Giovanni Anconitano;Lorenzo Giuliano Papale;Leila Guerriero;Mario Alberto Acuña;Nazzareno Pierdicca
{"title":"利用辐射传输模型标定雷达极化分解","authors":"Giovanni Anconitano;Lorenzo Giuliano Papale;Leila Guerriero;Mario Alberto Acuña;Nazzareno Pierdicca","doi":"10.1109/LGRS.2025.3588254","DOIUrl":null,"url":null,"abstract":"This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman–Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained by applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on the L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the root mean square error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in-situ data, shows a significant improvement. The calibrated GFD powers lead to an increased linear correlation (from 0.32 to 0.57), while the RMSE is reduced (from 0.096 to 0.055 m<sup>3</sup>/m<sup>3</sup>).","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4000,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078365","citationCount":"0","resultStr":"{\"title\":\"Calibration of a Radar Polarimetric Decomposition Using a Radiative Transfer Model\",\"authors\":\"Giovanni Anconitano;Lorenzo Giuliano Papale;Leila Guerriero;Mario Alberto Acuña;Nazzareno Pierdicca\",\"doi\":\"10.1109/LGRS.2025.3588254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman–Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained by applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on the L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the root mean square error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in-situ data, shows a significant improvement. The calibrated GFD powers lead to an increased linear correlation (from 0.32 to 0.57), while the RMSE is reduced (from 0.096 to 0.055 m<sup>3</sup>/m<sup>3</sup>).\",\"PeriodicalId\":91017,\"journal\":{\"name\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"volume\":\"22 \",\"pages\":\"1-5\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078365\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11078365/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11078365/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Calibration of a Radar Polarimetric Decomposition Using a Radiative Transfer Model
This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman–Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained by applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on the L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the root mean square error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in-situ data, shows a significant improvement. The calibrated GFD powers lead to an increased linear correlation (from 0.32 to 0.57), while the RMSE is reduced (from 0.096 to 0.055 m3/m3).