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Remote Sensing and GIScience pp 185–203Cite as

Mapping Rice Growth Stages Employing MODIS NDVI and ALOS AVNIR-2

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Abstract

Rice, a staple food of most Asian inhabitants, is broadly cultivated and has attracted substantial research interest in the past few decades. Monitoring rice production areas is important due to the growing global demand for the crop. Since cropping systems vary across time and space, frequent monitoring over broad areas is required. This research exploits Moderate Resolution Imaging Spectroradiometer (MODIS) data to identify the stage of rice growth, and Advanced Land Observing Satellite–Advanced Visible and Near Infrared Radiometer Type 2 (ALOS AVNIR-2) to map the stages. Exploiting X12-ARIMA to decompose Normalized Difference Vegetation Index (NDVI) time-series for growth-stage indication and five classifiers for mapping the growth stages, the framework was tested in Indonesia’s “paddy basket,” namely the North Coastal Region of West Java. A conventional classifier, Maximum Likelihood, was compared with some decision tree algorithms, namely Classification Rule with Unbiased Interaction Selection (CRUISE) and Quick, Unbiased, Efficient, Statistical Tree (QUEST); neural network (NN); and support vector machine (SVM) for mapping the growth stages. The seasonal component of time-series decomposition assisted in indicating the stages. Meanwhile, decision tree algorithms produced interpretable rules for rice growth stages while the spatial representation of SVM and NN was closer to the ground truth.

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Acknowledgements

The authors are indebted to the United States Geological Survey (USGS), PT Sang Hyang Seri, the Japan Aerospace Exploration Agency (JAXA) for data access, Australia Awards scholarship, and UNSW Canberra for research facilities. The MOD13Q1 data product was retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, (https://lpdaac.usgs.gov/data_access/data_pool). ALOS AVNIR was provided by JAXA under grant number RA6-3040. Thanks to Julie Kesby (previously with PEMS, UNSW, Canberra) for providing editorial advice.

Author information

Authors and Affiliations

  1. Department of Soil Science and Land Resource, Bogor Agricultural University, Bogor, Indonesia

    Dyah R. Panuju & Bambang H. Trisasongko

  2. School of Science, The University of New South Wales, Canberra, Australia

    David J. Paull

  3. Geospatial Science Discipline, School of Science, RMIT University, Melbourne, Australia

    Amy L. Griffin

Authors
  1. Dyah R. Panuju
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  2. David J. Paull
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  3. Amy L. Griffin
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  4. Bambang H. Trisasongko
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Corresponding author

Correspondence to Dyah R. Panuju .

Editor information

Editors and Affiliations

  1. College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, Uttar Pradesh, India

    Pavan Kumar

  2. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, Delhi, India

    Haroon Sajjad

  3. Department of Geophysics, Kurukshetra University, Kurukshetra, Haryana, India

    Bhagwan Singh Chaudhary

  4. Remote Sensing and GIS, Kumaun University, Almora, Uttarakhand, India

    J. S. Rawat

  5. Department of Geography, Kumaun University, Nainital, Uttarakhand, India

    Meenu Rani

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Panuju, D.R., Paull, D.J., Griffin, A.L., Trisasongko, B.H. (2021). Mapping Rice Growth Stages Employing MODIS NDVI and ALOS AVNIR-2. In: Kumar, P., Sajjad, H., Chaudhary, B.S., Rawat, J.S., Rani, M. (eds) Remote Sensing and GIScience . Springer, Cham. https://doi.org/10.1007/978-3-030-55092-9_11

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