Exploiting Sentinel-1 data and machine learning–based random forest for collectively mapping rice fields in Taiwan

Nguyen Thanh Son, Chi Farn Chen, Cheng Ru Chen, Youg Sin Cheng, Piero Toscano, Chein Hui Syu, Horng Yuh Guo, Shu Ling Chen, Tsang Sen Liu, Yi Ting Zhang, Huan Sheng Lin, Shih Hsiang Chen, Miguel Valdez

Research output: Contribution to journalArticlepeer-review

Abstract

Rice is the most important crop in Taiwan. Monitoring rice-growing areas is thus essential for crop management and food decision-making processes. This research aims to develop an approach for seasonally mapping rice areas from time-series Sentinel-1 data in Taiwan. The data were processed for 2019 and 2020 rice cropping seasons, following three main steps: (1) data pre-processing to construct smooth time-series satellite data, (2) rice area estimation using random forests (RF), and (3) accuracy assessment. The mapping results compared with the government’s reference data showed overall accuracy and kappa coefficient higher than 87.7% and 0.76, respectively. The rice area estimates at the county level well agreed with the official statistics, with the root mean square error (RMSE) in percentage smaller than 19.7%. An examination of changes in cropping areas between 2019 and 2020 showed a noticeable reduction of rice areas in 2020, mainly attributed to severe drought conditions.

Original languageEnglish
JournalApplied Geomatics
DOIs
StateAccepted/In press - 2022

Keywords

  • Cropping patterns
  • Random forests
  • Rice-cultivated area
  • Sentinel-1 SAR data

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