Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks

PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine

PlanetScope (PL) high-resolution composite base maps have recently become available within Google Earth Engine (GEE) for the tropical regions thanks to the partnership between Google and the Norway’s International Climate and Forest Initiative (NICFI). Object-based (OB) image classification in the GEE environment has increased rapidly due to the broadly recognized advantages of applying these approaches to medium- and high-resolution images. This work aimed to assess the advantages for land cover classification of (a) adopting an OB approach with PL data; and (b) integrating the PL datasets with Sentinel 2 and Sentinel 1 data both in Pixel-based (PB) or OB approaches. For this purpose, in this research, we compared ten LULC classification approaches (PB and OB, all based on the Random Forest (RF) algorithm), where the three satellite datasets were used according to different levels of integration and combination. The study area, which is 69,272 km2 wide and located in central Brazil, was selected within the tropical region, considering a preliminary availability of sample points and its complex landscape mosaic composed of heterogeneous agri-natural spaces, including scattered settlements. Using only the PL dataset with a typical RF PB approach produced the worse overall accuracy (OA) results (67%), whereas adopting an OB approach for the same dataset yielded very good OA (82%). The integration of PL data with the S2 and S1 datasets improved both PB and OB overall accuracy outputs (82 vs. 67% and 91 vs. 82%, respectively). Moreover, this research demonstrated the OB approaches’ applicability in GEE, even in vast study areas and using high-resolution imagery. Although additional applications are necessary, the proposed methodology appears to be very promising for properly exploiting the potential of PL data in GEE.

Velocity Estimation of Ocean Surface Currents in along-Track InSAR System Based on Conditional Generative Adversarial Networks

Velocity estimation of ocean surface currents is of great significance in the fields of the fishery, shipping, sewage discharge, and military affairs. Over the last decade, along-track interferometric synthetic aperture radar (along-track InSAR) has been demonstrated to be one of the important instruments for large-area and high-resolution ocean surface current velocity estimation. The calculation method of the traditional ocean surface current velocity, as influenced by the large-scale wave orbital velocity and the Bragg wave phase velocity, cannot easily separate the current velocity, characterized by large error and low efficiency. In this paper, a novel velocity estimation method of ocean surface currents is proposed based on Conditional Generative Adversarial Networks (CGANs). The main processing steps are as follows: firstly, the known ocean surface current field diagrams and their corresponding interferometric phase diagrams are constructed as the training dataset; secondly, the estimation model of the ocean surface current field is constructed based on the pix2pix algorithm and trained by the training dataset; finally, the interferometric phase diagrams in the test dataset are input into the trained model. In the simulation experiment, processing results of the proposed method are compared with those of traditional ocean surface current velocity estimation methods, which demonstrate the efficiency and effectiveness of the novel method.

Multitemporal Water Extraction of Dongting Lake and Poyang Lake Based on an Automatic Water Extraction and Dynamic Monitoring Framework

Timely and accurate large-scale water body mapping and dynamic monitoring are of great significance for water resource planning, flood control, and disaster reduction applications. Synthetic aperture radar (SAR) systems have the characteristics of strong operability, wide coverage, and all-weather data availability, and play a key role in large-scale water monitoring applications. However, there are still some challenges in the application of highly efficient, high-precision water extraction and dynamic monitoring methods. In this paper, a framework for the automatic extraction and long-term change monitoring of water bodies is proposed. First, a multitemporal water sample dataset is produced based on the bimodal threshold segmentation method. Second, attention block and pyramid module are introduced into the UNet (encoder-decoder) model to construct a robust water extraction network (PA-UNet). Then, GIS modeling is used for the automatic postprocessing of the water extraction results. Finally, the results are mapped and statistically analyzed. The whole process realizes end-to-end input and output. Sentinel-1 data covering Dongting Lake and Poyang Lake are selected for water extraction and dynamic monitoring analysis from 2017 to 2020, and Sentinel-2 images from a similar time frame are selected for verification. The results show that the proposed framework can realize high-precision (the extraction accuracy is higher than 95%), highly efficient automatic water extraction. Multitemporal monitoring results show that Dongting Lake and Poyang Lake fluctuate most in April, July, and November in 2017, 2019, and 2020, and the change trends of the two lakes are the same.