According to historical information, more than 300 metal smelting enterprises have been in the southwest of Xiongan for 300 years; however, these polluting enterprises have been gradually closed with the increased intensity of environmental protection. In the paper, 264 soil samples were collected and analyzed in the range of 400 nm–2500 nm by the spectra vista corporation (SVC), and the spectral noise was smoothed by the Savitzky–Golay filter. In order to enhance the spectral differences and curve shapes, mathematical transformations, such as the standard normal variate (SNV), first-order differential (FD), second-order differential (SD), multiple scattering correction (MSC), and continuum removal (CR), were performed on the data, and the correlation between spectral transformation and contents of REEs was analyzed. Moreover, three machine learning models—partial least-squares (PLS), random forest (RF), back propagation neural network (BPNN)—were used to predict the contents of REEs. Experimental results prove that REEs are combined with spectral active substances, such as organic compounds, clay minerals, and iron oxide, and it is possible to determine the contents of REEs using the reflection spectrum. The R² between the predicted values and measured contents reached 0.986 by using BPNN after FD transformation. More importantly, the predicted values basically agree with the actual situation for CASI/SASI airborne hyperspectral images, and this is an effective technique to obtain the contents of REEs in soil at the study area.

Water is an important factor in human survival and development. With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly prominent. It not only affects the living environment of residents in the city, but also threatens their diet and water quality. Therefore, the accurate monitoring and management of urban black and odorous water bodies is particularly important. At present, when researching water quality issues, the methods of fixed-point sampling and laboratory analysis are relatively mature, but the time and labor costs are relatively high. However, empirical models using spectral characteristics and different water quality parameters often lack universal applicability. In addition, a large number of studies on black and odorous water bodies are qualitative studies of water body types, and there are few spatially continuous quantitative analyses. Quantitative research on black and odorous waters is needed to identify the risk of health and environmental problems, as well as providing more accurate guidance on mitigation and treatment methods. In order to achieve this, a universal continuous black and odorous water index (CBOWI) is proposed that can classify waters based on evaluated parameters as well as quantitatively determine the degree of pollution and trends. The model of CBOWI is obtained by partial least squares machine learning through the parameters of the national black and odorous water classification standard. The fitting accuracy and monitoring accuracy of the model are 0.971 and 0.738, respectively. This method provides a new means to monitor black and odorous waters that can also help to improve decision-making and management.

Cloud and cloud shadow detection is a crucial procedure in remote sensing image processing. While the threshold method is a common method for its easy implementation, it compromises on accuracy for complex surface conditions. To address such limitations, a cloud detection algorithm based on land type has been proposed. As an extension of this, a novel cloud shadow detection algorithm based on an a Priori Land type and Reflectance Database Support (denoted as PLRDS) is proposed. This algorithm uses GlobeLand30 data and Landsat-8 images as support information for land cover and reflectance data, respectively. By utilizing this support information, optimal bands and thresholds are determined for cloud shadow detection in each land type. The threshold determination is discussed in terms of absolute and grey areas. To evaluate the PLRDS algorithm, experiments were conducted on Landsat-8 data for each land type and on complex surfaces. The results show good consistency compared to false-color images. In addition, a quantitative validation was conducted with the producer’s accuracy of cloud shadow and clear land (PAS and PAC), user’s accuracy of cloud shadow and clear land (UAS and UAC), and overall accuracy (OA). The OA of the proposed algorithm is 0.9719. Among all the land types, bareland and artificial surfaces achieved the lowest and highest OA, which reached 0.9867 and 0.9483, respectively. These results indicate the accuracy and good performance of the PLRDS cloud shadow detection algorithm.

Baiyangdian Lake is the largest wetland ecosystem in North China, which has a special strategic position, so it is of great significance to study its temporal and spatial changes. Based on the 112 Landsat satellite remote sensing data from 1984 to 2020, this paper extracted the thematic information of Baiyangdian Lake open water body, carried out the study on the temporal and spatial evolution of the Baiyangdian Wetland ecosystem, and analyzed its area change trend as “increase-decrease-increase-decrease again-increase again ”. Among them, the Landsat7 ETM SLC-off satellite digital product with band noise during 2003-2012 was repaired, so that it can better reflect the mutation characteristics and overall laws of Baiyangdian Lake water body in the past 30 years. On this basis, the Baiyangdian Lake water quality parameters were inverted, and the conclusions drawn have certain reference significance for maintaining the ecological environment of Baiyangdian Lake.

The Langcun W-Mo deposit, located in the Zhejiang Province of South China, is a medium-sized porphyry deposit. The ore bodies mainly occur in aplite, granite porphyry, and the contact zone with hornfels of the Nanhua System. Four stages of mineralization are recognized in the Langcun deposit, including the quartz–K-feldspar stage (stage I), quartz–sericite–molybdenite stage (stage II), quartz–chlorite–pyrite stage (stage III), and calcite stage (stage IV). Stages I and II are the main ore-forming stages for wolframite and molybdenite. The petrographic and microthermometric results show that four types of fluid inclusions exist in the Langcun W-Mo deposit, including two-phase liquid-rich fluid inclusions (type LV), three-phase CO₂-rich fluid inclusions (type LC), pure CO₂ fluid inclusions (type C), vapor H₂O inclusions (type V), and multi-phase inclusions with daughter minerals (type LDV). In stage I, the fluid inclusion assemblage is LDV + LV + V, and the LDV and LV fluid inclusions have similar homogenization temperatures (281–387 °C), indicating a boiling fluid inclusions association. In stage II, the fluid inclusion assemblage is LC + C, indicating immiscibility between CO₂ and aqueous fluids. The homogenization temperatures of type LC are in the range of 228–342 °C, and the salinities are in the range of 2.77–5.14 wt.% NaCl equiv. The fluid inclusions in stages III and IV are type LV, with homogenization temperatures in the ranges of 224–275 °C and 200–225 °C, respectively, and salinities in the ranges of 1.74 to 4.96 wt.% NaCl equiv and 1.06 to 3.39 wt.% NaCl equiv, respectively. Hydrogen and oxygen isotopic results indicate that the ore-forming fluids mainly come from magmatic water in the early stage and may have received an input of meteoric water in the late stage, which results in the decrease in the temperature and salinity of ore-forming fluid. Early W-Mo precipitation was induced by CO₂ escape because of decompression, and fluid mixing resulted in Mo precipitation in the later stage.