A phenology-based classification of time-series MODIS data for rice crop monitoring in Mekong Delta, Vietnam

Nguyen Thanh Son, Chi Farn Chen, Cheng Ru Chen, Huynh Ngoc Duc, Ly Yu Chang

Research output: Contribution to journalArticlepeer-review

104 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)135-156
Number of pages22
JournalRemote Sensing
Volume6
Issue number1
DOIs
StatePublished - 2013

Keywords

  • EMD
  • MODIS
  • Phenology-based classification
  • Rice cropping systems

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