The accurate monitoring of long-term spatial and temporal changes in open-surface water bodies offers important guidance for water resource security and management. In the middle and lower reaches of the Yangtze River, the monitoring of water body changes is especially critical due to the dense population and drastic climate change. Due to the complexity of the physical environment in which the water bodies are located, the advantages and disadvantages of various water body detection rules can vary in large-scale areas. In this paper, we use Landsat 5/7/8 data to extract the area of water bodies in the study area and analyze their spatial and temporal trends from 1984 to 2020 using the Google Earth Engine (GEE) platform. We propose an improved water body extraction rule based on an existing multi-indicator water body algorithm that combines impervious surface data and digital elevation model data. In this study, the performance of the improved algorithm was cross-validated using seven other water body indicator algorithms, and the results showed the following: (1) the rule accurately retained information about the water body while minimizing the interference of shadows on the extracted water body. (2) On the annual scale from 1984 to 2020, the open-surface water body dataset extracted using this improved rule showed that the turning point for the area of each water body type was 2011, with an overall decreasing trend in area before 2011 and an increasing trend in area after 2011, with the exception of special years, such as 1998. (3) The driving mechanism analysis showed that, overall, precipitation was positively correlated with the water body area and temperature was negatively correlated with the water body area. Additionally, human activities can have an impact on surface water dynamics. The key influencing factors are diverse for each water body type; it was found that seasonal water bodies were correlated with precipitation and paddy fields and permanent water bodies were correlated with temperature and urban construction. The accurate monitoring of the spatial and temporal dynamics of open-surface water performed in this study can shed light on the sustainable development of water resources and the environment.

Lakes are an important component of global water resources. In order to achieve accurate lake extractions on a large scale, this study takes the Tibetan Plateau as the study area and proposes an Automated Lake Extraction Workflow (ALEW) based on the Google Earth Engine (GEE) and deep learning in response to the problems of a low lake identification accuracy and low efficiency in complex situations. It involves pre-processing massive images and creating a database of examples of lake extraction on the Tibetan Plateau. A lightweight convolutional neural network named LiteConvNet is constructed that makes it possible to obtain spatial–spectral features for accurate extractions while using less computational resources. We execute model training and online predictions using the Google Cloud platform, which leads to the rapid extraction of lakes over the whole Tibetan Plateau. We assess LiteConvNet, along with thresholding, traditional machine learning, and various open-source classification products, through both visual interpretation and quantitative analysis. The results demonstrate that the LiteConvNet model may greatly enhance the precision of lake extraction in intricate settings, achieving an overall accuracy of 97.44%. The method presented in this paper demonstrates promising capabilities in extracting lake information on a large scale, offering practical benefits for the remote sensing monitoring and management of water resources in cloudy and climate-differentiated regions.

The kinetics of isothermal bainite formation at 483 K, 503 K and 533 K in a bearing SAE 52100 steel (1,01% C – 1,36% Cr – 0,32% Mn – 0,25% Si (wt.%)) is investigated theoretically and experimentally. It is found that the bainite formation becomes significantly slower with decreasing temperature. To interpret the results, a physical model with a concept of reconstructive approach proposed in the literature is applied. The model gives a reasonable agreement with experimental results, although a certain deviation exists at the late stage of the transformation. This deviation is attributed to the diffusion coefficient used in the model without considering the effect of alloying elements, mainly chromium.