Mapping paddy rice areas based on vegetation phenology and surface moisture conditions

Bingwen Qiu, Weijiao Li, Zhenghong Tang, Chongcheng Chen, Wen Qi

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

Accurate and timely rice mapping is important for food security and environmental sustainability. We developed a novel approach for rice mapping through Combined Consideration of Vegetation phenology and Surface water variations (CCVS). Variation of the Land Surface Water Index (LSWI) in rice fields was relatively smaller than that in other crops fields during the period from tillering to heading dates. Therefore, the ratios of change amplitude of LSWI to 2-band Enhanced Vegetation Index 2 (EVI2) during that period were utilized as the primary metric for paddy rice mapping. This algorithm was applied to map paddy rice fields in southern China using an 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) in 2013. The resultant rice cropping map was consistent with the agricultural census data (r2 = 0.8258) and ground truth observations (overall accuracy = 93.4%). Validation with Landsat Thematic Mapper images in test regions also revealed its high accuracy (with overall accuracy of 94.3% and kappa coefficient of 0.86). The proposed CCVS method was more robust to intra-class variability and other related uncertainties compared with other related methods in rice mapping. Its successful application in southern China revealed its efficiency and great potential for further utilization.

Original languageEnglish (US)
Pages (from-to)79-86
Number of pages8
JournalEcological Indicators
Volume56
DOIs
StatePublished - Sep 1 2015

Keywords

  • EVI2
  • LSWI
  • MODIS
  • Paddy rice
  • Phenological stage
  • Time series analysis

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Ecology, Evolution, Behavior and Systematics
  • Ecology

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