Satellite Data Rendered Irrigation using Penman–Monteith and SEBAL-sDRIPS for Surface Water Irrigation Optimization

Abstract

This study proposes a satellite remote sensing-based water-provider-centric irrigation advisory system designed to manage surface water resources and allocate water efficiently to areas in need, thereby promoting sustainable irrigation practices in the context of a changing climate. The system utilizes satellite remote sensing based SEBAL (Surface Energy Balance Algorithm for Land) and Penman–Monteith evapotranspiration models to estimate crop water use. By integrating the responses from the previous irrigation cycle, current precipitation, forecasted precipitation, and evapotranspiration-based water needs, the framework calculates the net water requirements for command areas within irrigation canal networks. Operating on a weekly basis, the system generates advisories that enable the irrigation water provider to make informed, science-based decisions about water allocation. These advisories quantify the net water requirement, giving water providers the flexibility to dispatch water to areas of higher need based on their on-ground judgment. Additionally, the proposed framework can simulate future cropping patterns by assuming potential policy changes or net reduction in water supply in the main canal due to climate change or increased transboundary withdrawal. The advisory system is co-developed and implemented with the irrigation management agency called Bangladesh Water Development Board on the Teesta River Irrigation System located in Northern Bangladesh. The study demonstrates its effectiveness when compared against actual water supplied for irrigation. However, the application of sDRIPS is not limited to Bangladesh, as it is scalable to other regions with similar water management challenges for agriculture.