Monitoring water quality is crucial for sustainable water management and meeting the United Nations Sustainable Development Goals. Urbanisation, agricultural practices, industrial activities, and population growth increase the presence of biological, chemical and physical properties in water bodies. Traditional water quality monitoring methods (laboratory and in situ measurements) are limited spatially, temporarily and are costly. Satellite remote sensing has been shown to provide a systematic, cost-effective, and near-real-time alternative. This paper analysed 142 peer-reviewed articles published between 2002 and 2024 from Web of Science and Scopus databases. The final included articles in the review were achieved through the PRISMA flowchart. The review revealed that low-resolution sensors with long-term records, such as MODIS, were commonly applied to study large lakes. In contrast, sensors such as Landsat-8 and Sentinel-2 were applied for both lakes and dams. These sensors contain necessary spectral regions for monitoring water quality, where it was shown that the 500–600 nm region is critical for chlorophyll assessment, while the 640–670 nm region is used for turbidity. The Secchi disk depth and the total suspended solids were assessed using regions 860–1040 nm and 1570–1650 nm. Water quality research also focused on countries such as China, India, Brazil, and South Africa, with an emphasis on optically active parameters. There is, however, limited research on non-optically active parameters, such as nitrogen, phosphorus, and temperature, especially in small inland water bodies. Therefore, there is a need for more research in these areas, using direct and indirect methods of water quality parameter estimation with the integration of machine learning algorithms.

Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna.

The emergence of the spectral variation hypothesis (SVH) has gained widespread attention in the remote sensing community as a method for deriving biodiversity information from remotely sensed data. SVH states that spectral heterogeneity on remotely sensed imagery reflects environmental heterogeneity, which in turn is associated with high species diversity and, therefore, could be useful for characterizing landscape biodiversity. However, the effect of phenology has received relatively less attention despite being an important variable influencing plant species spectral responses. The study investigated (i) the effect of phenology on the relationship between spectral heterogeneity and plant species diversity and (ii) explored spectral angle mapper (SAM), the coefficient of variation (CV) and their interaction effect in estimating species diversity. Stratified random sampling was adopted to survey all tree species with a diameter at breast height of > 10 cm in 90 × 90 m plots distributed throughout the study site. Tree species diversity was quantified by the Shannon diversity index (H′), Simpson index of diversity (D₂) and species richness (S). SAM and CV were employed on Landsat-8 data to compute spectral heterogeneity. The study applied linear regression models to investigate the relationship between spectral heterogeneity metrics and species diversity indices across four phenological stages. The results showed that the end of the growing season was the most ideal phenological stage for estimating species diversity, following the SVH concept. During this period, SAM and species diversity indices (S, H′, D₂) had an r² of 0.14, 0.24, and 0.20, respectively, while CV had an r² of 0.22, 0.22, and 0.25, respectively. The interaction of SAM and CV improved the relationship between the spectral data and H′ and D₂ (from r² of 0.24 and 0.25 to r² of 0.32 and 0.28, respectively) at the end of the growing season. The two spectral heterogeneity metrics showed differential sensitivity to components of plant diversity. SAM had a high relationship with H′ followed by D₂ and then a lower relationship with S throughout the different phenological stages. Meanwhile, CV had a higher relationship with D₂ than other plant diversity indices and its relationship with S and H′ remained similar. Although the coefficient of determination was comparatively low, the relationship between spectral heterogeneity metrics and species diversity indices was statistically significant (p < 0.05) and this supports the assertion that SVH could be implemented to characterize plant species diversity. Importantly, the application of SVH should consider (i) the choice of spectral heterogeneity metric in line with the purpose of the SVH application since these metrics relate to components of species diversity differently and (ii) vegetation phenology, which affects the relationship that spectral heterogeneity has with plant species diversity.