Estimation of paddy yield with AI-based model using scatterometers

Abstract

Scatterometers are specified radar instruments designed to measure the reflection of microwaves received back from the Earth's surface. Paddy is recognised as the primary food crop worldwide and accounts for up to 85% of its total global production. It is consumed as a source of carbohydrates (a high source of primary energy). This chapter provides basic knowledge related to the life cycle and remote sensing properties of paddy, which also includes the operations of scatterometers in the field of modern agricultural practices, and highlights the use of different processes to estimate paddy yield. This technology helps in the identification of backscattering and clustering properties of soil moisture content and paddy phenology (biomass, leaf area index, grain length, grain width, grain thickness, leaf length, leaf width, leaf thickness, and canopy structure). This identification is done by using the backscattering coefficient (σ°) where different models are applied. Scatterometer satellite (SCATSAT-1) (known as Ku-band-based scatterometer satellite 1) is used to measure vegetative dynamics, where the σ° of rice fields was measured at horizontal transmit-horizontal receive and vertical transmit-vertical receive polarisations. It is a dual pencil beam-based scatterometer. SCATSAT-1 is used to study the paddy crop growth at different stages with measured soil moisture content. The implementation of different AI-based software to estimate paddy yield ultimately increases the potential to revolutionise the agriculture sector by improving sustainable and effective food synthesis, monitoring crop different development stages and assisting farmers and researchers to analyse data to obtain paddy yield. By accurately forecasting rice yields, this book chapter highlights the critical role that scatterometers play in promoting sustainable agricultural methods and preserving global food safety. © 2026 Elsevier Ltd. All rights reserved..