This year officially marks the beginning of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030)—the Ocean Decade. A primary objective of this coordination framework is to support scientific research and technological developments that can contribute to the conservation and sustainable management of the world’s oceans. One of the seven Decade Outcomes is to secure healthy and resilient oceans where marine biodiversity is mapped and protected; however, fulfilling this goal will require data, knowledge, and technology. The use of remote sensing is now established in marine research and management and is crucial in developing our understanding of ocean patterns and processes at multiple spatial and temporal scales (e.g., Jawak et al., 2015). As such, remote sensing technology is expected to play a critical role in achieving the vision set by the Ocean Decade.

In the last 20 years, technological developments in remote sensing have boosted our ability to monitor the distribution and status of previously understudied ecosystems, from tidal flats and mangroves (Goldberg et al., 2020; Murray et al., 2019) to continental shelves (Pygas et al., 2020) and the deep sea (Lim et al., 2021). These developments have also enabled the mapping of marine physical and biogenic habitats and ecosystems at spatial resolutions never achieved before. For example, Lyons et al. (2020) recently demonstrated how coral reef habitats ranging from individual reefs (~200 km²) to entire barrier reef systems (200 000 km²) could be mapped across vast ocean extents (>6 000 000 km²) using global multiscale earth observations, generating high-resolution maps that can be used to support ecosystem risk assessments and to inform management. Deeper seafloor habitats can now be mapped and imaged at a centimeter scale using autonomous underwater vehicles and sensors like synthetic aperture sonars (e.g., Thorsnes et al., 2019). Maps produced by such efforts are invaluable communication tools; they have become key for data integration and synthesis to inform decision-making in a variety of contexts (Guisan et al., 2013; Harris & Baker, 2020). These mapping exercises can also be used to predict the distribution of species, communities, or ecosystems based on their associations with the physical and chemical characteristics of the environment and can support seascape ecology studies that relate spatial patterns with ecological processes (Pittman, 2018).

Passive sensors mounted on unoccupied aerial vehicles (UAVs) and satellites are commonly used to map and monitor characteristics and components of the marine environment, such as sea surface temperature, salinity, marine mammal distribution, primary productivity, and harmful algal blooms (Pettorelli, 2019). Satellite radar altimeters have also long been used to study the oceans and derive coarse-scale digital bathymetric models (e.g., Dixon et al., 1983). The information compiled by different sensors can then be integrated to delineate broad marine biogeographic units such as ecoregions (e.g., Sayre et al., 2017; Spalding et al., 2007). At finer scales, UAV-mounted lidar sensors have enabled increased aboveground biomass monitoring in coastal systems such as mangroves (e.g., Qiu et al., 2019), while bathymetric lidar systems have boosted data collection efforts in submerged coastal areas, where it is often too dangerous and resource intensive to collect acoustic data and challenging for radar altimeters to differentiate land from water (Sandwell et al., 2002).

While active underwater cameras mounted on remotely operated vehicles or towed or dropped platforms have been extensively used to collect species and seafloor data and create photomosaics of the seafloor (e.g., Jones, 2009; Sward et al., 2021), optical remote sensing is usually limited to shallow and optically clear waters. This means that, in most situations, acoustic remote sensing represents the most effective source of data for ecologists interested in marine biodiversity. Acoustic remote sensing can be passive (i.e., using hydrophones to capture sounds in the environment) or active (i.e., using sonars that produce directional sound and listen for returns); both have their place in support of marine ecology and conservation. For example, multibeam echosounders enable the production of high-resolution digital bathymetric models, from which different terrain attributes (e.g., slope, rugosity) known to be direct or indirect surrogates of species distributions can be derived (Lecours et al., 2015, 2016; McArthur et al., 2010). Multibeam backscatter data and sidescan sonar imagery can also provide information about the distribution of sediment and seafloor habitat characteristics important to many species. Most often used in fisheries, singlebeam echosounders can provide critical information about what lives in the water column, while passive acoustic remote sensing can contribute species occurrence and distribution data and inform abundance and behavioral research (Stowell & Sueur, 2020).

There is no doubt that the UN Ocean Decade will provide exciting opportunities for the field of remote sensing and its applications to marine and coastal environments. Active acoustic remote sensing technologies have historically been associated with military uses and the field of hydrography rather than with the remote sensing community of practice; this has slowed the integration of data processing and analysis methods that have proven effective in the study of terrestrial environments. This gap offers new research opportunities that remain unexplored in marine environments. For example, because raw multibeam echosounder data are displayed as point clouds that share many characteristics with lidar point clouds, acoustic data processing workflows might benefit from algorithms developed for processing lidar data. The opposite is also true; the commonly used CUBE (Combined Uncertainty and Bathymetry Estimator) algorithm for the generation of digital bathymetric models and the combined storage of bathymetry and uncertainty layers within a single BAG (Bathymetric Attributed Grid) file format may benefit other types of remotely sensed data like lidar-derived digital surface and terrain models. Data fusion techniques offer opportunities for the production of seamless digital surface models spanning the terrestrial and marine environments that combine both optical and acoustic remotely sensed data (e.g., Linklater et al., 2018). New developments in image processing tools, analytical methods like object-based image analysis, and artificial intelligence have the potential to enhance marine ecology and seascape ecology research (Pittman et al., 2021). New ways to study the marine environment, such as multibeam water column data (e.g., Schimel et al., 2020), multispectral acoustic systems (e.g., Brown et al., 2019), and satellite-derived bathymetry (Ashphaq et al., 2021), highlight the need for more research into how remote sensing can contribute to the understanding and conservation of the world’s oceans.

The issues targeted by the Ocean Decade, such as climate change and unsustainable exploitation of marine resources, are global and, as such, will require collaborative efforts and data from around the world. However, both ocean science and remote sensing capacities are unevenly distributed. Mapping marine ecosystems and biodiversity in places or through organizations that cannot count on well-funded initiatives must rely on existing, publicly available datasets such as the GEBCO (General Bathymetric Chart of the Oceans) global bathymetric dataset, archived satellite imagery, or marine biodiversity datasets like those compiled on OBIS (Ocean Biodiversity Information System). This highlights the need for open-source multidisciplinary data in both remote sensing and the marine sciences that can be spatially integrated accurately; it also highlights the need for a common platform where information gathered by these communities can be shared and scientific agendas synchronized. Since its inception, the editorial board of Remote Sensing in Ecology and Conservation has welcomed contributions to coastal and marine ecology and conservation that rely on remote sensing (Pettorelli et al., 2015). In 2017, the editorial board made it a goal to increase their engagement with communities working in marine systems and acoustic remote sensing (Pettorelli et al., 2017). The number of published “original research” articles on coastal or marine environments has steadily increased every year since 2016, reaching 21% of all contributions in 2020 (Table 1). However, the use of active acoustic remote sensing is still underrepresented, with only one article published since the launch of our journal. With efforts like the Seabed 2030 Project, which aims to map the world’s seafloor by 2030 and relies heavily on acoustic remote sensing technologies (Mayer et al., 2018), we expect the availability of seafloor data to increase and, with them, the opportunities to better understand the ecology of our seas and oceans. We thus want to reiterate our commitment to marine remote sensing developments and applications and hope that the increased opportunities will be reflected in the submissions to come.

今年正式标志着联合国海洋科学促进可持续发展十年（2021-2030）——海洋十年的开始。该协调框架的主要目标是支持有助于世界海洋保护和可持续管理的科学研究和技术发展。七个十年成果之一是确保海洋生物多样性得到绘制和保护的健康和有弹性的海洋；然而，实现这一目标需要数据、知识和技术。遥感的使用现已在海洋研究和管理中得到确立，对于发展我们在多个空间和时间尺度上对海洋模式和过程的理解至关重要（例如，Jawak 等，2015）。因此，遥感技术有望在实现海洋十年设定的愿景方面发挥关键作用。

在过去的 20 年里，遥感技术的发展提高了我们监测以前未充分研究的生态系统分布和状态的能力，从潮滩和红树林（Goldberg 等人，2020 年；Murray 等人，2019 年）到大陆架（ Pygas 等人，2020 年）和深海（Lim 等人，2021 年）。这些发展还使得能够以前所未有的空间分辨率绘制海洋物理和生物栖息地和生态系统的地图。例如，里昂等人。( 2020 ) 最近展示了从单个珊瑚礁 (~200 km ² ) 到整个堡礁系统 (200 000 km ^{2 ) 的珊瑚礁栖息地}) 可以使用全球多尺度地球观测在广阔的海洋范围（>6 000 000 km ²）上绘制地图，生成可用于支持生态系统风险评估和为管理提供信息的高分辨率地图。现在可以使用自主水下航行器和合成孔径声纳等传感器在厘米尺度上绘制和成像更深的海底栖息地（例如，Thorsnes 等人，2019 年）。通过这些努力制作的地图是非常宝贵的交流工具；它们已成为数据集成和综合的关键，为各种情况下的决策提供信息（Guisan 等人，2013 年；Harris & Baker，2020 年））。这些绘图练习还可用于根据物种、群落或生态系统与环境物理和化学特征的关联来预测它们的分布，并可以支持将空间模式与生态过程联系起来的海景生态学研究（Pittman，2018 年）。

安装在无人飞行器 (UAV) 和卫星上的无源传感器通常用于绘制和监测海洋环境的特征和组成部分，例如海面温度、盐度、海洋哺乳动物分布、初级生产力和有害藻华（Pettorelli，2019）。长期以来，卫星雷达高度计也被用于研究海洋并得出粗略的数字测深模型（例如，Dixon 等人，1983 年）。然后可以整合由不同传感器编译的信息以描绘广泛的海洋生物地理单元，例如生态区（例如，Sayre 等人，2017 年；Spalding 等人，2007 年））。在更精细的尺度上，安装在无人机上的激光雷达传感器能够增加红树林等沿海系统的地上生物量监测（例如 Qiu 等人，2019 年），而测深激光雷达系统促进了水下沿海地区的数据收集工作收集声学数据过于危险和资源密集，而且雷达高度计难以区分陆地和水域（Sandwell 等人，2002 年）。

虽然安装在遥控车辆或拖曳或掉落平台上的主动式水下相机已被广泛用于收集物种和海底数据并创建海底照片镶嵌（例如，Jones，2009；Sward 等人，2021），光学遥感通常仅限于浅水和光学清澈的水域。这意味着，在大多数情况下，声学遥感是对海洋生物多样性感兴趣的生态学家最有效的数据来源。声学遥感可以是被动的（即使用水听器捕捉环境中的声音）或主动的（即使用产生定向声音并监听返回的声纳）；两者都在支持海洋生态和保护方面占有一席之地。例如，多波束回声测深仪能够生成高分辨率数字测深模型，从中可以得出已知是物种分布的直接或间接替代的不同地形属性（例如坡度、粗糙度）（Lecours 等人，2015 年，2016 年） ; 麦克阿瑟等人，2010 年）。多波束反向散射数据和侧扫声纳图像还可以提供有关沉积物分布和对许多物种很重要的海底栖息地特征的信息。最常用于渔业的单波束回声测深仪可以提供有关水体中生物的重要信息，而被动声学遥感可以提供物种发生和分布数据，并为丰度和行为研究提供信息（Stowell 和 Sueur，2020 年）。

毫无疑问，联合国海洋十年将为遥感领域及其在海洋和沿海环境中的应用提供激动人心的机遇。主动声学遥感技术历来与军事用途和水文学领域相关联，而不是与遥感实践社区相关联；这减缓了数据处理和分析方法的整合，这些方法已被证明在陆地环境研究中有效。这一差距提供了在海洋环境中尚未开发的新研究机会。例如，由于原始多波束回声测深仪数据显示为与激光雷达点云共享许多特征的点云，因此声学数据处理工作流程可能会受益于为处理激光雷达数据而开发的算法。反之亦然；用于生成数字测深模型的常用 CUBE（组合不确定性和测深估计器）算法以及在单个 BAG（测深属性网格）文件格式中组合存储测深和不确定性层可能有利于其他类型的遥感数据，如激光雷达-衍生的数字表面和地形模型。数据融合技术为生成跨越陆地和海洋环境的无缝数字表面模型提供了机会，这些模型结合了光学和声学遥感数据（例如，Linklater 等人，2018 年）。图像处理工具、基于对象的图像分析等分析方法和人工智能的新发展具有增强海洋生态学和海景生态学研究的潜力（Pittman 等人，2021 年）。研究海洋环境的新方法，例如多波束水柱数据（例如，Schimel 等人，2020 年）、多光谱声学系统（例如，Brown 等人，2019 年）和卫星衍生的水深测量（Ashphaq 等人，2021 年），强调需要对遥感如何有助于理解和保护世界海洋进行更多研究。

海洋十年所针对的问题，例如气候变化和对海洋资源的不可持续开发，是全球性的，因此需要来自世界各地的协作努力和数据。然而，海洋科学和遥感能力分布不均。在无法依靠资金充足的计划的地方或通过组织绘制海洋生态系统和生物多样性地图，必须依赖现有的、公开可用的数据集，例如 GEBCO（海洋总测深图）全球测深数据集、存档卫星图像或海洋生物多样性数据集就像那些在 OBIS（海洋生物多样性信息系统）上编译的。这凸显了遥感和海洋科学领域对可以在空间上准确整合的开源多学科数据的需求；它还强调需要一个共同平台，让这些社区收集的信息可以共享，科学议程可以同步。自创刊以来，编委生态和保护中的遥感对依赖遥感的沿海和海洋生态和保护做出了贡献（Pettorelli 等人，2015 年）。2017 年，编辑委员会的目标是增加他们与从事海洋系统和声学遥感工作的社区的接触（Pettorelli 等，2017）。自 2016 年以来，发表的关于沿海或海洋环境的“原创研究”文章的数量每年都在稳步增加，到 2020 年达到所有贡献的 21%（表 1）。然而，主动声学遥感的使用仍然不足，自我们的期刊创刊以来仅发表了一篇文章。通过诸如 Seabed 2030 项目等努力，该项目旨在到 2030 年绘制世界海底地图，并严重依赖声学遥感技术（Mayer 等，2018)，我们预计海底数据的可用性会增加，并有机会更好地了解我们的海洋生态。因此，我们希望重申我们对海洋遥感发展和应用的承诺，并希望增加的机会将反映在即将提交的材料中。