Scatterometers for leaf area index estimation: A review

Abstract

Scatterometers are active remote sensing instruments that emit and receive radio detection and ranging (RADAR) pulses backscattered from targeted features on the Earth's surface. Scatterometer-based leaf area index (LAI) estimation is demonstrated as a potential approach to understand ecosystem productivity, crop yields, vegetation health, etc., as LAI serves as an important variable in empirical, semi-empirical, and process-based models used in studying vegetation dynamics. Scatterometers are anticipated to facilitate large-scale, uninterrupted LAI monitoring as they penetrate dense vegetation canopies under varying weather conditions. LAI computation using a scatterometer demands backscattering signal strength measurement, which primarily depends upon the dynamics of vegetation structure, surface texture, and soil moisture. The water-cloud model estimates soil moisture of areas possessing vegetation using backscattering data. The model provides a precise estimation of LAI as it can explicitly distinguish between the soil and vegetation backscatter. Further enhancement of these estimations can be carried out by data fusion methods that incorporate the scatterometer data with optical or synthetic aperture radar data. With the aid of case studies, this chapter demonstrates versatile applications of scatterometer across a wide range of environmental aspects, including estimation of above-ground biomass, carbon sequestration, soil moisture, yield prediction, vegetation structure analysis, etc., for forest, agriculture, and grassland ecosystems. Scatterometer data in agricultural areas provides accurate production calculation and periodic crop growth monitoring. Because scatterometers provide a stable and scalable method of measuring LAI, they may be used to monitor vegetation dynamics, agricultural productivity, and environmental changes globally. © 2026 Elsevier Ltd. All rights reserved..