Abstract Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environmental monitoring. Over the last decade, a typical and state-of-the-art ML framework named deep learning (DL), which is developed from the traditional neural network (NN), has outperformed traditional models with considerable improvement in performance. Substantial progress in developing a DL methodology for a variety of earth science applications has been observed. Therefore, this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process. First, the potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed. A typical network structure will then be introduced. Afterward, the applications of DL environmental monitoring in the atmosphere, vegetation, hydrology, air and land surface temperature, evapotranspiration, solar radiation, and ocean color are specifically reviewed. Finally, challenges and future perspectives will be comprehensively analyzed and discussed.

中文翻译：

---

环境遥感深度学习：成就与挑战

摘要 各种形式的机器学习 (ML) 方法历来在环境遥感研究中发挥了重要作用。随着来自地球观测的“大数据”越来越多以及机器学习的快速发展，出现了越来越多的新方法来帮助地球环境监测。在过去的十年中，从传统神经网络 (NN) 发展而来的典型的、最先进的机器学习框架称为深度学习 (DL)，在性能上取得了相当大的提升，其性能优于传统模型。已经观察到在为各种地球科学应用开发深度学习方法方面取得了重大进展。因此，本次审查将集中于使用传统的 NN 和 DL 方法来推进环境遥感过程。第一的，将分析深度学习在环境遥感中的潜力，包括土地覆盖制图、环境参数检索、数据融合和降尺度以及信息重建和预测。然后将介绍一个典型的网络结构。随后，具体综述了DL环境监测在大气、植被、水文、空气和地表温度、蒸散、太阳辐射、海洋颜色等方面的应用。最后，将全面分析和讨论挑战和未来前景。具体综述了DL环境监测在大气、植被、水文、空气和地表温度、蒸散、太阳辐射、海洋颜色等方面的应用。最后，将全面分析和讨论挑战和未来前景。具体综述了DL环境监测在大气、植被、水文、空气和地表温度、蒸散、太阳辐射、海洋颜色等方面的应用。最后，将全面分析和讨论挑战和未来前景。