Study on monthly irrigated area monitoring based on multi-source long time-series remote sensing image cloud computing

Abstract

[Objective] The accurate accounting of irrigated area is of great significance for guiding agricultural irrigation and water consumption allocation. However, due to limited availability of optical remote sensing imagery, insufficient ground observation data in irrigated area, and low update frequency of public data, there are still many challenges in achieving rapid identification of irrigated area at the regional scale. The aim of the study is to support the rapid monitoring of irrigated area using multi-source satellite data. [Methods] Based on optical and radar long time-series remote sensing images, a method for acquiring monthly historical samples from published datasets was proposed on the Google Earth Engine cloud computing platform. By integrating multi-source features, including radar, spectral, hydrothermal, environmental, and temporal data, rapid identification of monthly synthesized irrigated area at the regional scale was achieved for Washington State, USA, from 2016 to 2017. [Results] The result showed that 10% ~17% of the land in the core area of the study area had obvious irrigation activities during the growing season from April to October, with the irrigated area at the initial stage reaching 800 km² to 1 100 km², the irrigated area during peak growing season exceeding 1 600 km², and the total annual irrigated area reaching 2 200 km². The overall accuracy of monthly irrigated area identification in the study area was 84. 76%(Kappa coefficient = 0. 694 4). [Conclusion] Compared with the publicly available remote sensing irrigation datasets and regional annual agricultural census data, the proposed multi-source image cloud computing method enables more effective and accurate identification of monthly irrigated area distribution. Meanwhile, it can improve the frequency of irrigation information updates and reduce the risk of underestimating irrigated area caused by agricultural census, thereby providing a crucial basis for research on crop water demand and irrigation water consumption assessment.

Keywords

irrigated area / Google Earth Engine / long time-series remote sensing / multi-source features / monthly irrigation / Synthetic Aperture Radar(SAR) / remote sensing cloud computing / irrigation water management