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: In this paper, the authors show that 57% of the Earth's seasonal surface water storage variability occurs in human-managed reservoirs, whereas natural water bodies vary by only 0.22 meters.

TL;DR: In this paper, the spectral variations associated with heavy metals in soil and the possibility of extending the use of these parameters to hyperspectral images and to map the distribution of areas affected by heavy metals on HyMAP data was explored.

TL;DR: In this paper, the authors evaluate the potential for remote mapping of river bathymetry by examining the theoretical basis of a simple, ratio-based technique for retrieving depth information from passive optical image data, and performing radiative transfer simulations to quantify the effects of suspended sediment concentration, bottom reflectance, and water surface state.

TL;DR: In this paper, the authors report that despite the > 40 years of inland water observations made possible by optical remover, despite the availability of optical removals, the quality of these observations has not yet improved.