Leveraging Remote Sensing based Soil Moisture for High-resolution Irrigation Water Use Estimation and Validation with Reference Data

Irrigation water use constitutes the largest share of freshwater consumption by humans. With increasing water withdrawals for irrigation anticipated in the coming years due to population growth and climate change, there is an urgent need for effective strategies to manage agricultural water use sustainably. However, traditional methods for evaluating irrigation water use, such as administrative records and field surveys, are often constrained by limited spatial coverage, delays in reporting, and inconsistencies in data accuracy. These limitations significantly impede the timely and reliable assessment of irrigation practices, particularly in expansive canal command areas.

Satellite-based remote sensing offers a robust solution to these challenges by providing consistent, high-resolution data over large spatial and temporal scales. The complementary strengths of microwave and optical remote sensing are particularly advantageous in estimating soil moisture. Microwave sensors, with their ability to penetrate clouds and operate in all weather conditions, are effective in deriving baseline soil moisture estimates. Optical sensors, such as those on Sentinel-2, enhance these estimates through high spatial and temporal resolution data that capture vegetation dynamics and surface conditions. Models like OPTRAM (Optical Trapezoid Model), which utilizes optical indices such as NDVI and land surface temperature (LST), further enable the derivation of soil moisture by linking vegetation health and thermal properties to soil water content. This integration of optical and microwave data improves the accuracy and spatial detail of soil moisture estimates.

This study addresses these issues by utilizing satellite-based remote sensing products to estimate irrigation water use and validate these estimates with ground-based observations from provincial irrigation departments. High-resolution soil moisture estimates will be developed by downscaling microwave-based remote sensing products from SMAP at 1 km resolution using MODIS products, and at 20 m resolution using Sentinel-2 imagery. These estimates will be validated with ground-based soil moisture sensors. The downscaled soil moisture products will form the basis for a soil moisture-based inversion model to quantify irrigation water amounts at fine spatial and temporal scales.

By integrating remote sensing-derived estimates with ground-based water allocation data, this study seeks to enhance the accuracy and reliability of irrigation water use assessments. The outcomes of this study will provide actionable insights for water resource managers, policymakers, and irrigation departments, leading to more effective management of surface water supply, improved water allocation, and enhanced agricultural sustainability in high irrigated areas.