Using satellite images to monitor the spatiotemporal distribution and irrigation dynamic of paddy fields

Abstract

Rice is the staple crop with the largest cultivation area and water demand in Taiwan. An accurate and rapid understanding of rice planting time, area, and growth period is beneficial for overall planning and improving the efficiency of agricultural water resource management. Remote sensing provides information such as high coverage, real-time, multispectral images, and multi-angle images. Various agricultural monitoring technologies have been developed for crop planting areas, yield estimation, and pest warnings. However, Taiwan, located in the tropical/subtropical region, faces challenges in obtaining high-quality optical satellite images due to rainfall and cloud cover. This leads to reduced accuracy in interpreting planting time and area. Therefore, the purpose of this study is to monitor the spatiotemporal distribution of paddy fields and irrigation dynamics using optical and radar satellite images. The study area is in western Taiwan, where the first rice planting period extends from January to March, with harvesting taking place from May to July, covering an area of approximately 90,000 to 120,000 hectares. Optical (Sentinel-2) and radar (Sentinel-1) image signals, along with the Maximum Likelihood method (supervised classification), were used to interpret the irrigation and rice planting distribution during the early stages of the first rice crop from 2021 to 2023. Finally, the rice planting area in the test area from January to March was calculated based on the irrigation distribution results, and the performance of the rice distribution area interpretation was evaluated. The results indicated that the average Kappa value for paddy field area interpretation was 0.92. The percentage of rice planting from January to March was 48±13%, 32±4%, and 29±14%, respectively. The monitoring process established in this study for rice irrigation and distribution areas contributes to government planning and decision-making regarding overall agricultural water allocation.