Mapping irrigated areas and assessing net-irrigation by passive and active remote sensing datasets | |
| Author | Putalan, Kysiah Dalisay |
| Call Number | AIT Thesis no.WM-25-05 |
| Subject(s) | Irrigation--Remote sensing Soil moisture--Remote sensing Soil moisture--Data processing |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Water Engineering and Management |
| Publisher | Asian Institute of Technology |
| Abstract | Understanding the actual area, timing, and amount of irrigation at plot scale level has been a challenge to evaluate irrigation efficiency. Agricultural irrigation is the largest consumer of fresh water worldwide, accounting for nearly 70% of global water resources used for food production, often resulting in significant water losses and reduced efficiency. Traditional monitoring methods, which depend on limited head level data and indirect field measurements, failed to provide adequate insights into irrigation dynamics. However, advancements in satellite remote sensing presented a promising solution. Satellites frequent monitoring mechanisms can remotely observe irrigation practices, enabling detailed mapping of irrigation areas and estimating applied water volumes.This study utilized the capabilities of active and passive radar images to detect irrigation signals over the Kheang Khoi Ban Mor under the Pasak Basin, which is highly equipped with irrigation for rice production. Detecting surface soil moisture (SSM) changes is the key to identifying irrigated areas and estimating the timing and amount of irrigated water to the fields. The study aimed to enhance the temporal resolution of satellite imagery by temporal fusion of Soil Moisture Active Passive (SMAP) and Sentinel-1. This method was further employed to identify irrigation events and estimate the amount of net-irrigation through satellite derived fused surface soil moisture and the natural rainfall-soil moisture.The temporal resolution of the surface soil moisture dataset was effectively increased by 23% for the span of two years with acceptable RMSE, R2, R, and PBIAS to half of the representative monthly images. The biggest challenge was the varying spatial resolution of the datasets analyzing in a small-scale study area. The coarse nature of the source data, especially precipitation data with 31km spatial resolution, made it challenging to capture irrigation dynamics at plot scale (30m). The resampling caused abrupt pixel-to-pixel variation making it a challenge to plot the irrigated area at plot scale. |
| Year | 2025 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Water Engineering and Management (WM) |
| Chairperson(s) | Shanmugam, Mohana Sundaram |
| Examination Committee(s) | Shrestha, Sangam;Natthachet Tangdamrongsub;Sarawut Ninsawat |
| Scholarship Donor(s) | Global Water and Sanitation Center (GWSC);AIT Scholarship |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2025 |