Bridging the divide between inland water quantity and quality with satellite remote sensing: An interdisciplinary review

TL;DR: Urban fine-grained water quality monitoring based on stacked machine learning approach achieves high accuracy for water quality inversion. The proposed model, Stacked-RF, utilizes ensemble learning and optimal weight scheme to combine multiple machine learning techniques, leading to improved performance compared to traditional single models.

TL;DR: The Global Lake area, Climate, and Population (GLCP) dataset is extended to include ice, snow, and radiation-related climate variables, enabling assessment of lake water variability from local-to-global and monthly-to-decadal scales with FAIR data principles.

TL;DR: This study assesses the environmental impacts of a small dam on water quality in an urban stream in southeastern Brazil, finding significant changes in temperature, pH, and nutrient concentrations, with implications for water quality management in similar settings.

Abstract: Intensified agriculture practices contribute to nutrient enrichment in freshwater lakes, causing eutrophication, algal blooms, and water hyacinth infestations. Eutrophication in Lake Tana, the source of the Blue Nile in Ethiopia, necessitates effective monitoring due to rapid infestation of water hyacinths. While traditional monitoring is costly and limited in spatial and temporal coverage, remote sensing offers a promising alternative. This study develops a regression model to estimate Chlorophyll-a (Chl-a) concentration using in situ and remote sensing reflectance data. Field measurements from 143 locations across Lake Tana were used to validate the correlation equations. Results show that the Moderate Resolution Imaging Spectroradiometer (MODIS) in near-infrared reflectance exhibits the strongest linear relationship with in situ Chl-a measurements for August 2016 ( r 2 = 0.53), December 2016 ( r 2 = 0.56) and March 2017 ( r 2 = 0.61). The developed models were validated with a root-mean-square error of 2.76 μg/L, 5.89 μg/L, and 8.04 μg/L for August, December, and March, respectively. Applying the developed model from 2008–2018, the Chl-a concentration of the lake indicated an increasing trend, likely driven by non-point sources from surrounding watersheds, causing infestation of the lake by hyacinths since 2011. The agreement between MODIS and in situ Chl-a data, coupled with the satisfactory performance of the linear regression model, underscores that developing a regression model for Chl-a estimation from remote sensing in water hyacinth-infested lakes is a useful method in tracking spatiotemporal variations. This study will serve as a foundation for future Chl-a variation studies in Lake Tana and other similar lakes.

TL;DR: Dual polarized (VV and VH) Sentinel-1 SAR images obtained for the entire monsoon period in 2017 were used to create inundation maps of the Kaziranga National Park and they were considered to be one of the worst flooding events inundating most areas of the park.

TL;DR: Results show that the dominant wavelength exhibits some relationship with the three types of OAC concentrations under certain conditions, particularly TSM and Chl-a; inorganic suspended matter can be retrieved by using MODIS derived dominant wavelength in appropriate water body and differences in water quality for different seasons can be detected by dominant wavelength and FU-class.

TL;DR: Khandelwal et al. as discussed by the authors presented a global set of satellite-derived time series of surface water storage changes for lakes and reservoirs for a period that covers the satellite-altimetry era.

TL;DR: In this paper, the authors use a reduced-order model to conduct a systematic analysis of discharge variability (peak-flow intensities, duration and skewness) and streambed topography (bedform aspect ratios, channel slopes) and their role in the flow and transport characteristics of hyporheic zones (HZ).