Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information

Varun Narayan Mishra; Rajendra Prasad; Pradeep Kumar; Prashant K. Srivastava; Praveen Kumar Rai

doi:10.1117/1.JRS.11.046003

12 October 2017 Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information

Varun Narayan Mishra, Rajendra Prasad, Pradeep Kumar, Prashant K. Srivastava, Praveen Kumar Rai

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046003 (October 2017). https://doi.org/10.1117/1.JRS.11.046003

Abstract

Updated and accurate information of rice-growing areas is vital for food security and investigating the environmental impact of rice ecosystems. The intent of this work is to explore the feasibility of dual-polarimetric C-band Radar Imaging Satellite-1 (RISAT-1) data in delineating rice crop fields from other land cover features. A two polarization combination of RISAT-1 backscatter, namely ratio (HH/HV) and difference (HH−HV), significantly enhanced the backscatter difference between rice and nonrice categories. With these inputs, a QUEST decision tree (DT) classifier is successfully employed to extract the spatial distribution of rice crop areas. The results showed the optimal polarization combination to be HH along with HH/HV and HH−HV for rice crop mapping with an accuracy of 88.57%. Results were further compared with a Landsat-8 operational land imager (OLI) optical sensor-derived rice crop map. Spatial agreement of almost 90% was achieved between outputs produced from Landsat-8 OLI and RISAT-1 data. The simplicity of the approach used in this work may serve as an effective tool for rice crop mapping.

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Varun Narayan Mishra, Rajendra Prasad, Pradeep Kumar, Prashant K. Srivastava, and Praveen Kumar Rai "Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information," Journal of Applied Remote Sensing 11(4), 046003 (12 October 2017). https://doi.org/10.1117/1.JRS.11.046003

Received: 25 March 2017; Accepted: 15 September 2017; Published: 12 October 2017

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