A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam

Rice crop monitoring is an important activity for crop management. This study aimed to develop a phenology-based classification approach for the assessment of rice cropping systems in Mekong Delta, Vietnam, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data were processed from December 2000, to December 2012, using empirical mode decomposition (EMD) in three main steps: (1) data pre-processing to construct the smooth MODIS enhanced vegetation index (EVI) time-series data; (2) rice crop classification; and (3) accuracy assessment. The comparisons between the classification maps and the ground reference data indicated overall accuracies and Kappa coefficients, respectively, of 81.4% and 0.75 for 2002, 80.6% and 0.74 for 2006 and 85.5% and 0.81 for 2012. The results by comparisons between MODIS-derived rice area and rice area statistics were slightly overestimated, with a relative error in area (REA) from 0.9–15.9%. There was, however, a close correlation between the two datasets (R2 ≥ 0.89). From 2001 to 2012, the areas of triple-cropped rice increased approximately 31.6%, while those of the single-cropped rain-fed rice, double-cropped irrigated rice and double-cropped rain-fed rice decreased roughly −5.0%, −19.2% and −7.4%, respectively. This study demonstrates the validity of such an approach for rice-crop monitoring with MODIS data and could be transferable to other regions.

[1]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  T. M. Lillesand,et al.  Rapid maximum likelihood classification , 1991 .

[3]  Chuang Liu,et al.  Seasonal Variation of MODIS Vegetation Indexes and Their Statistical Relationship With Climate Over the Subtropic Evergreen Forest in Zhejiang, China , 2007, IEEE Geoscience and Remote Sensing Letters.

[4]  Christopher O. Justice,et al.  Estimating Global Cropland Extent with Multi-year MODIS Data , 2010, Remote. Sens..

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

[6]  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 .

[7]  Adriana Paolantonio,et al.  OECD-FAO, Agricultural Outlook 2008-2017 , 2008 .

[8]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[9]  D. Molden,et al.  Fourier analysis of historical NOAA time series data to estimate bimodal agriculture , 2007 .

[10]  Dominique Bachelet,et al.  Modelling the Impact of Climate Change on Rice Production in Asia , 1995 .

[11]  T. M. Stout,et al.  Central Great Plains , 1965 .

[12]  R. Chanda,et al.  Vulnerability Assessment of the Maize and Sorghum Crops to Climate Change in Botswana , 2003 .

[13]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[14]  Thomas J. Overcamp,et al.  Estimation of Southeast Asian rice paddy areas with different ecosystems from moderate-resolution satellite imagery , 2012 .

[15]  Robert E. Evenson,et al.  Crop Variety Improvement and its Effect on Productivity: The Impact of International Agricultural Research , 2003 .

[16]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[17]  Daniela Stroppiana,et al.  Forest leaf area index in an Alpine valley from medium resolution satellite imagery and in situ data , 2012 .

[18]  Robin Matthews,et al.  Modelling the impacts of climate change and methane emission reductions on rice production: a review , 2003 .

[19]  Anatoly A. Gitelson,et al.  Monitoring Maize (Zea mays L.) Phenology with Remote Sensing , 2004 .

[20]  S. Lawrence Dingman,et al.  Effective sea-level rise and deltas: Causes of change and human dimension implications , 2006 .

[21]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[22]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[23]  E. Hille,et al.  Calculus: One and Several Variables , 1971 .

[24]  Ai Hiramatsu,et al.  Adaptation and Mitigation Strategies for Climate Change , 2010 .

[25]  K. Shadan,et al.  Available online: , 2012 .

[26]  A. Viña,et al.  Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .

[27]  Chen Zhongxin,et al.  Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data , 2008 .

[28]  J. Tenhunen,et al.  On the relationship of NDVI with leaf area index in a deciduous forest site , 2005 .

[29]  C. Timmer,et al.  A World Without Agriculture: The Structural Transformation in Historical Perspective , 2009 .

[30]  N. T. Son,et al.  Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta, Vietnam , 2011 .

[31]  K. Cassman,et al.  Rice yields decline with higher night temperature from global warming. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[33]  M. Boschetti,et al.  Multi-year monitoring of rice crop phenology through time series analysis of MODIS images , 2009 .

[34]  Chi-Farn Chen,et al.  Satellite-based investigation of flood-affected rice cultivation areas in Chao Phraya River Delta, Thailand , 2013 .

[35]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[36]  Jun Furuya,et al.  Impact of global warming on agricultural product markets: stochastic world food model analysis , 2009 .

[37]  Prasad S. Thenkabail,et al.  Mapping rice areas of South Asia using MODIS multitemporal data , 2011 .

[38]  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 .

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

[40]  N. van Duivenbooden,et al.  Impact of Climate Change on Agricultural Production in the Sahel – Part 2. Case Study for Groundnut and Cowpea in Niger , 2002 .

[41]  Chi-Farn Chen,et al.  Investigating Rice Cropping Practices and Growing Areas from MODIS Data Using Empirical Mode Decomposition and Support Vector Machines , 2012 .

[42]  N. V. Nguyen,et al.  Global climate changes and rice food security , 2005 .

[43]  D. Bachelet,et al.  Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation , 1997 .

[44]  Chi-Farn Chen,et al.  Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier , 2011 .

[45]  Su-Wei Huang,et al.  Mapping double-cropped irrigated rice fields in Taiwan using time-series Satellite Pour I'Observation de la Terre data , 2011 .