The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH₄-N), nitrate-nitrogen (NO₃-N), and pH) were modeled and verified. The results show that the performance R² of the training model is above 80%, and the performance R² of the verification model is above 70%. In the training model, the highest fitting degree is TN (R² = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R² = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

The potential of biodiversity loss to impair the delivery of ecosystem services has motived ecologists to better understand the relationship between biodiversity and ecosystem functioning. Although increasing evidence underlines the collective contribution of different biodiversity components on the simultaneous performance of multiple functions (multifunctionality), we know little about the trade‐offs between individual diversity effects and the extent to which they determine multifunctionality differentially. Here, at a subcontinental scale of 62 dryland sites, we show in phototrophic microbiota of biological soil crusts (biocrusts) that, whereas richness alone is unable to guarantee the maxima of multifunctional performance, interspecies facilitation and compositional identity are particularly stronger but often neglected predictors. The inconsistent effects of different biodiversity components imply that soil multifunctionality can be lost despite certain species remaining present. Moreover, we reveal a significant empirical association between species functional importance and its topological feature in co‐occurrence networks, indicating a functional signal of species interaction. Nevertheless, abundant species tend to isolate and merely interact within small topological structures, but rare species were tightly connected in complicated network modules. Our findings suggest that abundant and rare species of soil phototrophs exhibit distinct functional relevance. These results give a comprehensive view of how soil constructive species drive multifunctionality in biocrusts and ultimately promote a deeper understanding of the consequences of biodiversity loss in real‐world ecosystems.