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

Abstract 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) R 2 -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 R 2 -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.

[1]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[2]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[3]  Hiroyuki Ohno,et al.  Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers , 2006 .

[4]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[5]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[6]  W. Draeger,et al.  Monitoring irrigated land acreage using LANDSAT imagery: An application example , 1976 .

[7]  Rattan Lal,et al.  Soil Carbon Sequestration in India , 2004 .

[8]  P. Thenkabail,et al.  Spectral Matching Techniques to Determine Historical Land-use/Land-cover (LULC) and Irrigated Areas Using Time-series 0.1-degree AVHRR Pathfinder Datasets , 2007 .

[9]  H. Haes,et al.  A hierarchical approach to ecosystems and its implications for ecological land classification , 1994, Landscape Ecology.

[10]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

[11]  G. Dutt,et al.  Irrigation atlas of India , 1987 .

[12]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[13]  C. Woodcock,et al.  Resolution dependent errors in remote sensing of cultivated areas , 2006 .

[14]  J. Mustard,et al.  Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil , 2008 .

[15]  P. P. Nageswara Rao,et al.  Cropland inventory in the command area of Krishnarajasagar project using satellite data , 1994 .

[16]  Petra Döll,et al.  Global modeling of irrigation water requirements , 2002 .

[17]  Petra Döll,et al.  Development and validation of the global map of irrigation areas , 2005 .

[18]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[19]  Peter Droogers,et al.  Global irrigated area mapping: overview and recommendations. , 2002 .

[20]  Ramaswamy Sakthivadivel,et al.  Satellite remote sensing for assessment of irrigation system performance: a case study in India , 1997 .

[21]  P. Thenkabail,et al.  Sub-pixel Area Calculation Methods for Estimating Irrigated Areas , 2007, Sensors.

[22]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[23]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[24]  Prasad S. Thenkabail,et al.  Influence of Resolution in Irrigated Area Mapping and Area Estimation , 2009 .

[25]  Jesslyn F. Brown,et al.  Mapping Irrigated Lands across the United States Using MODIS Satellite Imagery , 2009 .

[26]  Pawan Kumar Joshi,et al.  SPOT vegetation multi temporal data for classifying vegetation in south central Asia , 2003 .

[27]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[28]  B. Wardlow,et al.  Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains , 2008 .

[29]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[30]  D. Roy,et al.  An overview of MODIS Land data processing and product status , 2002 .

[31]  Saeid Homayouni,et al.  HYPERSPECTRAL IMAGE ANALYSIS FOR MATERIAL MAPPING USING SPECTRAL MATCHING , 2004 .

[32]  J. Qi,et al.  Developing land use/land cover parameterization for climate–land modelling in East Africa , 2006 .

[33]  Mutlu Ozdogan,et al.  A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US , 2008 .

[34]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[35]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Yuanjie Li,et al.  A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[37]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[38]  Prasad S. Thenkabail,et al.  Remote Sensing of Global Croplands for Food Security , 2009 .

[39]  A. Hay.  Sampling designs to test land-use map accuracy , 1979 .

[40]  D. H. Card.  Using known map category marginal frequencies to improve estimates of thematic map accuracy , 1982 .

[41]  Prasad S. Thenkabail,et al.  Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data , 2005 .

[42]  P. Thenkabail,et al.  Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India , 2006 .

[43]  M. Schull,et al.  An irrigated area map of the world (1999) derived from remote sensing , 2006 .