View in EDS

A PolSAR rotation model for crop classification and soil moisture retrieval in complex agricultural environments.

Saved in:

Bibliographic Details
Title:	A PolSAR rotation model for crop classification and soil moisture retrieval in complex agricultural environments.
Authors:	Han, Wentao¹ (AUTHOR) xtuhwt@xtu.edu.cn, Wang, Mingxu¹ (AUTHOR), Huang, Dengshan¹ (AUTHOR), Xie, Qinghua² (AUTHOR), Fu, Haiqiang³ (AUTHOR), Zhou, Cui⁴ (AUTHOR), Zhu, Jianjun³ (AUTHOR)
Source:	International Journal of Remote Sensing. Jun2026, Vol. 47 Issue 12, p5246-5267. 22p.
Subjects:	Soil moisture, Vegetation classification, Optical polarization, Polarimetric remote sensing, Parameter estimation, Agricultural ecology, Remote sensing, Scattering (Mathematics)
Geographic Terms:	Canada
Abstract:	In complex agricultural scenes, the structure, orientation and dielectric properties of scatterers exhibit significant heterogeneity, necessitating the development of highly adaptive scattering models to avoid parameter estimation biases caused by mismatches between models and actual conditions. The adaptability of such models is largely driven by polarization rotation mechanisms. Current research primarily focuses on polarization orientation angle (POA) rotation related to the ${T_{23}}$ T 23 (${T_{23}}\_RO$ T 23 _ RO). However, the effects of ${T_{12}}$ T 12 -related rotation (${T_{12}}\_RO$ T 12 _ RO) and ${T_{13}}$ T 13 -related rotation (${T_{13}}\_RO$ T 13 _ RO) have yet to be systematically investigated. This limitation limits model adaptability and compromises the accuracy of both crop classification and soil moisture (SM) retrieval. To address this gap, this study develops a rotation scattering model (ROM) that integrates ${T_{23}}\_RO$ T 23 _ RO , ${T_{12}}\_RO$ T 12 _ RO and ${T_{13}}\_RO$ T 13 _ RO. The proposed ROM aims to leverage its inherent adaptability to improve the accuracy of crop parameter retrieval. UAVSAR data covering the Winnipeg region of Manitoba, Canada, were utilized to evaluate the ROM's effectiveness in varied SM and vegetation coverage conditions. Experimental results demonstrate that the model parameters of ROM carry clear physical meanings, effectively characterizing both structure, randomness and dielectric constant of targets. These physically interpretable parameters contribute to improved crop classification accuracy, yielding an overall increase of 3.59% compared to conventional general scattering models. Furthermore, the model's adaptive power transformation capability across polarization channels allows it to better accommodate variations in surface roughness. In SM retrieval, the ROM can achieve a root mean square error (RMSE) of 8.39% and a correlation coefficient of 0.71. Importantly, experiments using UAVSAR data under varying moisture revealed that for optimal parameter inversion, PolSAR data acquisition should be scheduled to avoid periods of low moisture content. [ABSTRACT FROM AUTHOR]
	Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	In complex agricultural scenes, the structure, orientation and dielectric properties of scatterers exhibit significant heterogeneity, necessitating the development of highly adaptive scattering models to avoid parameter estimation biases caused by mismatches between models and actual conditions. The adaptability of such models is largely driven by polarization rotation mechanisms. Current research primarily focuses on polarization orientation angle (POA) rotation related to the ${T_{23}}$ T 23 (${T_{23}}\_RO$ T 23 _ RO). However, the effects of ${T_{12}}$ T 12 -related rotation (${T_{12}}\_RO$ T 12 _ RO) and ${T_{13}}$ T 13 -related rotation (${T_{13}}\_RO$ T 13 _ RO) have yet to be systematically investigated. This limitation limits model adaptability and compromises the accuracy of both crop classification and soil moisture (SM) retrieval. To address this gap, this study develops a rotation scattering model (ROM) that integrates ${T_{23}}\_RO$ T 23 _ RO , ${T_{12}}\_RO$ T 12 _ RO and ${T_{13}}\_RO$ T 13 _ RO. The proposed ROM aims to leverage its inherent adaptability to improve the accuracy of crop parameter retrieval. UAVSAR data covering the Winnipeg region of Manitoba, Canada, were utilized to evaluate the ROM's effectiveness in varied SM and vegetation coverage conditions. Experimental results demonstrate that the model parameters of ROM carry clear physical meanings, effectively characterizing both structure, randomness and dielectric constant of targets. These physically interpretable parameters contribute to improved crop classification accuracy, yielding an overall increase of 3.59% compared to conventional general scattering models. Furthermore, the model's adaptive power transformation capability across polarization channels allows it to better accommodate variations in surface roughness. In SM retrieval, the ROM can achieve a root mean square error (RMSE) of 8.39% and a correlation coefficient of 0.71. Importantly, experiments using UAVSAR data under varying moisture revealed that for optimal parameter inversion, PolSAR data acquisition should be scheduled to avoid periods of low moisture content. [ABSTRACT FROM AUTHOR]
ISSN:	01431161
DOI:	10.1080/01431161.2026.2664867