Greenhouse vegetable production provides significant quantities of vegetables throughout the year and improves farmers’ income. However, over-fertilization with mineral fertilizer causes soil secondary salinization and decreases the stability of the soil structure. To improve aggregate formation and decrease salt accumulation in the soil profile, bio-organic fertilizers (Protaetia brevitarsis larvae frass with Bacillus amyloliticus and/or Trichoderma harziensis) were applied to partially substitute mineral fertilizer in a salinized vegetable soil. Soil nutrient condition, aggregate stability, and salt movement in the soil profile were measured in a greenhouse double-cucumber system. The results showed that soil organic matter (SOM), total nitrogen (TN), and available phosphorus (AP) increased significantly under bio-organic fertilizer treatments compared with control. Soil electrical conductivity (EC) and total salt content (TSC) decreased by 15.74–24.20% and 19.15–29.05%, respectively, with bio-organic fertilizers (p < 0.05). Cl⁻, NO₃⁻, and SO₄²⁻ content under double inoculation with B. amyloliticus and T. harziensis reduced by 31.19%, 26.30%, and 53.11%, respectively, compared to CK (p < 0.05). In addition, double inoculation was more efficient in reducing nitrate content in the soil profile than single inoculation. Soil microaggregates of 0.25–0.053 mm increased by 75.87–78.51% with bio-fertilizers compared with control, and double inoculation was the best for aggregate formation. In conclusion, the inoculation of plant-growth-promoting and salt-tolerant microorganisms with high humic acid larvae frass can alleviate salinization in vegetable soil, enhance soil nutrient content, and improve the soil structure.

Large-scale monitoring of water quality parameters (WQPs) is one of the most critical issues for protecting and managing water resources. However, monitoring optically inactive WQPs, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP) in inland waters, is still challenging. This study constructed retrieval models to explore the spatiotemporal evolution of TN, AN, and TP by Landsat 8 images, water quality sampling, and five machine learning algorithms (support vector regression, SVR; random forest regression, RFR; artificial neural networks, ANN; regression tree, RT; and gradient boosting machine, GBM) in the Nandu River downstream (NRD), a tropical river in China. The results indicated that these models can effectively monitor TN, AN, and TP concentrations at in situ sites. In particular, TN by RFR as well as AN and TP by ANN had better accuracy, in which the R² value ranged between 0.44 and 0.67, and the RMSE was 0.03–0.33 mg/L in the testing dataset. The spatial distribution of TN, AN, and TP was seasonal in NRD from 2013–2022. TN and AN should be paid more attention to in normal wet seasons of urban and agricultural zones, respectively. TP, however, should be focus on in the normal season of agricultural zones. Temporally, AN decreased significantly in the normal and wet seasons while the others showed little change. These results could provide a large-scale spatial overview of the water quality, find the sensitive areas and periods of water pollution, and assist in identifying and controlling the non-point source pollution in the NRD. This study demonstrated that multispectral remote sensing and machine learning algorithms have great potential for monitoring optically inactive WQPs in tropical large-scale inland rivers.