In this research, we derived indicator spectral bands and classification models for detecting diesel or gasoline pollution in soil using a near-and shortwave-infrared (NIR-SWIR) hyperspectral camera under a close range and laboratory condition. The soils samples were collected from temperate climate soil with spectral characteristics manifested by secondary minerals. The hyperspectral images show that the diesel and gasoline polluted soil samples have distinctive spectral differences from clean soil. Different from moisture soil, the spectral absorption features of petroleum hydrocarbons (PHCs) are preserved with an increase in gravimetric content. The more PHCs contents, the stronger the depths at the spectral absorption features. In diesel polluted soils, the absorption features were observed in various content levels. However, we found a detection limit for gasoline content in soil, because the absorption features by PHCs disappeared at 8 wt.%. To derive the indicator bands, the images were classified by the random forest algorithm (RF) with an accuracy and kappa coefficient of 94.3% and 0.92 using three groups of bands corresponding to ferric ion, C-H stretch/bending, and BTEX C-H absorptions. The detection models derived from a logistic regression achieved an overall accuracy of 91.82%. The field test of the models on unprocessed soils achieved an accuracy of 83.36%. Because of their simple forms, the logistic detection models can be transferred to remote sensing applications of soil PHCs pollution under a close-range condition such as drone-based projects.

This study developed case-adaptive spectral indices for detecting heavy metal pollution in skarn soil, hydrothermal soil, and acid mine drainage (AMD) soil, and a universal index for general cases by merging data from all cases. Sequential analyses were conducted including heavy metal concentration, mineral identification, grain size, and spectral characteristics. A factor analysis was used to combine multiple heavy metal elements into representative factors and used for establishing the spectral indices using the stepwise multiple linear regression (SMLR), which is compared with random forest regression (RFR). The spectral indices of skarn soil predict Zn, As, and Pb pollution from moderate to ultrahigh levels using absorption bands of skarn and supergene minerals. The spectral index for hydrothermal soil predicts pollution of Cr and Ni from very low to very high using absorption bands of hydrothermal alteration or supergene minerals. The index for the AMD soil predicts Al, Fe, and As pollution from very low to very high levels by employing the absorption features of iron hydroxide. The universal index predicts Fe, Zn, and Pb pollution levels from very low to ultrahigh by utilizing the clay and iron oxide absorptions, commonly observed in general soils. All models showed statistical significance with R ² = 0.66-0.92 for SMLR and R ² = 0.66-0.95 for RFR. Because our models were derived from large number of samples (600s) for detecting the pollution levels of multiple heavy metal elements for the first time, the models could provide a decision-making tool for soil survey in heavy metal pollution.

Effective monitoring of inland waters requires approaches capable of capturing high spatial and temporal variability. Traditional in situ sampling provides accurate point measurements but lacks spatial coverage, while satellite remote sensing is often limited by coarse spatial resolution and cloud cover. To address these limitations, this study developed and validated an integrated monitoring platform combining an Autonomous Surface Vehicle (ASV) and a drone-based multispectral imaging system for flexible, high-resolution water quality monitoring. The study was conducted in two contrasting aquatic environments in Alabama: the North River–Lake Tuscaloosa system and the Sardine Pass and Duck Skiff Pass tidal inlets in Mobile Bay. A HyCAT ASV equipped with a YSI EXO2 multiparameter sonde collected continuous in situ measurements of turbidity, chlorophyll-a (Chl-a), and fluorescent dissolved organic matter (fDOM), which served as water-truth for a MicaSense Dual multispectral camera onboard a DJI Inspire-2 drone platform acquiring imagery in 10 spectral bands at ~8 cm spatial resolution. Machine learning models, including ensemble and Random Forest approaches, were developed and compared with traditional empirical algorithms. Ensemble models consistently outperformed empirical approaches, while Random Forest models achieved the highest accuracy and best generalization across variable environmental conditions. Compared with Sentinel-2 and Landsat-8 imagery, the drone-derived maps resolved fine-scale spatial variability, including sediment plumes and near-shore gradients, that could not be detected by satellite sensors. To facilitate operational implementation, the RS-WaterQuality Mapper software tool was expanded to support ensemble and Random Forest analyses for MicaSense imagery. Overall, the integrated ASV–drone system demonstrated substantial advantages over traditional sampling and satellite remote sensing, including rapid deployment, user-controlled acquisition timing, high spatial resolution, and improved monitoring of small and optically complex water bodies, highlighting its potential for adaptive water resource management and early warning applications.

Large lakes exhibit spatial variations in water levels, leading to distinct lake surface topography. Accurate characterization of lake surface topography enables improved understanding of lake dynamics, hydrological exchanges with surrounding ecosystems, and water balance assessments. Satellite radar altimetry has been widely used in monitoring lake water. Most radar altimetry missions carry a nadir-looking profiling altimeter that measures water level only along their fixed ground tracks, leaving huge data gaps between their ground tracks. The recently launched Surface Water and Ocean Topography (SWOT) mission has produced wide swath 2D elevation measurements at high spatial resolution over the water bodies on a global scale. However, its long repeat cycle of 21 days limits the temporal frequency of observations. In this study, we present a method to map lake surface topography on a near-daily basis by integrating 2D SWOT observations with 1D radar altimetry observations. The method consists of three components. First, the inter-mission biases between each of the four operational radar altimetry missions (i.e., Jason-3, Sentinel-3A, -3B, and Sentinel-6) and SWOT are estimated based on their concurrent and co-located overpasses. Second, the intra-lake spatial pattern of water surface topography is derived by applying an Empirical Orthogonal Function (EOF) to the time series of daily SWOT wide-swath measurements. Third, the SWOT-calibrated profiling altimetry measurements from each radar altimetry mission were then propagated to the entire lake based on the spatial pattern from SWOT observations. Our method has been successfully applied to Lake Ontario, generating near-daily, high-accuracy time series maps of water surface topography.