Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused deep models and time series reconstruction techniques. The proposed method first reconstructed cloud-contaminated pixels through time series filtering, during which the cloud masks initialized by a deep model were refined and updated during the reconstruction process. Then, the reconstructed time series images were fed into a spectral-indices-fused deep model trained on samples collected worldwide for classification. Finally, post-classification processing, including spatio-temporal majority filtering and time series refinement considering land–water interactions, was conducted to enhance the LULC mapping accuracy and consistency. We applied the proposed method to the cloud- and rain-prone Pearl River Delta (i.e., Guangdong–Hong Kong–Macao Greater Bay Area, GBA) and used time series Sentinel-2 images as the experimental data. The proposed method enabled seamless LULC mapping at a temporal frequency of 2–5 days, and the production of 10 m resolution annual LULC products in the GBA. The assessment yielded a mean overall accuracy of 87.01% for annual mapping in the four consecutive years of 2019–2022 and outperformed existing mainstream LULC products, including ESA WorldCover (83.98%), Esri Land Cover (85.26%), and Google Dynamic World (85.06%). Our assessment also reveals significant variations in LULC mapping accuracies with different cloud masks, thus underscoring their critical role in time series LULC mapping. The proposed method has the potential to generate seamless and near real-time maps for other regions in the world by using deep models trained on datasets collected globally. This method can provide high-quality LULC data sets at different time intervals for various land and water dynamics in cloud- and rain-prone regions. Notwithstanding the difficulties of obtaining high-quality LULC maps in cloud-prone areas, this paper provides a novel approach for the mapping of LULC dynamics and the provision of reliable annual LULC products.

中文翻译：

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学习光谱指数融合深度模型，用于云多发地区的时间序列土地利用和土地覆盖绘图：珠江三角洲的案例


利用光学卫星图像绘制土地利用和土地覆盖 (LULC) 的高度动态变化可能会受到各种多云条件的阻碍。这些条件导致高时间密度 LULC 映射的不连续性。在本文中，我们开发了一种集成时间序列映射方法，通过结合光谱指数融合深度模型和时间序列重建技术来提高多云区域的 LULC 映射精度和频率。该方法首先通过时间序列滤波重建云污染像素，在此期间，由深度模型初始化的云掩模在重建过程中被细化和更新。然后，将重建的时间序列图像输入到光谱指数融合的深度模型中，该模型对全球收集的分类样本进行训练。最后，进行分类后处理，包括考虑土地-水相互作用的时空多数过滤和时间序列细化，以提高土地利用和覆盖率制图的准确性和一致性。我们将所提出的方法应用于云雨多发的珠江三角洲（即粤港澳大湾区，GBA），并使用时间序列Sentinel-2图像作为实验数据。所提出的方法能够以 2-5 天的时间频率进行无缝 LULC 测绘，并在大湾区生成 10 m 分辨率的年度 LULC 产品。该评估得出的2019-2022年连续四年年度测绘的平均总体准确度为87.01%，优于现有主流LULC产品，包括ESA WorldCover（83.98%）、Esri Land Cover（85.26%）和Google Dynamic World（ 85.06%）。 我们的评估还揭示了不同云掩模的 LULC 制图精度存在显着差异，从而强调了它们在时间序列 LULC 制图中的关键作用。所提出的方法有可能通过使用在全球收集的数据集上训练的深度模型来为世界其他地区生成无缝且近乎实时的地图。该方法可以为多云和多雨地区的各种土地和水动态提供不同时间间隔的高质量 LULC 数据集。尽管在云多发地区获得高质量的 LULC 地图存在困难，但本文提供了一种绘制 LULC 动态图并提供可靠的年度 LULC 产品的新方法。