Combining organic and chemical fertilizers is a sustainable strategy for vegetable production. However, there is limited research concerning the risks associated with heavy metals (HMs) in greenhouse systems with long-term location application. A three-year investigation, conducted from 2021 to 2023, explored a fifteen-year field experiment with combinations of chemical fertilizer (CH), corn straw (SW) and pig manure (PM). Five treatments were evaluated: excessive fertilization (high CH and PM), conventional fertilization (normal CH), organic–inorganic fertilization (3/4CN + 1/4PN, 2/4CN + 2/4PN and 2/4CN + 1/4PN + 1/4SN). This study evaluated the risks associated with heavy metals (HMs) by analyzing and quantifying their concentrations in soil and cucumber fruits, as well as by calculating the bioconcentration factors (BCFs), the geo-accumulation index (Igeo), and both the non-carcinogenic and carcinogenic risks. The results indicated that excessive fertilization (CF) increased the concentrations of Cu and Zn in fruits, as well as the Igeo values of Cu, Zn, and Cd, and the non-carcinogenic Cu risk, while decreasing the BCFs of Cu and Zn. Organic–inorganic fertilization also elevated the Igeo values of Cu and Zn. Redundancy analyses confirmed a positive correlation between the soil concentrations of Cu and Zn and higher levels of available phosphorus contents (48.4%), alongside a lower pH (4.9%). The concentrations of Cu, Zn, and Cd in both soil and cucumber fruits increased linearly with the duration of application and amount of input. Although the combined application of CH with PM or SW did not significantly elevate the non-carcinogenic or carcinogenic risks associated with most heavy metals, the carcinogenic risks of Cd and As emerged as potential risk factors after 15 years of organic–inorganic fertilization. Utilizing a combination of CH with PM and SW as a fertilizer management strategy can effectively address both the control of heavy metal inputs in the facility and the safety and quality of cucumbers.

The spatial heterogeneity and dynamic changes in soil organic matter (SOM) are key indicators for assessing cultivated land quality and the carbon cycle. Currently, large-scale SOM monitoring relies primarily on limited ground sampling, making it difficult to capture continuous spatiotemporal variation patterns. Taking Anhui Province, China as the study area, this research integrates multi-source remote sensing and geostatistical methods to construct a multi-source collaborative SOM inversion model and analyze its spatiotemporal evolution patterns, thereby achieving high-precision, continuous spatiotemporal monitoring of SOM. A total of 3026 sampling points in Huangshan, Chuzhou and Fuyang cities in Anhui Province were selected as model training samples. The study divided the terrain into three elevation zones (40 m) and employed the Synthetic Minority Oversampling Technique (SMOTE) method to optimize sample distribution. Based on MODIS data, this study screened spectral bands and key phenological periods significantly correlated with SOM. By integrating spectral information from Landsat 8/9 OLI imagery, meteorological data and topographic factors, a random forest (RF) inversion model incorporating multi-source environmental variables was constructed. The results indicate that (1) the RF-based SOM inversion model exhibits moderate predictive accuracy acceptable for regional-scale SOM mapping, with a coefficient of determination (R²) of 0.55 and a root-mean-square error (RMSE) of 3.3 g/kg, effectively enabling the quantitative estimation of SOM at a regional scale. (2) The model’s inversion results reflect the spatial distribution of SOM in cultivated land in Anhui Province for the years 2019, 2022 and 2024. The provincial average SOM value shows an upward trend, with SOM content exhibiting a pattern of higher levels in the south and lower levels in the north, higher levels in the west and lower levels in the east, as well as a tendency to cluster. (3) Analysis using GeoDetector indicates that topography and precipitation are the primary drivers influencing SOM distribution, and the interaction between these two factors provides significantly greater explanatory power for SOM distribution than either factor alone. Through the integration of multi-source remote sensing data and model optimization, this study has validated the feasibility of multi-scale remote sensing-based SOM inversion, revealed the spatial differentiation characteristics and driving mechanisms of SOM in Anhui Province’s cultivated land, and provided a scientific basis for improving cultivated land quality and soil carbon sink management.