Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015.

Anukesh Krishnankutty Ambika,Brian Wardlow,Vimal Mishra

doi:10.1038/sdata.2016.118

Anukesh Krishnankutty Ambika, Brian Wardlow + Show 1 more

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https://doi.org/10.1038/sdata.2016.118

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Abstract

India is among the countries that uses a significant fraction of available water for irrigation. Irrigated area in India has increased substantially after the Green revolution and both surface and groundwater have been extensively used. Under warming climate projections, irrigation frequency may increase leading to increased irrigation water demands. Water resources planning and management in agriculture need spatially-explicit irrigated area information for different crops and different crop growing seasons. However, annual, high-resolution irrigated area maps for India for an extended historical record that can be used for water resources planning and management are unavailable. Using 250 m normalized difference vegetation index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and 56 m land use/land cover data, high-resolution irrigated area maps are developed for all the agroecological zones in India for the period of 2000–2015. The irrigated area maps were evaluated using the agricultural statistics data from ground surveys and were compared with the previously developed irrigation maps. High resolution (250 m) irrigated area maps showed satisfactory accuracy (R2=0.95) and can be used to understand interannual variability in irrigated area at various spatial scales.

Highlights

Background & SummaryDemands for fresh water during the twenty first century will continue to increase to meet the needs of a growing global population
South Indian states such as Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu generally have low irrigation potential compared to the other regions, which can attributed to availability of surface and groundwater resources
Identifying potential agricultural area and defining cropping schedule The methodology consists of two main steps as shown in Supplementary Fig. 1: 1) classifying crop types using spectral similarity along the n-dimensional space vectors based on decision rules of physicallyinterpretable thresholds selected from spectral features, and 2) a decision tree model formulated using the vegetation condition index (VCI)[34,35] for each pixel in the selected agroecological region to accommodate the weather component in separating irrigated and non-irrigated areas

Summary

Introduction

Background & SummaryDemands for fresh water during the twenty first century will continue to increase to meet the needs of a growing global population. Zhao and Siebert[11] developed crop class based irrigated area maps for India using net sown area and extent of irrigated crops from the census and land use land cover data at 500 m spatial resolution for year 2005.

Results

Conclusion