Chlorophyll-a (chl-a) serves as an indicator of productivity in surface water. Estimating chl-a concentration is pivotal for monitoring and subsequent conservation of surface water quality. Artificial neural network (ANN) based models were validated and tested for their efficacy against various regression models to determine the chl-a concentration in the Upper Ganga river. Landsat-8 Operational Land Imager (OLI) surface reflectance (SR) imagery for May and October along with in-situ data over a period of 2 years (2016–2017) was used to develop and validated models. Regression model performance was acceptable with a coefficient of determination (R²) of 0.57, 0.63, 0.66 and 0.68 for linear, exponential, logarithmic and power model, respectively. However, there was a significant improvement in the efficacy of chl-a determination using ANN model performance having a root mean square error (RMSE) of 1.52 µg l^–1 and R² = 0.97 in comparison to the best-performing regression model (power) with RMSE = 9.86 µg l^–1 and R² = 0.68. ANN exhibited comparatively more precise spatial and seasonal variability with mean absolute error (MAE) of 1.26 µg l^–1 as compared to the best regression model (power) MAE = 7.98 µg l^–1 suggesting the applicability of ANN for large-scale spatial and temporal monitoring river stretches using Landsat-8 OLI SR images.

The composition and structure of macrophyte communities of the littoral region of Bhindawas wetland (India) was investigated. Two hypotheses were tested: (1) if limnological variables such as water chemistry and soil parameters are important determinants of species composition and abundance and (2) what is the variance in species richness and composition within the wetland. Twenty-eight macrophyte species belonging to 17 different families were identified with relatively low species diversity and richness (H′ = 2.09; Margalef index = 1.12). Eichhornia crassipes, attained the highest relative frequency (22.69%), followed by Salvinia natans, Paspalum paspaloides, Phragmites karka, Cynodon dactylon and Cyperus alopecuroides. The first and second axis of CCA ordination explained 38.4% and 28.5% of the total variance of species–limnological variables relationship, respectively. Water-related variables (dissolved oxygen, pH and nitrate concentration) and soil nutrient factors (soil carbon and phosphorus levels) were significantly correlated to macrophyte distribution. Sampling sites formed two distinct groups based on cluster analysis, indicating varying ecological conditions. Generally, composition and abundance of macrophytes evince highly productive nature of the lake, while dominance of invasive species like E. crassipes and S. natans indicates high influence of increased nutrient levels and anthropogenic activities in the catchment on lake macrophyte composition.