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Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data.

Jinglian Tian1,2,3, Yongzhong Tian1,2,3, Yan Cao1,2,3

  • 1Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

Accurate rice field mapping is crucial for food security. This study uses the Normalized Difference Vegetation Index (NDVI) difference method with Sentinel data to precisely extract rice fields, improving accuracy by addressing water edge effects.

Keywords:
ChongqingNDVINDWIextraction of rice fieldsrice harvesting period

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Area of Science:

  • Agricultural Remote Sensing
  • Geospatial Analysis
  • Food Security Monitoring

Background:

  • Accurate and timely rice field information is essential for global food security.
  • Existing rice field extraction methods face challenges in accuracy and efficiency.
  • Intra-annual variation of Normalized Difference Vegetation Index (NDVI) offers unique signatures for different land features.

Purpose of the Study:

  • To develop an accurate, rapid, and convenient method for rice field extraction using remote sensing data.
  • To leverage the unique NDVI variation characteristics of rice fields between pre-harvest and post-harvest periods.
  • To improve the precision of rice field mapping by mitigating the 'water edge effect'.

Main Methods:

  • Utilized Sentinel data and the NDVI difference method for rice field extraction.
  • Employed partial correlation and multiple regression analysis to model and determine optimal rice harvesting periods.
  • Applied the Normalized Difference Water Index (NDWI) to remove water edge effects and refine extraction results.

Main Results:

  • The rice harvesting period showed significant correlations with altitude (0.978) and latitude (0.922), enabling effective remote sensing image selection.
  • The NDVI difference method, combined with Sentinel data, proved excellent for rice field extraction.
  • Integrating NDWI effectively reduced the impact of mixed pixels and the water edge effect, significantly enhancing extraction accuracy.

Conclusions:

  • A robust simulation model for the rice harvesting period aids in selecting optimal remote sensing data for rice field mapping.
  • The NDVI difference method is a highly effective technique for extracting rice fields from Sentinel data.
  • Combining NDVI difference with NDWI is crucial for improving the accuracy of rice field extraction by minimizing water edge effects.