一种改进的星载GNSS-R森林生物量反演方法

周勋; 郑南山; 丁锐; 章恒一; 何佳星

doi:10.13700/j.bh.1001-5965.2022.0654

一种改进的星载GNSS-R森林生物量反演方法

doi: 10.13700/j.bh.1001-5965.2022.0654

周勋¹,
郑南山^{1, 2, ,},
丁锐^{1, 2},
章恒一¹,
何佳星¹

1.
中国矿业大学环境与测绘学院，徐州 221116
2.
中国矿业大学自然资源部国土环境与灾害监测重点实验室徐州 221116

基金项目: 国家自然科学基金(41974039)；自然资源部国土环境与灾害监测重点实验室开放基金(LEDM2021B11)；江苏省研究生科研与实践创新计划(KYCX22_2595)；中国矿业大学研究生创新计划项目(2022WLJCRCZL256)

详细信息

通讯作者:
E-mail： znshcumt@163.com

中图分类号: P237
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出版历程
- 收稿日期: 2022-07-27
- 录用日期: 2022-09-02
- 网络出版日期: 2022-11-08
- 整期出版日期: 2024-08-28

An improved inversion method of forest biomass based on satellite GNSS-R

ZHOU Xun¹,
ZHENG Nanshan^{1, 2
, ,},
DING Rui^{1, 2},
ZHANG Hengyi¹,
HE Jiaxing¹

1.
School of Environment and Spatial Informatics，China University of Mining and Technology，Xuzhou 221116，China
2.
Key Laboratory of Land Environment and Disaster Monitoring，Ministry of Natural Resources，China university of mining and technology，Xuzhou 221116，China

Funds: National Natural Science Foundation of China (41974039); Open Research Fund of Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources (LEDM2021B11); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_2595); Graduate Innovation Program of China University of Mining and Technology (2022WLJCRCZL256)

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Corresponding author: E-mail： znshcumt@163.com

摘要

摘要:
基于Tau-Omega模型提出一种涉及地面土壤湿度修正的星载全球导航卫星系统反射测量(GNSS-R)森林地上生物量反演方法。选择SMAP卫星的土壤湿度作为辅助数据，运用Tau-Omega模型对旋风卫星导航系统(CYGNSS)反射率做出改正，提高建模参数的准确性。将SMAP卫星提供的植被光学深度(VOD)和地上植被生物量(AGB)地图作为生物量参考数据，比较了改正前后观测值与参考数据的相关性变化。结果表明，改正后相关系数提升明显，改正后参数较反射率与VOD的相关系数从0.54提升到了0.67，与AGB的相关系数从0.46提升到了0.56。随后通过人工神经网络分别基于改正后的参数和反射率建立GNSS-R VOD和AGB反演模型。结果表明，所提方法能够有效提高VOD和AGB的反演精度，且在生物量水平较低的地区改进效果更优。对于VOD反演，改进后相关系数从0.70提升到了0.83，RMES从0.21降低到了0.17；对于AGB反演，改进后相关系数从0.61提升到了0.71，RMES从74 t/hm² 降低到了65 t/hm² 。
- CYGNSS /
- GNSS-R /
- 森林生物量 /
- SMAP卫星 /
- 土壤湿度 /
- 植被光学深度
Abstract:
Based on the Tau-Omega model, a spaceborne global navigation satellite system relectometry (GNSS-R) forest aboveground biomass inversion method considering the correction of ground soil moisture is proposed. The cyclone global navigation satellite system (CYGNSS) reflectance was corrected using the Tau-Omega model to increase the modeling parameters' accuracy, and SMAP satellite soil moisture was chosen as supplementary data. The biomass reference data utilized was the vegetation optical depth (VOD) supplied by SMAP satellite and the above-ground biomass (AGB) maps. The correlation changes between the observed values and the reference data prior to and following improvement were compared. The results show that the correlation coefficient increases significantly after the correction. The correlation coefficient between the parameters after improvement with VOD is increased from 0.54 to 0.67 compared to the reflectivity with VOD, and the correlation coefficient with AGB is increased from 0.46 to 0.56. Then, the GNSS-R VOD and AGB inversion models were established based on the corrected parameters and reflectivity through the artificial neural network, respectively. The results show that the improved method can effectively improve the inversion accuracy of VOD and AGB, and the improvement effect is better in areas with low biomass levels. For VOD inversion, after improvement, the correlation coefficient increased from 0.70 to 0.83, and the RMES decreased from 0.21 to 0.17; for AGB inversion, after improvement, the correlation coefficient increased from 0.61 to 0.71, and the RMES decreased from 74 t/hm² to 65 t/hm².
- CYGNSS /
- GNSS-R /
- forest biomass /
- SMAP satellite /
- soil moisture /
- vegetation optical depth

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图 1 $ \tau $与$ \cos (\theta ) $的关系

Figure 1. The relationship between $ \tau $ and $ \cos (\theta ) $

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图 2 SMAP植被光学深度8月至12月平均值

Figure 2. Average VOD of SMAP from August to December

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图 3 亚马逊地区AGB

Figure 3. AGB of the Amazon region

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图 4 CYGNSS反射率8月至12月平均值

Figure 4. Average reflectivity of CYGNSS from August to December

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图 5 实验区域

Figure 5. Experimental area

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图 6 人工神经网络示意

Figure 6. Schematic diagram of an artificial neural network

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图 7 人工神经网反演VOD结果

Figure 7. Inversion results of VOD by artificial neural network

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图 8 人工神经网反演AGB结果

Figure 8. Inversion results of AGB by artificial neural network

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表 1 不同植被类型单次散射反照率

Table 1. Single scattering albedo corresponding to different vegetation types

植被类型	$ {\omega ^{\rm{canopy}}} $
常绿针叶林、常绿阔叶林	0.07
落叶针叶林、落叶阔叶林	0.07
混交林	0.07
封闭的灌木地、开放的灌木地	0.05
多树草原	0.05
稀树草原	0.08
草原	0.05

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表 2 不同入射角下的相关系数

Table 2. Correlation coefficients at different incident angles

角度/（°）	反射率与VOD	反射率与AGB
[0～10]	−0.53	−0.46
[10～20]	−0.53	−0.46
[20～30]	−0.52	−0.45
[30～40]	−0.53	−0.47
[40～50]	−0.57	−0.49
[50～60]	−0.58	−0.49
[60～70]	−0.56	−0.46
[70～80]	−0.37	−0.28

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表 3 改进前后相关系数

Table 3. Correlation coefficients before and after improvement

参考数据	反射率	$ \tau $	$ {\tau _{{\mathrm{corr}}}} $
VOD	−0.54	0.51	0.67
AGB	−0.46	0.42	0.56

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表 4 相关系数和RMSE

Table 4. Correlation coefficient and RMSE

参考数据	相关系数		RMSE
参考数据	VOD	AGB/（t·hm⁻²）	VOD	AGB/（t·hm⁻²）
改进前	0.70	0.61	0.21	74
改进后	0.83	0.71	0.17	65

下载: 导出CSV

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