; Ben-Dor, E.; Vithanage, Jagath; Manthrithilake, Herath; Kendjabaev, S.; Isaev, S. 2008. Water productivity mapping (WPM) using Landsat ETM+ data for the irrigated croplands of the Syrdarya River Basin in Central Asia. Sensors, 8:8156-8180.
Water productivity ; Mapping ; Remote sensing ; Water use ; Crops ; Productivity ; Crop yield ; Models ; Evapotranspiration ; Irrigated farming ; River basins / Central Asia / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H041566)
The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing “more crop per drop” (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involving crop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fraction by reference ET. The ET fraction was determined using Landsat thermal imagery by selecting the “hot” pixels (zero ET) and “cold” pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.
7 Thenkabail, Prasad S.; Biradar, Chandrashekhar M.; Noojipady, P.; Dheeravath, Venkateswarlu; Li, Yuan Jie; Velpuri, M.; Reddy, G. P. O.; Cai, Xueliang; Gumma, Murali Krishna; Turral, Hugh; Vithanage, Jagath; Schull, M.; Dutta, R. 2008. A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium. Colombo, Sri Lanka: International Water Management Institute (IWMI) 62p. [doi: https://doi.org/10.5337/2011.0024]
(Location: IWMI HQ Call no: e-copy only Record No: H042115)
(3.00 MB) (3MB)
8 Gumma, 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)
Vegetation ; Phenology ; River basins ; Vegetation ; Maps ; Land cover ; Land use ; Groundwater irrigation ; Surface irrigation ; Canals ; Reservoirs ; Irrigated land ; Time series analysis ; Remote sensing / India / Krishna River basin
(Location: IWMI HQ Call no: e-copy only Record No: H042217)
(1.15 MB)
This 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).
9 Gumma, Murali Krishna; Thenkabail, P. S.; Fujii, Hideto; Namara, Regassa. 2009. Spatial models for selecting the most suitable areas of rice cultivation in the Inland Valley Wetlands of Ghana using remote sensing and geographic information systems. Journal of Applied Remote Sensing, 3(1):21p. [doi: https://doi.org/10.1117/1.3182847]
Remote sensing ; Models ; Wetlands ; Rice ; Cultivation / Africa / West Africa / Ghana / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H042218)
http://remotesensing.spiedigitallibrary.org/data/Journals/APPRES/20338/033537_1.pdf
https://vlibrary.iwmi.org/pdf/H042218.pdf
https://vlibrary.iwmi.org/pdf/H042218.pdf
(4.64 MB)
The overarching goal of this research was to develop spatial models and demonstrate their use in selecting the most suitable areas for the inland valley (IV) wetland rice cultivation. The process involved comprehensive sets of methods and protocols involving: (1) Identification and development of necessary spatial data layers; (2) Providing weightages to these spatial data layers based on expert knowledge, (3) Development of spatial models, and (4) Running spatial models for determining most suitable areas for rice cultivation. The study was conducted in Ghana. The model results, based on weightages to 16-22 spatial data layers, showed only 3-4 % of the total IV wetland areas were “highly suitable” but 39-47 % of the total IV wetland areas were “suitable” for rice cultivation. The outputs were verified using field-plot data which showed accuracy between 84.4 to 87.5% with errors of omissions and commissions less than 23%. Given that only a small fraction (<15% overall) of the total IV wetland areas (about 20-28% of total geographic area in Ghana) are currently utilized for agriculture and constitute very rich land-units in terms of soil depth, soil fertility, and water availability, these agroecosystems offer an excellent opportunity for a green and a blue revolution in Africa.
10 Cai, Xueliang; Thenkabail, P. S.; Biradar, C. M.; Platonov, Alexander; Gumma, Murali Krishna; Dheeravath, V.; Cohen, Y.; Goldlshleger, F.; Ben-Dor, E.; Alchanatis, V.; Vithanage, Jagath; Anputhas, Markandu. 2009. Water productivity mapping using remote sensing data of various resolutions to support more crop per drop. Journal of Applied Remote Sensing, 3(033557). 23p. [doi: https://doi.org/10.1117/1.3257643]
Water productivity ; Crops ; Water use ; Evapotranspiration ; Mapping ; Remote sensing ; Models / Central Asia / Kyrgyzstan / Tajikistan / Uzbekistan / Kazakhstan / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H042408)
(4.07 MB)
The overarching goal of this research was to map crop water productivity using satellite sensor data at various spectral, spatial, radiometric, and temporal resolutions involving: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) 500m, (b) MODIS 250m, (c) Landsat enhanced thematic mapper plus (ETM+) 60m thermal, (d) Indian Remote Sensing Satellite (IRS) 23.5 m, and (e) Quickbird 2.44 m data. The spectro-biophysical models were developed using IRS and Quickbird satellite data for wet biomass, dry biomass, leaf area index, and grain yield for 5 crops: (a) cotton, (b) maize, (c) winter wheat, (d) rice, and (e) alfalfa in the Sry Darya basin, Central Asia. Crop-specific productivity maps were developed by applying the best spectro-biophysical models for the respective delineated crop types. Water use maps were produced using simplified surface energy balance (SSEB) model by multiplying evaporative fraction derived from Landsat ETM+ thermal data by potential ET. The water productivity (WP) maps were then derived by dividing the crop productivity maps by water use maps. The results of cotton crop, an overwhelmingly predominant crop in Central Asian Study area, showed that about 55% area had low WP of < 0.3 kg/m3, 34% had moderate WP of 0.3-0.4 kg/m3, and only 11% area had high WP > 0.4 kg/m3. The trends were similar for other crops. These results indicated that there is highly significant scope to increase WP (to grow "more crop per drop") through better water and cropland management practices in the low WP areas, which will substantially enhance food security of the ballooning populations without having to increase: (a) cropland areas, and\or (b) irrigation water allocations.
11 Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Li, Yuan Jie; Velpuri, N. M.; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Turral, H.; Cai, Xueliang; Vithanage, Jagath; Schull, M. A.; Dutta, R. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing, 30(14):3679-3733. [doi: https://doi.org/10.1080/01431160802698919]
(Location: IWMI HQ Call no: e-copy only Record No: H042409)
(18.23 MB)
A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997–1999, (b) Syste`me pour l’Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961–2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST-SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) ‘zoom-in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAMwas produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252Mha from season one, (b) 174Mha from season two and (c) 41Mha from continuous yearround crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).
12 Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Gumma, Murali Krishna; Li, Y. J.; Velpuri, M.; Gangalakunta, O. R. P. 2009. Global irrigated area maps (GIAM) and statistics using remote sensing. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.41-117. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042419)
(2.98 MB)
13 Biradar, C. M.; Thenkabail, P. S.; Noojipady, P.; Dheeravath, V.; Velpuri, M.; Turral, H.; Cai, Xueliang; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Schull, M. A.; Alankara, Ranjith; Gunasinghe, Sarath; Xiao, X. 2009. Global map of rainfed cropland areas (GMRCA) and statistics using remote sensing. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.357-389. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042430)
(1.40 MB)
14 Gumma, Murali Krishna. 2008. Methods and approaches for irrigated area mapping at various spatial resolutions using AVHRR, MODIS and LANDSAT ETM+ data for the Krishna River Basin, India. Thesis submitted to the Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, India, for the award of Degree of Doctor of Philosophy in Faculty of Spatial Information Technology. 421p.
River basins ; Land use ; Irrigated sites ; Canals ; Reservoirs ; Tanks ; Mapping ; Remote sensing ; Time series ; Surface irrigation ; Groundwater irrigation ; Cropping systems / India / Krishna River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H042567)
(42.13 MB)
Net irrigated area in the Krishna River Basin is varying quite frequently due to water scarcity. Data on accurate area and extent of irrigated area in this basin are not available. There are discrepancies in the statistics provided by agencies like the State Irrigation Department, State Agriculture Department, and Census of India. The State Irrigation Department projects a large irrigated area in the Krishna River Basin, attributed to its prestigious irrigation projects. However, the irrigation projects do not fulfill the demands in the basin so that the tail enders grow dry crops.
Remote sensing replaces costly and tedious data collection on the ground, which is nondestructive. The aim of the present study is to prepare a comprehensive land use/land cover (LULC) map including irrigated areas using continuous time series of multiple resolutions by using AVHRR and MODIS. Methodologies were developed to map irrigated area categories using LANDSAT ETM+ along with coarse resolution data sets which are MODIS time series, SRTM elevation and other secondary data.
There is a need to bridge the gap between the use of high resolution satellite data and coarse resolution satellite data and to modify the existing methodology to derive irrigated areas using high resolution satellite data. Space-time spiral curves; resolving the mixed classes; decision tree algorithms; spatial modeling; Google Earth data; irrigated area fractions; Landsat-based estimates of the irrigated fractions were used in this study.
It is well established that LULC change has significant effects on many processes in basins including soil erosion, global warming and biodiversity, and that LULC is expected to cause greater impact on human habitability than climate change. Irrigated area fraction (IAF) in coarser resolution data cannot be overemphasized. Therefore, in this dissertation research my focus will be addressing these gaps in mapping irrigated areas using remote sensing.
Dug-wells, shallow tube wells, and deep tube wells are used for groundwater irrigation while tanks may be used for both surface water irrigation and groundwater recharge. Highly significant agricultural land use changes have taken place as a result of inter annual variations in water availability.
One of the objectives of this study was to investigate the changes in cropland areas as a result of water availability using MODIS 250 m time series and spectral matching techniques. The study was conducted in a very large river basin (Krishna) in India considering a water-surplus year (2000-01) and a water-deficit year (2002-03).
Remote sensing replaces costly and tedious data collection on the ground, which is nondestructive. The aim of the present study is to prepare a comprehensive land use/land cover (LULC) map including irrigated areas using continuous time series of multiple resolutions by using AVHRR and MODIS. Methodologies were developed to map irrigated area categories using LANDSAT ETM+ along with coarse resolution data sets which are MODIS time series, SRTM elevation and other secondary data.
There is a need to bridge the gap between the use of high resolution satellite data and coarse resolution satellite data and to modify the existing methodology to derive irrigated areas using high resolution satellite data. Space-time spiral curves; resolving the mixed classes; decision tree algorithms; spatial modeling; Google Earth data; irrigated area fractions; Landsat-based estimates of the irrigated fractions were used in this study.
It is well established that LULC change has significant effects on many processes in basins including soil erosion, global warming and biodiversity, and that LULC is expected to cause greater impact on human habitability than climate change. Irrigated area fraction (IAF) in coarser resolution data cannot be overemphasized. Therefore, in this dissertation research my focus will be addressing these gaps in mapping irrigated areas using remote sensing.
Dug-wells, shallow tube wells, and deep tube wells are used for groundwater irrigation while tanks may be used for both surface water irrigation and groundwater recharge. Highly significant agricultural land use changes have taken place as a result of inter annual variations in water availability.
One of the objectives of this study was to investigate the changes in cropland areas as a result of water availability using MODIS 250 m time series and spectral matching techniques. The study was conducted in a very large river basin (Krishna) in India considering a water-surplus year (2000-01) and a water-deficit year (2002-03).
15 Biradar, Chandrashekhar M.; Thenkabail, Prasad S.; Noojipady, P.; Li, Yuan Jie; Dheeravath, Venkateswarlu; Turral, Hugh; Velpuri, Manohar; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Cai, X. L.; Xiao, X.; Schull, M. A.; Alankara, Ranjith; Gunasinghe, Sarath; Mohideen, Sadir. 2009. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. International Journal of Applied Earth Observation and Geoinformation, 11(2):114-129. [doi: https://doi.org/10.1016/j.jag.2008.11.002]
(Location: IWMI HQ Call no: e-copy only Record No: H042769)
The overarching goal of this study was to produce a global map of rainfed cropland areas (GMRCA) and calculate country-by-country rainfed area statistics using remote sensing data. A suite of spatial datasets, methods and protocols for mapping GMRCA were described. These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R2-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. The global rainfed cropland area estimate from the GMRCA 9-class map was 1.13 billion hectares (Bha). The total global cropland areas (rainfed plus irrigated) was 1.53 Bha which was close to national statistics compiled by FAOSTAT (1.51 Bha). The accuracies and errors of GMRCA were assessed using field-plot and Google Earth data points. The accuracy varied between 92 and 98% with kappa value of about 0.76, errors of omission of 2–8%, and the errors of commission of 19–36%.
16 Velpuri, N. M.; Thenkabail, P. S.; Gumma, Murali Krishna; Biradar, C.; Dheeravath, V.; Noojipady, P.; Yuanjie, L. 2009. Influence of resolution in irrigated area mapping and area estimation. Photogrammetric Engineering and Remote Sensing, 75(12):1383-1395.
Remote sensing ; Satellite surveys ; Mapping ; Irrigated sites ; Estimation ; River basins ; Surface irrigation ; Groundwater irrigation ; Conjunctive use / India / Krishna River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H043443)
(3.31 MB)
The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R2 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that “the finer the spatial resolution of the sensor used, greater was the irrigated area derived,” since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and nonirrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent).
17 Thenkabail, P. S.; Hanjra, M. A.; Dheeravath, V.; Gumma, Murali Krishna. 2010. 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. Remote Sensing, 2(1):211-261. [doi: https://doi.org/10.3390/rs2010211]
Farmland ; Irrigated land ; Remote sensing ; Mapping ; Water use ; Virtual water ; Water productivity ; Food security
(Location: IWMI HQ Call no: e-copy only Record No: H043444)
(4.41 MB)
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 R2-value of 0.89–0.94. The global cropland area estimates amongst different studies are quite close and range between 1.47–1.53 billion hectares. However, significant uncertainties exist in determining irrigated areas which, globally, consume nearly 80% of all human water use. The estimates show that the total water use by global croplands varies between 6,685 to 7,500 km3 yr-1 and of this around 4,586 km3 yr-1 is by rainfed croplands (green water use) and the rest by irrigated croplands (blue water use). Irrigated areas use about 2,099 km3 yr-1 (1,180 km3 yr-1 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 global croplands are examined. Finally, the paper links global croplands and their water use to a paradigm for ensuring future food security.
18 Dheeravath, V.; Thenkabail, P. S.; Chandrakantha, G.; Noojipady, P.; Reddy, G. P. O.; Biradar, C. M.; Gumma, Murali Krishna; Velpuri, M. 2010. Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1):42-59. [doi: https://doi.org/10.1016/j.isprsjprs.2009.08.004]
(Location: IWMI HQ Call no: e-copy only Record No: H043479)
(5.30 MB)
The overarching goal of this research was to develop methods and protocols for mapping irrigated areas using a Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m time series, to generate irrigated area statistics, and to compare these with ground- and census-based statistics. The primary mega-file data-cube (MFDC), comparable to a hyper-spectral data cube, used in this study consisted of 952 bands of data in a single file that were derived from MODIS 500 m, 7-band reflectance data acquired every 8-days during 2001–2003. The methods consisted of (a) segmenting the 952-band MFDC based not only on elevation-precipitation-temperature zones but on major and minor irrigated command area boundaries obtained from India’s Central Board of Irrigation and Power (CBIP), (b) developing a large ideal spectral data bank (ISDB) of irrigated areas for India, (c) adopting quantitative spectral matching techniques (SMTs) such as the spectral correlation similarity (SCS) R2-value, (d) establishing a comprehensive set of protocols for class identification and labeling, and (e) comparing the results with the National Census data of India and field-plot data gathered during this project for determining accuracies, uncertainties and errors. The study produced irrigated area maps and statistics of India at the national and the subnational (e.g., state, district) levels based on MODIS data from 2001–2003. The Total Area Available for Irrigation (TAAI) and Annualized Irrigated Areas (AIAs) were 113 and 147 million hectares (MHa), respectively. The TAAI does not consider the intensity of irrigation, and its nearest equivalent is the net irrigated areas in the Indian National Statistics. The AIA considers intensity of irrigation and is the equivalent of “irrigated potential utilized (IPU)” reported by India’s Ministry of Water Resources (MoWR). The field-plot data collected during this project showed that the accuracy of TAAI classes was 88% with a 12% error of omission and 32% of error of commission. Comparisons between the AIA and IPU produced an R2-value of 0.84. However, AIA was consistently higher than IPU. The causes for differences were both in traditional approaches and remote sensing. The causes of uncertainties unique to traditional approaches were (a) inadequate accounting of minor irrigation (groundwater, small reservoirs and tanks), (b) unwillingness to share irrigated area statistics by the individual Indian states because of their stakes, (c) absence of comprehensive statistical analyses of reported data, and (d) subjectivity involved in observation-based data collection process. The causes of uncertainties unique to remote sensing approaches were (a) irrigated area fraction estimate and related sub-pixel area computations and (b) resolution of the imagery. The causes of uncertainties common in both traditional and remote sensing approaches were definitions and methodological issues.
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