Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R² = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

A significant proportion of the solar irradiance that reaches the Earth's surface is normally attenuated by atmospheric properties and overcast conditions related to the rainy season. The Solar Analyst (SA) model, irradiance and long term precipitation data were used to assess this relationship in Guadalajara, Mexico. A spatial analysis based on morphological and statistical criteria increased the model's certainty. The SA model explains 95.4% of the irradiation variability observed on the ground, with average uncertainties of 3.7% during clear sky conditions in the dry season and 4.4% on sunny days in the wet season. The meteorological data analysis shows that total precipitation in 2014 had an atypical temporal distribution and was slightly lower (12.6%) than the average from 1991 to 2012. A deficit of 39% in precipitation compared to the long term average was found in the first half of the season, which was later partially compensated. This deficit was interpreted as a temporary delay in high values of precipitation. Based on the potential average irradiation from the SA model and field observations, it can be concluded that overcast conditions related to rainfall through 2014 attenuated approximately 28.5% of the incoming solar energy. Taking the global energy balance into account, this fraction was higher in comparison to the energy proportion reflected by the cloud's albedo (ca 23%). These results suggest that both the high proportion of energy attenuated and atypical weather conditions may be local effects of large‐scale phenomena such as the El Niño‐Southern Oscillation.

Herbicides have contributed to increased agricultural production. However, their residual amount can cause negative effects on environmental and public health. Therefore, this work aimed to determine the occurrence of both atrazine and diuron in surface and well water and investigate their link with drinking use. The samples were collected during dry and rainy seasons in three wells and surface water from a river and a pond located in the low plains of the Ixcatepec catchment, at the Amacuáhuitl community of the municipality of Arcelia, Guerrero State, in the center south of México, which is a rural community where farming is the main activity. The compounds were obtained by solid phase extraction and determined by HPLC-MS quadrupole with positive electrospray ionization mode. A geomorphic analysis was conducted inside the Ixcatepec catchment using the digital elevation model of the Shuttle Radar Topography Mission, SRTM-v4. The human risk for drinking water was calculated according to the Hazard Quotient. The concentrations of atrazine and diuron were between 5.77 and 402 ng L⁻¹. Atrazine was the most abundant and frequent pesticide found with an average concentration of 105.18 ng L⁻¹, while that of diuron was 86.56 ng L⁻¹. The highest levels were found in pond Ushe, likely being the result of the lowest flow and stagnation of water, and during the cold-dry season a consequence of mobilization by irrigation runoff. The morphological analysis indicated that the compounds mainly reached body water located in the lower surfaces from cultivated areas. Therefore, the occurrence is mainly linked to agriculture activity within the rural community. However, chemical properties of compounds, crop irrigation, and environmental conditions could be contributing to the dispersion of residual amounts of herbicides within the hydrological system. The estimation of risk showed that atrazine can mainly generate health problems for children using the Azul well as a source of drinking water.