College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China; The Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya, China.
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China; The Key Laboratory of Computational Geodynamics, Chinese Academy of Sciences, Beijing, China.
Sci Total Environ. 2022 Oct 15;843:156981. doi: 10.1016/j.scitotenv.2022.156981. Epub 2022 Jun 25.
Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. This study proposes the application of a spatial filling method using the machine learning-based Extreme Gradient Boosting (BST) to reconstruct missing pixels of daily MODIS Chl-a data from 2007 to 2018. The approach is applied to different trophic biogeographical subregions of the Northwestern Pacific where it has complex phytoplankton dynamics and frequent data missing. Various environmental variables are taken into consideration, including meteorological forcing, geographic and topographic features, and oceanic physical components. The BST-reconstructed Chl-a (BST Chl-a) is validated using in-situ Chl-a measurements, VIIRS and Himawari-8 Chl-a products. The results show that the BST model is highly adaptive in reconstructing Chl-a data, and it performs well in pelagic, offshore and coastal with the best performance in pelagic. BST Chl-a improves coverage without significant quality degradation compared to the original MODIS Chl-a. BST Chl-a agrees better with in-situ data than that of MODIS, with CC of 0.742, RMSE of 0.247, MAE of 0.202 and Bias of 0.089. Cross-satellite validation using VIIRS and Himawari-8 Chl-a also shows promising results with the CC of 0.861 and 0.765, respectively, suggesting the high accuracy of BST Chl-a. The inter-annual trend of BST Chl-a decreases in coastal and increases in offshore and pelagic. BST Chl-a images present similar spatial patterns to MODIS Chl-a under different missing rates, with gradual decreases from coastal to pelagic. It indicates that phytoplankton bloom patterns can be identified by daily BST Chl-a images.
海表叶绿素 a 浓度（Chl-a）是浮游植物生物量的关键指标。时空连续的 Chl-a 数据对于了解叶绿素发生和发展的机制以及跟踪浮游植物变化非常重要。然而，利用每日 Chl-a 数据的最大挑战是由于轨道位置和云层覆盖而导致大量像素缺失。本研究提出了一种基于机器学习的极端梯度提升（BST）的空间填充方法，用于重建 2007 年至 2018 年期间每日 MODIS Chl-a 数据的缺失像素。该方法应用于西北太平洋不同营养生物地理亚区，该地区浮游植物动态复杂，数据经常缺失。考虑了各种环境变量，包括气象强迫、地理和地形特征以及海洋物理成分。使用现场 Chl-a 测量值、VIIRS 和 Himawari-8 Chl-a 产品验证了 BST 重建的 Chl-a（BST Chl-a）。结果表明，BST 模型在重建 Chl-a 数据方面具有高度适应性，在远洋、近海和沿海地区表现良好，在远洋地区表现最佳。与原始 MODIS Chl-a 相比，BST Chl-a 提高了覆盖范围，而不会显著降低质量。BST Chl-a 与现场数据的一致性优于 MODIS，CC 为 0.742，RMSE 为 0.247，MAE 为 0.202，偏差为 0.089。使用 VIIRS 和 Himawari-8 Chl-a 的交叉卫星验证也分别得到了 0.861 和 0.765 的有希望的结果，表明 BST Chl-a 的精度很高。BST Chl-a 的年际趋势在沿海地区减少，在近海和远洋地区增加。在不同的缺失率下，BST Chl-a 图像呈现出与 MODIS Chl-a 相似的空间模式，从沿海到远洋逐渐减少。这表明可以通过每日 BST Chl-a 图像识别浮游植物爆发模式。
