It is necessary to further investigate the spatial considerations, temporal characteristics, and drivers of change affecting the beginning and end of soil freezing and thawing, including the maximum depth of the seasonal freezing (MDSF) and the active layer thickness (ALT) in Northeast China. Hourly soil temperature, among other data, from 1983–2022 were investigated, showing a delay of about 6 days in freezing. In contrast, thawing and complete thawing advanced by about 26 and 20 d, respectively. The freezing period and total freeze-thaw days decreased by about 29 and 23 days, respectively. The number of complete thawing period days increased by about 22 days, while the MDSF decreased by about 25 cm. The ALT increased by about 22 cm. Land Surface Temperature (LST) is the main factor influencing the beginning and end of soil freezing and thawing, MDSF and ALT changes in Northeast China; air temperature, surface net solar radiation, and volumetric soil water content followed. The influence of the interacting factors was greater than the single factors, and the interactive explanatory power of the LST and surface net solar radiation was highest when the soil started to freeze (0.858). The effect of the LST and the air temperature was highest when the soil was completely thawed (0.795). LST and the volumetric soil water content interacted to have the first explanatory power for MDSF (0.866) and ALT (0.85). The results of this study can provide scientific reference for fields such as permafrost degradation, cold zone ecological environments, and agricultural production in Northeast China.

Maintaining high water quality is essential not only for human survival but also for social and ecological safety. In recent years, due to the influence of human activities and natural factors, water quality has significantly deteriorated, and effective water quality monitoring is urgently needed. Traditional water quality monitoring requires substantial financial investment, whereas the remote sensing and random forest model not only reduces operational costs but also achieves a paradigm shift from discrete sampling points to spatially continuous surveillance. The random forest model was adopted to establish a remote sensing inversion model of three water quality parameters (conductivity, total nitrogen (TN), and total phosphorus (TP)) during the growing period (May to September) from 2020 to 2022 in the Songhua River Basin (SRB), using Landsat 8 imagery and China’s national water quality monitoring section data. Model verification shows that the R² of conductivity is 0.67, followed by that of TN at 0.52 and TP at 0.47. The results revealed that the downstream conductivity of SRB (212.72 μS/cm) was significantly higher than that upstream (161.62 μS/cm), with TN and TP concentrations exhibiting a similar increasing pattern. This study is significant for improving ecological conservation and human health in the SRB.