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Biocrusts are common living covers found across drylands worldwide, and their photosynthesis substantially contributes to carbon input in these ecosystems. However, direct monitoring of biocrusts’ photosynthetic carbon fixation is challenging due to their scattered distribution, dark pigments, and lower CO2 exchange rate. Current studies have limited monitoring frequency and the results are difficult to extend to a broader spatial scale. Greenness typically functions as an indicator of both vegetation cover and plant photosynthesis. Monitoring biocrust greenness is expected to be able to provide insights into their environmental responses and estimate carbon fixation rates, particularly for moss biocrusts. Here, we monitored greenness, soil moisture and temperature, and gross primary productivity (GPP) of moss biocrusts, as well as climatic factors over two years in the northern China’s Loess Plateau. The results indicated that moss biocrust greenness was highly sensitive to changing environmental conditions and exhibited significant temporal variability. Moss biocrust greenness exhibited a notably higher value in wet season (0.384) than in dry season (0.259). According to these greenness values, we proposed moss biocrust activity and classified it into high (>0.403), medium (0.334–0.403), low (0.271–0.333), and dormancy (

Study region: The source region of the Yellow River (SRYE) is located in the eastern part of the Tibetan Plateau is a major water production and water conservation area for the Yellow River. Study focus: This study aims to investigate the correlations between streamflow and meteorological factors/ocean in the SRYE from 1960 to 2018 using wavelet analysis. The effects of meteorological factors/ocean signals on streamflow were calculated using the Partial Least Squares-Structural Equation Model (PLS-SEM). Furthermore, climate factors with strong correlation with streamflow were used as inputs to random forest (RF) and multiple linear regression (MLR) models to predict monthly streamflow. New hydrological insights: Meteorological factors showed stronger correlation with streamflow compared to ocean signals, explaining 79.3% of the streamflow variation and much higher than ocean signals (0.1%). Among the meteorological factors, precipitation had the largest direct effect on streamflow (P 0.05), which together explained 78% of the streamflow variability. Temperature and relative humidity are two important factors that indirectly influenced streamflow through potential evapotranspiration (P