A Review of Forest Height Inversion by PolInSAR: Theory, Advances, and Perspectives

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 15; p. 3781
• Main Authors: Xing, Cheng, Wang, Hongmiao, Zhang, Zhanjie, Yin, Junjun, Yang, Jian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Algorithms; Biomass; Carbon cycle; case studies; China; Comparative analysis; Data acquisition; data collection; forest height inversion; Forests; Forests and forestry; Interferometry; Measurement; microwave remote sensing; Parameter estimation; polarimetry; PolInSAR; Remote sensing; Reviews; Scattering; Synthetic aperture radar; Topography; Trees; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15153781

Forests cover approximately one-third of the Earth’s land surface and constitute the core region of the carbon cycle on Earth. The paramount importance and multi-purpose applications of forest monitoring have gained widespread recognition over recent decades. Polarimetric synthetic aperture radar interferometry (PolInSAR) has been demonstrated as a promising technique to retrieve the forest height over large areas with a limited cost. This paper presents an overview of forest height inversion (FHI) techniques based on PolInSAR data. Firstly, we introduce the basic theories of PolInSAR and FHI procedures. Next, we review the established data-based algorithms for single-baseline data and describe innovative techniques related to multi-baseline data. Then, the model-based algorithms are also introduced with their corresponding forest scattering models under multiple data acquisition modes. Subsequently, a case study is presented to demonstrate the applicable scenarios and advantages of different algorithms. Model-based algorithms can provide accurate results when the scene and forest properties are well understood and the model assumptions are valid. Data-based algorithms, on the other hand, can handle complex scattering scenarios and are generally more robust to uncertainties in the input parameters. Finally, the prospect of forest height inversion was analyzed. It is our hope that this review will provide guidelines to future researchers to enhance further FHI algorithmic developments.