A New Algorithm for Measuring Vegetation Growth Using GNSS Interferometric Reflectometry
A New Algorithm for Measuring Vegetation Growth Using GNSS Interferometric Reflectometry
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使用 GNSS 干涉反射仪测量植被生长的新算法
DOI：
10.1109/jstars.2022.3230090
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发表时间：
2023-01-01
影响因子：
5.5
通讯作者：
Hong, Xuebao
中科院分区：
文献类型：
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作者：
Li, Jie;Yang, Dongkai;Hong, Xuebao
利用全球导航卫星系统干涉反射仪(GNSS-IR)测量植被生长状况已成为遥感领域中一项迅速发展的技术。地表反射的GNSS信号会影响植被生长状况(植被覆盖密度)测量的准确性，土壤湿度的影响程度也不同。本研究建立了一种可以减小SM和雪层对反射率影响的定标模型。利用基于Lomb-Scarger周期图的直射信号幅度比和GNSS-IR高度计计算植被反射率和雪层深度。利用全球导航卫星系统(GNSS)的板块边界观测数据验证了模型的有效性。结果表明，在校正了SM和雪层的影响后，反射率与植被生长状况有较好的相关性。而且，关联度提高了近0.14。本研究分析了积雪对植被生长状况的影响，发现当积雪深度大于30 cm时，积雪对植被生长状况的测量有明显的影响。在此基础上，提出了一种结合反射率和归一化微波反射指数(NMRI)来提高植被生长状况测量精度的融合方法。实验结果表明，与反射率和核磁共振成像的单站观测相比，该方法可以获得更好的效果，实测植被指数与现场归一化植被指数的最佳相关性达到0.91以上，均方根误差降至0.1893。
The use of global navigation satellite system interferometric reflectometry (GNSS-IR) to measure vegetation growth status has become a rapidly growing technique in remote sensing. GNSS signals reflected by the soil surface affect the accuracy of vegetation growth status (vegetation cover density) measurement, and the influence of soil moisture (SM) varies. This study establishes a calibration model that can reduce the influence of SM and snow layer on reflectivity. We used a direct-reflected signal amplitude ratio and GNSS-IR altimeter based on the Lomb–Scargle Periodogram to calculate the reflectivity of vegetation and snow layer depth. GNSS data from plate boundary observation were used to verify the validity of our model. The results show that reflectivity correlates better with vegetation growth status after calibrating the influence of the SM and snow layer. Moreover, the correlation increased by nearly 0.14. This study analyzed the influence of the snow layer and found that it had a noticeable effect on vegetation growth status measurement when the snow depth was over 30 cm. Furthermore, a fusion method is proposed to improve the accuracy of vegetation growth status measurement by combining the reflectivity and normalized microwave reflection index (NMRI). The experimental results show that better performance can be obtained compared to the single observation of the reflectivity and NMRI, and the best correlation between the measured and in situ normalized difference vegetation index is over 0.91, and the root mean square error decreases to 0.1893.