Ulva prolifera (U. prolifera), a global eco-environmental issue, has been recurring annually in the Yellow Sea of China since 2007, leading to significant impacts on the coastal ecosystem and the economies of coastal cities. To enhance the frequency of daily monitoring for U. prolifera and to advance the multi-source remote sensing monitoring system, a combination of the Sentinel-1 SAR remote sensing satellite and the Sentinel-3 OLCI and NPP VIIRS optical remote sensing satellites was employed. This comprehensive analysis encompassed the examination of Sentinel-1 C band characteristics, the range of influence of U. prolifera, and the migration trajectory of its enrichment zones. On 6 June 2021, three satellite images depicted the northwest drift of U. prolifera, followed by a southward movement after making contact with the coast of Qingdao, China, on 12 June. The most extensive impact area caused by U. prolifera was observed on 18 June. Subsequently, the images revealed a contraction and enrichment of U. prolifera in an eas–-west direction. The amalgamation of radar and optical remote sensing satellites in a multi-frequency monitoring approach allows for a continuous all-weather surveillance mechanism for U. prolifera. This mechanism serves to provide timely alerts for the prevention and management of U. prolifera outbreaks.

Carbon dioxide (CO₂) is currently the most harmful greenhouse gas in the atmosphere. Obtaining long-term, high-resolution atmospheric column CO₂ concentration (XCO₂) datasets is of great practical significance for mitigating the greenhouse effect, identifying and controlling carbon emission sources, and achieving carbon cycle management. However, mainstream satellite observations provide XCO₂ datasets with coarse spatial resolution, which is insufficient to support the needs of higher-precision research. To address this gap, in this study, we integrate spatial information with the extreme random trees model and develop a new machine learning model called spatial extreme random trees (SExtraTrees) to reconstruct a 1 km spatial resolution XCO₂ dataset for China from 2016 to 2020. The results indicate that the predictive ability of spatial extreme random trees is more stable and has higher fitting accuracy compared to other methods. Overall, XCO₂ in China shows an increasing trend year by year, with the spatial distribution revealing significantly higher XCO₂ levels in eastern coastal regions compared to western inland areas. The contributions of this study are primarily in the following areas: (1) Considering the spatial heterogeneity of XCO₂ and combining spatial features with the advantages of machine learning, we construct the spatial extreme random trees model, which is verified to have high predictive accuracy. (2) Using the spatial extreme random trees model, we reconstruct high-resolution XCO₂ datasets for China from 2016 to 2020, providing data support for carbon emission reduction and related decision making. (3) Based on the generated dataset, we analyze the spatiotemporal distribution patterns of XCO₂ in China, thereby improving emission reduction policies and sustainable development measures.

Cyanobacterial blooms are a widespread phenomenon in aquatic ecosystems worldwide, causing significant harm to the ecological environment. Lake Taihu is the third-largest freshwater lake in China. The region has been increasingly affected by cyanobacterial blooms, drawing greater attention from people. Currently, numerous models have been developed for detecting algal bloom based on spectral characteristics. However, the intuitive basis of optical detection lies in water color. Therefore, constructing an algal bloom detecting model from the perspective of chromaticity is worth exploring. This study constructed an algal bloom detecting model based on chromatic parameters, DFLH, and IAVW by using hyperspectral data from Lake Taihu. It further applied the model to the ZY-1E hyperspectral satellite for large-scale algal bloom monitoring. The threshold for detecting cyanobacterial blooms is defined as DFLH > 0.013 sr⁻¹ and Hue Angle > 170.58 degrees; the threshold for the normal water is defined as DFLH < 0.013 sr⁻¹. The parameter thresholds for the floating leaf vegetation range were defined as DFLH > 0.013 sr⁻¹, Saturation < 0.07, and IAVW > 598 nm. Through the validation, in the modeling dataset, the overall accuracy (OA) value is 0.81 and the F₁-score is 0.86. In the validation dataset, the overall accuracy (OA) value is 0.83 and the F₁-score is 0.89. The model demonstrates good detecting performance. Regarding its application on the ZY-1E satellite, we validated the detection results accuracy through matching synchronized in situ algal density data. The results are as follows: OA is 0.95, and the F₁-score is 0.95. The results above indicate that the algal bloom detection method developed in this study had a good accuracy in detecting algal blooms in Lake Taihu on 6 September 2020. This study provided the algae bloom detecting model based on water color characteristics in Lake Taihu, which had high detecting accuracy.