Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.

Land surface temperature (LST) and its diurnal variation are the critical factors in many aspects of climate study, surface energy balance, and environmental applications. Several satellite-based LST products are available for retrievals from regional to a global scale. However, due to the differences in sensor configurations and retrieval algorithms, these products may not necessarily be consistent. In this letter, the consistency of spatial and temporal skin temperature variations from two infrared satellite platforms has been evaluated over the contiguous United States (CONUS). Comparisons are made between the LST products from the newly launched Geostationary Operation Environmental Satellite R Series (GOES-R) advanced baseline imager (ABI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both Aqua and Terra satellites which are polar orbiting. Overall, both products show a general agreement in their diurnal variations with differences mostly under 2 K. However, a temperature-dependent inconsistency has been detected. The MODIS LST product seems to estimate higher temperatures in the summer months while the GOES product estimates higher temperatures during the winter months. Moreover, the maximum observed diurnal differences could reach up to 10 K in mountainous regions. The results suggest that the corresponding temperature differences should be accounted for when LST diurnal variations are compared or generated from satellite observations.

To engender and raise awareness to the geosciences, a geoscience research project and a corresponding geoscience internship program were designed around plume dispersion dynamics within and above the New York City subway system. Federal, regional, and local agencies partnered with undergraduate students from minority-serving institutions to conduct the largest plume dispersion study ever done in a complex, dense, urban-coastal metropolis. The students were engaged in an array of geoscience activities within the confines of geoscience learning communities. Assessment results indicate that the geoscience exposure and experience helped to stimulate and proliferate geoscience awareness and knowledge among the undergraduates.

Land use change is a crucial driving factor in hydrological processes. Understanding its long-term dynamics is essential for sustainable water resources management. This study sought to quantify and analyze land use change between 1985 and 2021 and its impacts on the hydrology of the Sejnane watershed, northern Tunisia. Remote sensing and a SWAT model using the SUFI-2 algorithm to identify the most sensitive parameters were used to achieve this objective. Land use maps were developed for 1985, 2001 and 2021. For the last 37 years, the watershed experienced a slight decrease in forest, scrubland and forage crops, a significant reduction in grassland, and a conspicuous expansion of olive trees and vegetable crops. Given the scarcity of observed discharge data, a SWAT model was calibrated for the period 1997–2010 and validated for 2011–2019. Model performance was good for both calibration (NSE = 0.78, PBIAS = −6.6 and R² = 0.85) and validation (NSE = 0.70, PBIAS = −29.2 and R² = 0.81). Changes in land use strongly affected the water balance components. Surface runoff and percolation were the most influenced, showing an increase in runoff and a decrease in percolation by 15.5% and 13.8%, respectively. The results revealed that the construction of the Sejnane dam, the extension of irrigated perimeters and olive tree plantations were the major contributors to changes in hydrology.

A LOICZ Budget Model is applied to the Ichkeul Lake, a wetland ecosystem of the South Mediterranean-North African region, to evaluate its functioning in order to boost water management. The Ichkeul Lake water and nutrient budget, net ecosystem metabolism (NEM), nutrient availability, and their seasonal changes are estimated using field data. A considerable anthropogenic-driven amount of nitrogen is transferred into N₂/N₂O to the atmosphere during the dry season with predominance of denitrification-anammox processes. The primary production is impacted by forcing the ecosystem respiration to reduce the NEM so that the system is functioning as heterotrophic. Climate change and anthropogenic pressures are expected to exacerbate the current trends of water quality degradation, with possible negative impacts on Palearctic birds’ population. Mitigation actions are possible, through the implementation of National Wetland Management Strategies that include nutrient load and water resources management.