As a marine ecological disaster caused by the explosive proliferation of green macroalgae, green tides impair economic development and the ecological environment, affecting dozens of regions worldwide. The largest green tide in the world occurs in the Yellow Sea, with Ulva prolifera ( U. prolifera ) the dominant species. Satellite remote sensing technology, with its advantages of a large scale, a long time series, and traceability, plays a significant role in U. prolifera monitoring, providing important support for obtaining deeper scientific understanding and promoting disaster prevention and control. To systematically and comprehensively summarize research progress and identify weaknesses and priorities for future development, this article reviews over 350 articles on U. prolifera green tide remote sensing in the Yellow Sea, published before ­November 2023 from three aspects: remote sensing ­mechanisms (electromagnetic scattering and remote sensing image features), methods (detection, coverage area retrieval, species discrimination, biomass estimation, drift velocity determination, and so on), and applications (growth and decay, interannual variabilities, and so forth). Additionally, challenges, opportunities, and development priorities are analyzed (see “Article Contents”). The findings in this article promote the future development of U. prolifera remote sensing technology to assist with disaster prevention and ecosystem protection.

<!-- *** Custom HTML *** --> Large intraclass variance and low interclass variance are among the most challenging problems in very high-resolution (VHR) image classification. Semantic segmentation constructed in a deep convolution neural network is used as a classification algorithm conducted via end-to-end training, which combines spectral–spatial features and context information. However, large-scale remote sensing images cannot be directly processed because they are limited by GPU memory and segmentation algorithm. At the same time, classification using single band combinations is also unsatisfactory due to the extraordinary complex features of VHR images. Therefore, a method is proposed based on multiple band combinations and patchwise scene analysis. A complex remote sensing image can be considered as into a combination of simple scenes from multiple patchwise images. And optimal band combinations of each patchwise image are selected according to their scene. The segmentation results of each patchwise image are merged to get the desired results according to geographical coordinates. Our method is validated on the ISPRS 2-D Semantic Labeling dataset of Potsdam, on which results competitive with the state-of-the-art are obtained. The proposed scheme has strong universality and can be used for large-scale high-resolution remote sensing image classification.

Timely monitoring of marine aquaculture has considerable significance for marine ecological protection and maritime safety and security. Considering that supervised learning needs to rely on a large number of training samples and the characteristics of intensive and regular distribution of the laver aquaculture zone, in this paper, an inaccurate supervised classification model based on fully convolutional neural network and conditional random filed (FCN-CRF) is designed for the study of a laver aquaculture zone in Lianyungang, Jiangsu Province. The proposed model can extract the aquaculture zone and calculate the area and quantity of laver aquaculture net simultaneously. The FCN is used to extract the laver aquaculture zone by roughly making the training label. Then, the CRF is used to extract the isolated laver aquaculture net with high precision. The results show that the $kappa$ coefficient of the proposed model is 0.984, the $F_1$ is 0.99, and the recognition effect is outstanding. For label production, the fault tolerance rate is high and does not affect the final classification accuracy, thereby saving more label production time. The findings provide a data basis for future aquaculture yield estimation and offshore resource planning as well as technical support for marine ecological supervision and marine traffic management.

Water is indispensable for sustainable socioeconomic development, especially in China’s sandy regions. Despite existing studies in sandy regions, the drivers of changes in total water storage and their potential impacts remain inadequately understood. Here we found a 55.97 billion m³ net loss in total water storage from 2002 to 2023 by integrating multiple datasets. Water loss was primarily attributed to increased farmland irrigation (49.56 billion m³) and ecological restoration (37.04 billion m³). In the coming 40 years, water supply capacity will decrease by up to 6.54% and 19.07% under low and high emission scenarios, respectively, requiring reductions in human water consumption of 60% to 135%. This depletion potentially threatens both regional sustainability and national ecological security. This study calls for urgent scientific regulations on water resources, including strict control of local consumption and enhancement of supply capacity.

The classification of remote sensing images with high spatial resolution requires considerable training samples, but the process of sample making is slow and laborious. How to guarantee the accuracy of supervised classification under the condition of limited samples is an urgent problem to be solved in the field of supervised classification. For addressing this problem, we proposes an improved conditional generative adversarial network with edge feature (ECGAN) to augment limited training data for the classification of remote sensing images with high spatial resolution in this paper. On the basis of conditional generative adversarial network, feature factors of interclass boundaries and intraclass edges are added to networks, and an objective function with multiscale and multilevel features is constructed. The ISPRS Potsdam and Vaihingen remote sensing datasets are regarded as examples. Results indicate that the high-resolution remote sensing images generated by using the network proposed in this paper have abundant texture, accurate edges, and are highly similar to real images. The generated images are used to augment training samples, and an experiment for classifying high-resolution remote sensing images is conducted. The classification results of the proposed augmentation method perform better than that of the traditional sample augmentation method. We prove that ECGAN as a means of sample augmentation can effectively solves the problem that the classification effect is unideal when the supervised classification sample is insufficient.

Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance.