Deep learning in environmental remote sensing: Achievements and challenges

• Published in: Remote sensing of environment Vol. 241; p. 111716
• Main Authors: Yuan, Qiangqiang, Shen, Huanfeng, Li, Tongwen, Li, Zhiwei, Li, Shuwen, Jiang, Yun, Xu, Hongzhang, Tan, Weiwei, Yang, Qianqian, Wang, Jiwen, Gao, Jianhao, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.05.2020; Elsevier BV
• Subjects: air; Air temperature; artificial intelligence; Atmospheric models; color; Data integration; Deep learning; Earth; environmental factors; Environmental monitoring; Environmental remote sensing; Evapotranspiration; Hydrology; Land cover; Land surface temperature; Learning algorithms; Machine learning; Mapping; Multisensor fusion; Neural network; Neural networks; Ocean color; Parameter retrieval; prediction; Remote sensing; Solar radiation; Surface temperature; vegetation; Deep learning; Environmental remote sensing; Neural network; Parameter retrieval
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2020.111716

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. •The potential of deep learning (DL) in environmental remote sensing is analyzed.•Typical DL network architectures in remote sensing applications are introduced.•Progress on DL in remote sensing of ten more environmental parameters is reviewed.•New insights on combining DL and physical/geographical laws are discussed.