Assessing the impact of land use and land cover (LULC) on water quality (WQ) is central to land-based environmental research. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this study analyses recent trends using LULC maps to assess stream WQ within buffers, focusing on papers published between 2020 and 2024. It identifies relevant remote sensing practices for LULC mapping, landscape metrics, WQ physicochemical parameters, statistical techniques for correlating LULC and WQ, and conventions for configuring buffers. Materials include Scopus, Web of Science, and Atlas.ti, which support both qualitative data analysis and Conversational Artificial Intelligence (CAI) tasks via its integration with OpenAI’s large language models. The methodology highlights creating a bibliographic database, coding, CAI, and validating prompts. Official maps and visual or digital interpretations of optical imagery provided inputs for LULC. Classifiers from earlier generations have shaped LULC cartography. The most employed WQ parameters were phosphorus, total nitrogen, and pH. The three most referenced landscape metrics were the Largest Patch Index, Patch Density, and Landscape Shape Index. The literature mainly relied on Redundancy Analysis, Principal Component Analysis, and alternative correlation approaches. Buffer configurations varied in size. CAI facilitated an agile systematic review; however, it encountered challenges related to a phenomenon known as hallucination, which hampers its optimal performance.

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country’s economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix.

Environmental research underscores that land use and land cover (LULC) influence key ecological components, such as water quality (WQ). This study examines how seasonal LULC affects WQ in the Cebollatí and Olimar Grande rivers in the Merín Lagoon Watershed, Uruguay. This region encompasses diverse natural and anthropogenic LULC. It is a key rice-producing area in South America, making it a relevant case study for the Global South, which is often underrepresented in similar research. The analysis focuses on buffers of 1500, 2500, and 5000 m, as well as the catchment defined by WQ sampling sites (SAS). The primary materials and methods comprise summer and winter seasonal LULC maps for 2024, WQ variables (WQVs), and statistical analyses. WQVs comprise total phosphorus (TP), total nitrogen (TN), nitrate (NO₃⁻), dissolved oxygen (DO), and conductivity (EC). The statistical approaches include the paired t-test, the coefficient of determination (R²), and partial least squares regression (PLSR). Most agricultural land is concentrated in buffers. Statistically significant seasonal differences were identified for TP, DO, and EC. The R² scores and PLSR indicate that seasonal and spatial differences exist in how different scales explain WQVs. The multiscale approach demonstrated how buffers and catchments influence the seasonally dissimilar effects of LULC on WQ. Maps were valuable for analysis, but relying solely on them overlooks complexities in the study area’s biophysical and socioeconomic conditions. Future research should develop local case studies across various areas of the CLM to provide a more comprehensive analysis.

Land use and land cover mapping is a key tool for understanding how territorial configuration influences biotic, abiotic, and anthropic components. In this regard, Geo Big Data technologies enable the agile and accurate generation of cartographic products. This study proposes two solutions for mapping land use and land cover in Uruguay for the 2021–2022 agricultural year. The inputs include Sentinel-1 and Sentinel-2 imagery, Google Earth Engine, GEEMAP, Scikit-learn, and the Random Forest and Support Vector Machines algorithms. The methodology highlights the creation of a multitemporal dataset, hyperparameters tuning, and supervised classification. As a result, two maps were generated: S1S2RF_uy and S1S2SVM_uy. Both products exhibited elevated levels of accuracy, although S1S2RF_uy performed slightly better, with an overall accuracy of 83 % and a kappa coefficient of 0.81, compared to 81 % and 0.78 for S1S2SVM_uy. At the class level, Random Forest showed a greater ability to classify agricultural covers, while Support Vector Machines were more effective in identifying artificial surfaces such as urban fabric. The findings confirm that hyperparameter tuning is essential for optimal classifier performance. Based on the reported accuracy statistics, it is also demonstrated that freely accessible Geo Big Data resources are well-suited for the efficient production of national-scale cartography at medium-to-high spatial resolution. Future research should prioritise regional focus and extend timeframes beyond the traditional agricultural year.

Geo Big Data and Sentinel-2 have been extensively employed to map rice paddies and general land use/land cover. Focusing on the environmental value and relevance of rice production in Cuenca de la Laguna Merín in Uruguay, the research aimed to 1) map rice paddies and other land use/land cover classes, 2) compare the capabilities of Random Forest and Support Vector Machine for classifying two different Sentinel-2 time series stacks, and 3) identify the most important features according to Random Forest. In addition to quoted imagery and classifiers, the materials include Google Earth Engine, GEEMAP, and Python's Scikit-learn GridSearchCV. The main methods comprised hyperparameter tuning, supervised classification, and accuracy assessment. Quoted assessment revealed that the four maps performed well. The feature importance analysis highlighted the Near-Infrared and Shortwave Infrared as the most relevant features. Future research should focus on integrating diverse data sources and comparing different time series than those employed here.