MODIS surface reflectance product (R_land) has been used to monitor waters due to its free availability and higher spatial resolution than MODIS ocean bands. However, its applicability in aquatic remote sensing has not been sufficiently assessed. Some fundamental questions such as the following need to be addressed: How does the R_land product perform in global inland and coastal waters? What water color parameters can be mapped using R_land? This study provided a comprehensive evaluation of the performance of MODIS R_land products against a field optical dataset containing 4143 reflectance spectra, 2320 chlorophyll-a (Chla) samples, and 1467 suspended particulate matter (SPM) samples across global nearshore coastal and inland waters. The results showed that R_land significantly overestimated remote sensing reflectance, particularly in the bands of 469 nm and 859 nm. The noticeable negative values and patchiness were found in the R_land imagery, and existing algorithms did not estimate satisfactory Chla and SPM from R_land across the global inland and coastal waters. Furthermore, we tested popular machine-learning approaches, such as random forest (RF), support vector machine, XGBoost, and deep neural networks, to examine the potential of the R_land product in estimating SPM and Chla. Machine learning models were found to outperform the state-of-the-art algorithms for SPM retrievals from R_land. Specifically, RF and XGBoost showed the good performance with mean absolute errors of ~25.0% and mean absolute percentage error of ~23% for a broad SPM range of 10–500 mg L⁻¹. Yet, machine learning models cannot retrieve reliable Chla from R_land with approximately 55% uncertainty due to the limited spectral information and uncertainty of R_land products. This implicated that R_land might be able to quantify the parameters that are closely related to SPM (e.g., water clarity and extinction coefficients) in most waters; however, it is challenging to quantify pigments like Chla in waters from R_land. We conclude that R_land might not be an optimal data source for monitoring inland and coastal waters, despite the ease of using this product and its higher spatial resolution than the MODIS ocean bands.

MODIS地表反射率产品 (R_land) 由于其免费可用性和比 MODIS 海洋波段更高的空间分辨率，已被用于监测水域。然而，其在水生遥感中的适用性尚未得到充分评估。需要解决以下一些基本问题： R_land 产品在全球内陆和沿海水域的表现如何？使用 R_land 可以映射哪些水彩参数？本研究针对包含 4143 个反射光谱、2320 个叶绿素-a (Chl a ) 样品和 1467 个悬浮颗粒物 (SPM) 样品的现场光学数据集对 MODIS R_land 产品的性能进行了全面评估，这些样品遍布全球近岸沿海和内陆水域。结果表明，R_land 显着高估了遥感反射率，特别是在 469 nm 和 859 nm 波段。在 R_land 图像中发现了明显的负值和不均匀性，现有算法无法估计来自 R_land 全球内陆和沿海水域的令人满意的 Chl a和 SPM。此外，我们测试了流行的机器学习方法，例如随机森林 (RF)、支持向量机、XGBoost 和深度神经网络，以检验 R_land 产品在估计 SPM 和 Chl a 方面的潜力. 发现机器学习模型优于用于从 R_land 检索 SPM 的最先进算法。^{具体而言，RF 和 XGBoost 在 10-500 mg L -1}的宽 SPM 范围内表现出良好的性能，平均绝对误差约为 25.0%，平均绝对百分比误差约为 23% 。然而，由于 R_land 产品的光谱信息有限和不确定性，机器学习模型无法以大约 55% 的不确定性从 R_land 检索可靠的 Chl a 。这意味着 R_land 可能能够量化大多数水域中与 SPM 密切相关的参数（例如，水的透明度和消光系数）；然而，量化像 Chl a这样的色素是一项挑战在来自 R_land 的水域中。我们得出结论，尽管 R_land 易于使用且其空间分辨率高于 MODIS 海洋波段，但 R_land 可能不是监测内陆和沿海水域的最佳数据源。