Retrieval of Forest Aboveground Biomass via Compact Polarimetric SAR Data

Zhao Han; Zhang Wangfei; ji Yongjie; Han Zongtao

doi:10.11834/jrs.20232363

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Retrieval of Forest Aboveground Biomass via Compact Polarimetric SAR Data
Pages: 1-11(2023)
Published Online： 15 March 2023 ，
DOI： 10.11834/jrs.20232363

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赵含，张王菲，姬永杰，韩宗涛.XXXX.简缩极化SAR数据支持的森林地上生物量反演.遥感学报，XX（XX）： 1-11

Zhao Han，Zhang Wangfei，ji Yongjie，Han Zongtao. XXXX. Retrieval of Forest Aboveground Biomass via Compact Polarimetric SAR Data. National Remote Sensing Bulletin， XX（XX）：1-11
赵含，张王菲，姬永杰，韩宗涛.XXXX.简缩极化SAR数据支持的森林地上生物量反演.遥感学报，XX（XX）： 1-11 DOI： 10.11834/jrs.20232363.

Zhao Han，Zhang Wangfei，ji Yongjie，Han Zongtao. XXXX. Retrieval of Forest Aboveground Biomass via Compact Polarimetric SAR Data. National Remote Sensing Bulletin， XX（XX）：1-11 DOI： 10.11834/jrs.20232363.

摘要

简缩极化（Compact Polarimetry， CP）SAR作为一种国内外学者高度关注的新型SAR，目前鲜有研究将其应用在森林地上生物量（Above Ground Biomass， AGB）反演研究中。在全球气候变化及“双碳”目标下，森林AGB的精确反演是当下亟待解决的热点问题。为探究CP SAR数据在森林AGB反演中的可行性，以宜良小哨林场为研究区，提取水平线性CP Stokes1模式、垂直线性CP Stokes2模式、π/4线性（π/4 Transmit and Dual Orthogonal Linear Receive）模式及CTLR（Circular Transmit and Dual Orthogonal Linear Receive）模式的4种CP SAR数据，并基于波的二分性原理，分别提取了各种模式的若干SAR参数，利用基于快速迭代特征选择的k最近邻（KNN-FIFS）算法开展了研究。研究结果表明：基于CTLR模式的森林AGB反演结果最优，R

= 0.52，RMSE = 13.02 t/hm

；此外，联合4组CP SAR数据的森林AGB反演结果精度有明显提升：R

= 0.58，RMSE = 12.16 t/hm

。KNN-FIFS适合于采用CP SAR参数进行森林AGB反演，其反演结果与采用全极化SAR数据进行反演的差别并不明显。提取的CP SAR参数中，线极化度

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104171&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104170&type=

2.96333337

3.21733332

，倾斜角45度或-45度时的线极化分量功率值

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104173&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104172&type=

2.70933342

3.89466691

等特征在森林AGB反演中表现出较高的适用性，说明其能更好的表征森林信息。

Abstract

Objective Compact Polarimetric synthetic aperture radar (CP SAR) is a new type SAR and it attracts most researchers especially on the application of CP SAR

however

few studies have been explored in the application of forest aboveground biomass (AGB) retrieval using CP SAR information. Under the global climate change and the goals of achieving peak carbon emissions and carbon neutrality

the accurate inversion of forest AGB become urgent in recent years. The paper aims to explore the feasibility of CP SAR data applied in forest AGB inversion.Method In this study

we took Xiaoshao Forest Farm in Yiliang county as the test site

using simulated CP SAR data from quad polarimetric GF-3 data with four modes

Stokes1 mode (Stokes related parameters were extracted from horizontal Transmit and Dual Orthogonal Linear Receive)

Stokes2 mode (Stokes related parameters were extracted from vertical Transmit and Dual Orthogonal Linear Receive)

the π/4 linear mode (π/4 Transmit and Orthogonal Linear Receive)

and CTLR mode (Circular Transmit and Dual Orthogonal Linear Receive) mode to explore the potential of CP SAR data in forest AGB estimation. First

several SAR parameters of various modes were extracted based on the theory of wave dichotomy

respectively

then the k Nearest Neighbor algorithms with Fast Iterative Feature Selection(KNN-FIFS)method were applied to estimate the forest AGB in the study area. Finally

the accuracy of the KNN-FIFS inversion results were verified using the LOOCV (Leave-One-Out-Cross-Validation) methods.Result R

= 0.28 and RMSE = 16.36 t/hm

were acquired for the forest AGB estimation using Stokes1 mode and the corresponding optimal feature combination was

、

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104175&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104174&type=

2.45533323

3.72533321

、

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104285&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104284&type=

1.60866666

2.28600001

; for Stokes2 mode

= 0.35 and RMSE = 14.96 t/hm

the corresponding optimal feature combination was

、

; Compared with Stokes1and Stokes2 modes

the similar performance was shown in π/4 mode for forest AGB estimation

the R

value was 0.34 while RMSE =15.21 t/hm

and the corresponding optimal feature combination is

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104179&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104178&type=

3.04800010

、

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104281&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104280&type=

3.04800010

、

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104273&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104182&type=

2.70933342

3.72533321

、

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104275&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104274&type=

2.70933342

3.72533321

; Among four CP SAR modes

best performance of the forest AGB inversion was shown in CTLR mode with R

= 0.52

RMSE = 13.02 t/hm

and the corresponding optimal feature combination is

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104281&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104280&type=

3.04800010

、

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104279&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104278&type=

4.06400013

3.21733332

. The forest AGB inversion result combining 4 sets of CP SAR parameters showed obvious improvement with R

= 0.58

RMSE = 12.16 t/hm

.Conclusion The CTLR CP SAR mode performed best in forest AGB estimation in the four CP SAR mode

when the parameters extracted from four CP SAR modes were combined and applied forest AGB estimation

the improvement of inversion result is obvious. KNN-FIFS is suitable for forest AGB estimation via CP SAR parameters

there is no obvious difference between the estimation results estimated using CTLR CP SAR data and quad polarimetric SAR data. Among all the extracted CP SAR parameters

the degree of linear polarization (

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104281&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104280&type=

3.04800010

)

and the power of the linear polarization component at a tilt angle of 45 degrees or 135 degrees (

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104283&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104282&type=

2.70933342

3.72533321

) show best performance in the forest AGB estimation since both of them are selected in all the four modes as the optimized features. It revealed that they can better characterize the forest AGB changes. Meanwhile

the parameters that can reflect the forest density to a certain extent (

)

the parameters that reflect the characteristics of the forest scattering direction (

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=41104285&type=

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1.60866666

2.28600001

)

and the parameters that represent the degree of forest depolarization all have good performance in the forest AGB inversion.

关键词

森林AGBGF-3Stokes简缩极化SARKNN-FIFS

Keywords

Forest AGBGF-3StokesCP SARKNN-FIFS

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