Remote Sensing and GIS Based Approach to Estimate the Monthly and Seasonally Evapotranspiration (ET) For Kharif Maize Using Landsat 8 Data and the QWaterModel

Abstract

Evapotranspiration (ET) plays a crucial role in agricultural water management, particularly in semi-arid regions where efficient resource allocation is essential for sustainable production. This study utilizes remote sensing and GIS-based approaches to estimate monthly and seasonal ET for kharif maize using Landsat 8 satellite data from 2016 to 2020. The research was conducted at the Main Maize Research Station (MMRS) in Panchmahal, Gujarat, India, leveraging the QWaterModel plugin for ET calculations. Thermal and optical data from Landsat 8, including Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), were processed to derive ET values for the maize-growing season (July to October). The findings reveal distinct temporal trends in ET. Monthly ET progressively increases from July (early growth stages) to September (peak growth), followed by a sharp decline in October (maturity stage). Across the study years, July ET ranged from 42 mm in 2016 to 62 mm in 2018, while September ET peaked at 251 mm in 2020. Seasonal ET varied between 427 mm (2016) and 463 mm (2019), reflecting fluctuations in climatic conditions and management practices. Seasonal ET values exhibit slight variations across the years, ranging from a low of 427 mm in 2016 to a high of 463 mm in 2019. This study emphasizes the application of water to maize crops by the canal command authority, ensuring that the water provided aligns with the crop's growth stages. Initially, maize requires less water, followed by a substantial increase in demand during the mid-season, while towards the end of the season, water application becomes almost unnecessary. Remote sensing offers a cost-effective and scalable solution for large-area ET estimation compared to traditional methods which are limited in spatial coverage and resource-intensive.