Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.

The biophysical modeling of water ecosystem services is crucial to understanding their availability, vulnerabilities, and fluxes. Among the most popular models, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models stand out. While many studies have used them, few have assessed their performance. This study evaluates the performance of InVEST’s Seasonal Water Yield, Nutrient Delivery Ratio, and Sediment Delivery Ratio models in a subtropical basin in southeastern Brazil on temporal and spatial scales, using 39 years of streamflow data, 29 for total phosphorus and total nitrogen, and 19 for total suspended solids. Statistical indicators R², PBIAS, and NSE, were also calculated. The performance of the models varied according to the type of simulated WES and analysis scales used, with the Seasonal Water Yield model demonstrating the best performance and effectively representing the spatial and temporal variability of the average annual streamflow. All models performed well in simulating long-term mean values when compared to observed data. While one should bear in mind the study’s limitations, the results indicate that the models perform well in terms of relative magnitude, although their application in studies involving water-resource management and decision making is limited.

Additional measures of in situ water quality monitoring in natural environments can be obtained through remote sensing because certain elements in water modify its spectral behavior. One of the indicators of water quality is the presence of algae, and the aim of this study was to propose an alternative method for the quantification of chlorophyll in water by correlating spectral data, infrared images, and limnology data. The object of study was an artificial lake located at Unisinos University, São Leopoldo/RS, Brazil. The area has been mapped with a modified NGB (near infrared (N), green (G) and blue (B)) camera coupled to an unmanned aerial vehicle (UAV). From the orthorectified and georeferenced images, a modified normalized difference vegetation index (NDVImod) image has been generated. Additionally, 20 sampling points have been established on the lake. At these points, in situ spectral analysis with a spectroradiometer has been performed, and water samples have been collected for laboratory determination of chlorophyll concentrations. The correlation resulted in two models. The first model, based on the multivariate analysis of spectral data, and the second model, based on polynomial equations from NDVI, had coefficients of determination (R2) of 0.86 and 0.51, respectively. This study confirmed the applicability of remote sensing for water resource management using UAVs, which can be characterized as a quick and easy methodology.

The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.

The study aims to understand when the effort required to run a distributed and detailed water balance model leads to effective better results, when compared with a lumped, data-parsimonious, and easier-to-apply hydrological model. The study compares the results obtained from a lumped hydrological model with the results from soil water assessment tool (SWAT) when used to estimate stream flow values from precipitation and climate data. Both models use the curve number (CN) concept, determined from land use, soil hydrologic group, and antecedent soil moisture conditions, and were run with a daily time step. The lumped model input variables and parameters are considered to be uniformly distributed throughout the watershed, while the SWAT model requires an extensive and detailed spatial data, including weather data (rainfall, temperature, humidity, wind, and solar radiation), physical characteristics of the basin (topography and drainage network), as well as soil and land use data. The effort required to fully understand SWAT formulation, to obtain the needed data and to calibrate the model, is time consuming and does not lead to significant better results, when compared with the lumped model results. The results confirm that in many situations, the simpler lumped model leads to reliable values of hydrological variables, both at a daily and monthly scale. The models’ predictability power is higher at a monthly time step, because both models fail to reproduce the daily flow values fully in a consistent way, although they are able to reproduce the overall variability. Lack of data to describe the precipitation accurately may explain the poorer model performance at the daily time step. The study concludes that when available data fail to represent the spatial distribution of the input variables and model parameters, there is little advantage in using the more complex distributed model.

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R² values of greater than 0.60, consistent with literature values.