The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous water bodies, such as Taihu Lake. Sensors operating in the microwave band can effectively mitigate this issue, particularly the emerging Global Navigation Satellite System Reflectometry (GNSS-R), which offers high temporal resolution and cost-effectiveness. In this paper, we propose a new method to recover lake-surface NDVI on cloudy days, utilizing GNSS-R observables and auxiliary meteorological data in conjunction with a machine learning regression algorithm called Bagging Tree. We also examine the effective range of GNSS-R data within this application scenario. Meanwhile, the Weighted Linear Regression-Laplacian Prior Regulation Method (WLR-LPRM) image gap-filling algorithm is used as a benchmark to evaluate recovery accuracy. The regression coefficient of NDVI retrieved using the proposed method is 0.95, with a root mean square error (RMSE) of 0.021 and a mean absolute error (MAE) of 0.010. Compared to the previous work on GNSS-R algal bloom detection with overall accuracy of 0.82, this work shows significant improvement in both accuracy and utility. The recovery of lake surface NDVI provides detailed insights into algal blooms, including quantifiable metrics such as the amount and spatial distribution, which are crucial for effective monitoring and management. Additionally, the recovered image textures exhibit high clarity and closely resemble the reference NDVI images. Experimental evaluation using simulated and actual cloud blocks indicates the model’s robustness to recover NDVI under varying cloud cover conditions. In summary, this study demonstrates the capability of GNSS-R aided by supplementary data for recovering missing NDVI values on lake surfaces when optical observations are absent for the first time.

中文翻译：

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恢复湖面的 NDVI：通过 ERA-5 输入增强 CYGNSS 数据的初步见解


近几十年来，许多湖泊不断升级的水污染导致藻华灾难更加频繁地发生。这些灾害的严重性可以通过遥感技术进行评估，特别是使用归一化差值植被指数 （NDVI） 进行测量。然而，在水体众多的地区（如太湖），使用光学传感器的 NDVI 观测经常受到云和雾的影响。在微波频段工作的传感器可以有效缓解这个问题，特别是新兴的全球导航卫星系统反射计 （GNSS-R），它提供高时间分辨率和成本效益。在本文中，我们提出了一种在阴天恢复湖面 NDVI 的新方法，利用 GNSS-R 可观测数据和辅助气象数据，并结合称为 Bagging Tree 的机器学习回归算法。我们还研究了 GNSS-R 数据在此应用场景中的有效范围。同时，采用加权线性回归-拉普拉斯先验调控法 （WLR-LPRM） 图像间隙填充算法作为评估恢复精度的基准。使用所提出的方法检索的 NDVI 回归系数为 0.95，均方根误差 （RMSE） 为 0.021，平均绝对误差 （MAE） 为 0.010。与之前总体准确率为 0.82 的 GNSS-R 藻华检测工作相比，这项工作在准确性和实用性方面都有了显着提高。湖面 NDVI 的恢复提供了对藻华的详细见解，包括数量和空间分布等可量化指标，这些指标对于有效监测和管理至关重要。此外，恢复的图像纹理表现出高清晰度，与参考 NDVI 图像非常相似。 使用模拟和实际云块的实验评估表明，该模型在不同云覆盖条件下恢复 NDVI 的鲁棒性。总之，本研究证明了 GNSS-R 在补充数据的帮助下，在首次没有光学观测时恢复湖面上缺失的 NDVI 值的能力。