In recent years, the utilization of machine learning algorithms and advancements in unmanned aerial vehicle (UAV) technology have caused significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV–satellite data fusion have emerged as two prominent approaches for the estimation of vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for the mapping of crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV–satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV–satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV–RapidEye dataset exhibits the highest coefficient of determination (R²) and the lowest root mean square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian process regression (GPR) (R² = 0.89 and 0.89 and RMSE = 8.99 µg/cm² and 9.65 µg/cm², respectively). Similar performance is observed for the UAV–Landsat and UAV–PlanetScope datasets (R² = 0.86 and 0.87 for KRR, respectively). For the hybrid models, the maximum performance is attained with the Landsat data using KRR and GPR (R² = 0.77 and 0.51 and RMSE = 33.10 µg/cm² and 42.91 µg/cm², respectively), followed by R² = 0.75 and RMSE = 39.78 µg/cm² for the PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, the RapidEye data yield the most stable performance, with the R² ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm² to 33.07 µg/cm². The study highlights the importance of synergizing UAV and satellite data, which enables the effective monitoring of canopy chlorophyll in small agricultural lands.

This study assessed the extent of potential toxic element (PTE) contamination and its associated health risks in groundwater from the Tista Floodplain, Rangpur, northern Bangladesh. A total of 130 groundwater samples (65 from the wet season and 65 from the dry season) were analyzed for six PTEs: As, Al, Cu, Mn, Cr, and B using Atomic Absorption Spectrophotometry (AAS) and VIS Spectrophotometry. The mean concentrations of PTEs followed a decreasing order of B > Mn > Cu > Al > As > Cr in the wet season and Mn > B > Al > Cu > As > Cr in the dry season. Metal evaluation index and degree of contamination indicated minor contamination, while the Nemerow pollution index showed moderate to severe contamination, especially in Nilphamari district. Contamination levels were higher in the dry season compared to the wet season. No individual PTEs posed non-carcinogenic health risks, but combined PTEs presented potential health threats, particularly to infants, with higher severity during the dry season. No dermal health risks were identified. Despite the relatively low contamination levels, monitoring policies for new and existing tubewells should be implemented to ensure sustainable groundwater management and long-term water quality.