Microalgal amendments can improve soil structure by regulating extracellular polymeric substances (EPSs). However, the mechanisms underlying this process in red soils (characterized by high clay content and susceptibility to acidification) under different farming practices remain unclear. This study examined how Chlorella vulgaris (C. vulgaris) amendment influences EPS composition to enhance soil aggregate stability under arable land and rice paddy farming. A five-month pot experiment using a completely randomized design was conducted to investigate the effects of Chlorella vulgaris amendment on soils cultivated with Pennisetum × sinese and rice, two economically important crops commonly grown in South China. At the end of the experiment, Chlorella vulgaris amendment substantially increased both the mean weight diameter (MWD) and geometric mean diameter (GMD) of soil aggregates under both farming systems. Excitation–emission matrix (EEM) fluorescence spectroscopy revealed distinct changes in soil EPS components between the two farming types. Under arable land farming, humic-like and protein-like EPSs were dominant in Chlorella vulgaris-amended treatments, with fluorescence intensities more than doubling compared to the control. Conversely, under rice paddy farming, soil fulvic acid was the main component and showed a moderate increase. Partial least squares path modeling (PLS-PM) demonstrated that protein-like and humic-like EPSs had the strongest direct effects on aggregate stability in arable land red soil, while fulvic acid was the key factor in rice paddy red soil. The present study demonstrates that Chlorella vulgaris amendment improves aggregate stability in red soils through farming-specific, EPS-mediated pathways, providing a quantitative framework for researchers and land managers seeking to apply microalgal amendments for red soil enhancement and sustainable land management.

The spatial resolution of current microwave remote sensing soil moisture (SM) data is about 25 km in global scale. The coarse scale hinders the application of SM product at regional scale. The global 9 km SM can be released by radar observations of Soil moisture Active and Passive (SMAP) satellite since 2015. For the failed radar sensor, SMAP 9 km SM is less than three months. Therefore, European Space Agency Climate Change Initiative (CCI) SM data is downscaled to 9 km using spatial temporal fusion model in the study. And the 43-year 9 km SM is downscaled by CCI data from 1978 to 2020. Results display that downscaled 9 km SM gets more detailed spatial information than CCI data. Moreover, temporal variation of CCI data in anomaly can be well captured by downscaled data. The evaluations against in-situ data indicate that temporal accuracies of downscaled data (r = 0.676, μbRMSE = 0.069 m³/m³) are comparable with CCI data (r = 0.670, μbRMSE = 0.070 m³/m³). Overall, downscaled data improves the spatial resolution of CCI data and inherits the temporal accuracy with slight improvement. Higher spatial resolution SM offers greater application potential. Additionally, the model herein enriches SM downscaling techniques.