Exploiting Sentinel-1 data and machine learning–based random forest for collectively mapping rice fields in Taiwan
Journal
Applied Geomatics
Journal Volume
14
Journal Issue
2
Start Page
405
End Page
419
Date Issued
2022-06
Author(s)
Nguyen-Thanh Son
Chi-Farn Chen
Cheng-Ru Chen
Youg-Sin Cheng
Piero Toscano
農業部農業試驗所
Horng-Yuh Guo
Shu-Ling Chen
農業部農業試驗所
農業部農業試驗所
Huan-Sheng Lin
Shih-Hsiang Chen
Miguel Valdez
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.
Subjects
Sentinel-1 SAR data
Rice-cultivated area
Cropping patterns
Random forests
Publisher
Springer Heidelberg
Type
journal article