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Crop Dominance Mapping with IRS-P6 and MODIS 250-m Time Series Data

Author

Listed:
  • Murali Krishna Gumma

    (ICRISAT, Patancheru-502324, India)

  • Kesava Rao Pyla

    (National Institute of Rural Development, Rajendranagar, Hyderabad-500068, India)

  • Prasad S. Thenkabail

    (US Geological Survey (USGS), Flagstaff, AZ 86001, USA)

  • Venkataramana Murthy Reddi

    (Sairam Engineers, Bangalore- 560037, India)

  • Gundapaka Naresh

    (Spatial Information Technology, Jawarharlal Nehru Technological University, Hyderabad-500072, India)

  • Irshad A. Mohammed

    (ICRISAT, Patancheru-502324, India)

  • Ismail M. D. Rafi

    (ICRISAT, Patancheru-502324, India)

Abstract

This paper describes an approach to accurately separate out and quantify crop dominance areas in the major command area in the Krishna River Basin. Classification was performed using IRS-P6 (Indian Remote Sensing Satellite, series P6) and MODIS eight-day time series remote sensing images with a spatial resolution of 23.6 m, 250 m for the year 2005. Temporal variations in the NDVI (Normalized Difference Vegetation Index) pattern obtained in crop dominance classes enables a demarcation between long duration crops and short duration crops. The NDVI pattern was found to be more consistent in long duration crops than in short duration crops due to the continuity of the water supply. Surface water availability, on the other hand, was dependent on canal water release, which affected the time of crop sowing and growth stages, which was, in turn, reflected in the NDVI pattern. The identified crop-wise classes were tested and verified using ground-truth data and state-level census data. The accuracy assessment was performed based on ground-truth data through the error matrix method, with accuracies from 67% to 100% for individual crop dominance classes, with an overall accuracy of 79% for all classes. The derived major crop land areas were highly correlated with the sub-national statistics with R 2 values of 87% at the mandal (sub-district) level for 2005–2006. These results suggest that the methods, approaches, algorithms and datasets used in this study are ideal for rapid, accurate and large-scale mapping of paddy rice, as well as for generating their statistics over large areas. This study demonstrates that IRS-P6 23.6-m one-time data fusion with MODIS 250-m time series data is very useful for identifying crop type, the source of irrigation water and, in the case of surface water irrigation, the way in which it is applied. The results from this study have assisted in improving surface water and groundwater irrigated areas of the command area and also provide the basis for better water resource assessments at the basin scale.

Suggested Citation

  • Murali Krishna Gumma & Kesava Rao Pyla & Prasad S. Thenkabail & Venkataramana Murthy Reddi & Gundapaka Naresh & Irshad A. Mohammed & Ismail M. D. Rafi, 2014. "Crop Dominance Mapping with IRS-P6 and MODIS 250-m Time Series Data," Agriculture, MDPI, vol. 4(2), pages 1-19, April.
    • Handle: RePEc:gam:jagris:v:4:y:2014:i:2:p:113-131:d:35482
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    References listed on IDEAS

    as
    1. Thenkabail, Prasad & Biradar, Chandrashekhar & Turral, Hugh & Noojipady, Praveen & Li, Yuanjie & Vithanage, Jagath & Dheeravath, Venkateswarlu & Velpuri, Manohar & Schull, M. & Cai, Xueliang & Dutta, , 2006. "An irrigated area map of the world (1999) derived from remote sensing," IWMI Research Reports H039270, International Water Management Institute.
    2. Sakthivadivel, R. & Thiruvengadachari, S. & Amerasinghe, U. & Bastiaanssen, W. G. M. & Molden, D., 1999. "Performance evaluation of the Bhakra Irrigation System, India, using remote sensing and GIS techniques," IWMI Research Reports H024199, International Water Management Institute.
    3. Thiruvengadachari, S. & Sakthivadivel, R., 1997. "Satellite remote sensing for assessment of irrigation system performance: a case study in India," IWMI Research Reports H020351, International Water Management Institute.
    4. Bastiaanssen, W. G. M. & Molden, D. J. & Thiruvengadachari, S. & Smit, A. A. M. F. R. & Mutuwatte, L. & Jayasinghe, G., 1999. "Remote sensing and hydrologic models for performance assessment in Sirsa Irrigation Circle, India," IWMI Research Reports H024074, International Water Management Institute.
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    Cited by:

    1. Venkata Ramana Murthy Reddi & Murali Krishna Gumma & Kesava Rao Pyla & Amminedu Eadara & Jai Sankar Gummapu, 2017. "Monitoring Changes in Croplands Due to Water Stress in the Krishna River Basin Using Temporal Satellite Imagery," Land, MDPI, vol. 6(4), pages 1-18, October.
    2. Yanfei Wei & Xinhua Tong & Gang Chen & Deqiang Liu & Zhenfeng Han, 2019. "Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography," Agriculture, MDPI, vol. 9(7), pages 1-14, July.
    3. Prashant Patil & Murali Krishna Gumma, 2018. "A Review of the Available Land Cover and Cropland Maps for South Asia," Agriculture, MDPI, vol. 8(7), pages 1-22, July.

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