An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand

He Li,Fenzhen Su,Chong Huang,Gaohuan Liu,Qingsheng Liu,Dongjie Fu,Shangrong Wu

doi:10.3390/rs12233959

Abstract

Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia.

Highlights

Fields of paddy rice, one of the three leading grain crops throughout the world, account for approximately 12% of global cropland area and feed more than half of the world’s population [1]
The research on Synthetic aperture radar (SAR) remote sensing mapping of rice paddies has lasted for more than 20 years, the development of operational automatic and rapid rice identification methods combined with high spatial resolution and long time-series SAR data, is still the hotspot and frontier in current rice mapping research
By integrating 402 Sentinel-1 scenes, auxiliary data of permanent water bodies, and data on the general phenological characteristics of rice with the Google Earth Engine geospatial data cloud computing platform, the proposed method can rapidly identify the extent of rice paddies without needing local expert experience or field sample plots

Summary

Introduction

One of the three leading grain crops throughout the world, account for approximately 12% of global cropland area and feed more than half of the world’s population [1]. Efficient and timely collection of accurate information on the geographic location, distribution, and extent of rice paddies is important for sustainable rice production, water and food security, greenhouse gas emissions, environmental sustainability, effective decision making, and policy management [2,6,7,8,9,10,11,12,13]. Despite its significance for water and food security and global change research, the extent of rice paddies worldwide still remains uncertain [14,15]. Those existing maps covering the continental or global scale have been derived primarily from regional or national statistics, which cannot reflect the extent or distribution of rice paddies across large areas [16,17]. Remote sensing has gradually become the core alternative for rice paddy mapping over large areas due to its low cost, scalability, unbiased estimates, high temporal resolution, and spatial continuity [18,19]

Methods

Results

Discussion

Conclusion