This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity.

Land use change studies permeate the geographic literature. While these studies have helped researchers understand the dynamics and importance of such changes, they have less often taken a deeper historical approach in combination with their traditional strengths of geographic information analysis. In this study, we explore historical land use changes in one of South Africa's former bantustans, Lebowa, from 1963 to 2001. We argue that changes in land use arise from both current socioeconomic dynamics but also from historical precedent established by the apartheid regime. Our methods couple historical aerial photography to recent household surveys to elucidate the national, regional and local influences over land use change. We conducted extensive field research in the study site between 2003 and 2006. Our findings show a high degree of urbanization, a loss of grassland and agricultural land and a dramatic pattern of increasing spatial concentration near growth points. We outline three recommendations for policymakers planning post‐apartheid rural spaces and conclude with future research needs.

Arsenic-based compounds have been used for cattle dipping for about half a century to combat East Coast Fever in cattle in South Africa. The government introduced a compulsory dipping programme in communal areas to eradicate the disease in 1911. Concern has been raised regarding the ecological legacy of the use of arsenic-based compounds in these areas. We investigated the incidence of arsenic residue in soil at 10 dip sites in the Vhembe district of Limpopo Province, South Africa. We found high levels of arsenic contamination at a depth of 300 mm at some sites. Control samples indicated that these high arsenic levels are the result of the application of inorganic arsenic. Variation of arsenic concentrations is attributed to duration of exposure to the chemical, soil properties and distance from the dip tank. Concerns are raised regarding the structural condition of the dip tanks, encroaching villages and possible health threats to the human population in the area.

Soil organic carbon constitutes an important indicator of soil fertility. The purpose of this study was to predict soil organic carbon content in the mountainous terrain of eastern Lesotho, southern Africa, which is an area of high endemic biodiversity as well as an area extensively used for small-scale agriculture. An integrated field and laboratory approach was undertaken, through measurements of reflectance spectra of soil using an Analytical Spectral Device (ASD) FieldSpec® 4 optical sensor. Soil spectra were collected on the land surface under field conditions and then on soil in the laboratory, in order to assess the accuracy of field spectroscopy-based models. The predictive performance of two different statistical models (random forest and partial least square regression) was compared. Results show that random forest regression can most accurately predict the soil organic carbon contents on an independent dataset using the field spectroscopy data. In contrast, the partial least square regression model overfits the calibration dataset. Important wavelengths to predict soil organic contents were localised around the visible range (400–700 nm). This study shows that soil organic carbon can be most accurately estimated using derivative field spectroscopy measurements and random forest regression.

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