Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China\'s surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake\'s water quality. To ensure the continued health of Hulun Lake\'s water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.

This study authorizes processes and approaches using optical and microwave data to determine the availability of water in the study area at any given moment. This will aid in identifying the optimal time and location for irrigation to enhance crop growth. For this purpose, a set of spectral vegetation parameters (from Sentinel-2), soil moisture (from Sentinel-1), evapotranspiration, and surface temperature (from Landsat-8) were used, along with field data on water content and irrigation timing. The results showed that both NDVI and NDMI are highly sensitive to moisture, making them the best indices for determining the timing and location of irrigation. This research contributes to sustainable agricultural development. It has implications for farmers, policymakers, and researchers in optimizing irrigation schedules, developing policies for sustainable agriculture, and enhancing crop productivity while conserving water resources. This approach can be particularly useful in regions facing water scarcity, where the efficient use of water resources is crucial for sustainable agricultural development.

Background: Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. Methods: A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared. Results: The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement. Conclusions: MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.

Spectral indices-based soil prediction models derived from multispectral datasets are too intricate in terms of accuracy as well as resolution. Complications arise while incorporating multispectral datasets for regional-scale spatial assessment of soil macronutrients. Sporadically satellite image fusion techniques have been used for soil nutrient interpolation to circumvent the complications. The fusion of multispectral bands encompasses precise soil information that cannot be observed as accurate with single satellite dataset. In this study, fusion of near infrared regions of Landsat 8 Operational Land Imager and Sentinel 2 has been observed for its contribution on soil macronutrient assessments. Area-to-point regression Kriging (ATPRK) approach is followed in fusing the two satellite imagery and in situ soil spectral have used for the validation of the resultant. Comparative statistical analysis on Landsat 8 OLI band 5 (wavelength: 845-885 nm), Sentine-2 band 8,8A (wavelength: 785-900 nm) datasets and fused satellite bands provides R2 values of 0.8209, 0.8436, and 0.8763 respectively. Regression models y = (0.25006 ± 0.00754) + (0.0000313)x, y = (0.25252 ± 0.0062) + (0.0000810)x, and y = (0.23715 ± 0.0062) + (0.0001210)x for nitrogen, phosphorus, and potassium respectively aids for soil macronutrient interpolation and assessments. Computations reveals the ranges of nitrogen, phosphorus and potassium that floats from 48 to 295 kg/ha, 5.0 to 37 kg/ha, and 32 to 455 kg/ha in the study area. Fusion of satellite imagery by ATPRK approaches in soil macronutrient study at regional scale brings the novelty of the study.

Canada has more lakes than any other country, making comprehensive monitoring a huge challenge. As more and more satellite data become readily available, and as faster data processing systems make massive satellite data operations possible, new opportunities exist to use remote sensing to develop comprehensive assessments of water quality at very large spatial scales. In this study, we use a published empirical algorithm to estimate Secchi depth from Landsat 8 reflectance data in order to estimate water clarity in lakes across southern Canada. Combined with ancillary information on lake morphological, hydrological, and watershed geological and landuse characteristics, we were able to assess broad spatial patterns in water clarity for the first time. Ecological zones, underlying geological substrate, and lake depth had particularly strong influences on clarity across the whole country. Lakes in western mountain ecozones had significantly clearer waters than those in the prairies and plains, while lakes in sedimentary rock formations tended to have lower clarity than lakes in intrusive rock. Deep lakes were significantly clearer than shallow lakes over most of the country. Water clarity was also significantly influenced by human impact (urbanization, agriculture, and industry) in the watershed, with most lakes in high impact areas having low clarity or very low clarity. Finally, we used in situ measured data to help interpret the underlying optical water column constituents influencing clarity across Canada, and found that chlorophyll-a, total suspended solids, and color dissolved organic matter all had strong but varying underlying effects on water clarity across different ecozones. This research provides an important step towards further research on the relationship between water column optical properties and the health and vulnerability status of lakes across the country.

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.

In this paper, Yuqiao Reservoir is taken as the research object. The total suspended matter (TSM) produced by the economic development in the upper reaches of the reservoir and its surrounding areas has brought great ecological harm to the safe operation of the reservoir. Satellite remote sensing technology provides a good way to obtain the temporal and spatial variation of TSM in the study area. Two field surveys were carried out in the Yuqiao Reservoir, a total of 44 sampling points collected in the two tests. The spectral data and concentration of TSM were obtained. We developed and validated a robust empirical model to estimate the concentration of TSM in the water of the Yuqiao Reservoir for the first time. The TSM distribution map of the Yuqiao Reservoir in 2013-2018 is retrieved based on Landsat 8 OLI images. This paper analyzes the spatial distribution characteristics of TSM concentration in the Yuqiao Reservoir for several years, as well as the interannual, seasonal, and monthly variation laws and development trends. The results show that the spatial distribution of TSM in Yuqiao Reservoir shows a decreasing trend from the periphery to the center; the interannual changes are mainly as follows: The annual change trend of TSM in Yuqiao Reservoir is not obvious; the seasonal changes are significant: the highest in summer (higher than 40 mg/L), the second in autumn, and the lowest in spring and winter (lower than 15 mg/L); and the monthly changes show regular fluctuations: In a year cycle, the concentration of TSM generally shows an inverted V-shaped trend; that is, TSM increases gradually from January to August and decreases gradually from August to December. The research results of this paper can be applied to other similar types of land water bodies, which will promote the wide application of Landsat 8 OLI images in the monitoring of TSM in lakes, rivers, and reservoirs in different regions across China, and provide data support for the scientific management of the safe operation of research areas. PRACTITIONER POINTS: The monitoring model of TSM in Yuqiao Reservoir was built for the first time. Temporal and spatial analysis of TSM concentration in Yuqiao Reservoir for the first time. The concentration of TSM is in Yuqiao Reservoir greatly affected by wind speed and precipitation.

The widespread occurrence of Cyanobacterial blooms (CABs) in inland waters is a typical and severe challenge for water resources management and environment protection. An accurate and spatially continuous risk assessment of CABs is critical for prediction and preparedness in advance. In this study, a multivariate integrated risk assessment (MIRA) method of CABs in inland waters was proposed. MIRA was simplified with the trophic levels, cyanobacterial and other aquatic plant condition using remote sensing indexes, including the Trophic State Index (TSI), Floating Algae Index (FAI) and Cyanobacteria and Macrophytes Index (CMI). First, the dates of risk assessment were carefully selected based on TSI. Then, we obtained the trophic levels, cyanobacterial, and other aquatic plant condition of water using TSI, CMI and FAI on the selected date, and further scored them pixel by pixel to quantify the risk value. Finally, the risk of CABs in water was accurately assessed based on the pixel risk value. Based on Landsat 8 OLI dataset, MIRA was executed and validated in three different lakes of Wuhan urban agglomeration (WUA) with different trophic states. The results demonstrated that the risk of CABs in Lake LongGan was overall higher than that in Lake LiangZi and Lake FuTou. And the risk of CABs in the east part of Lake LongGan was higher than the other parts. Seasonally, the risk level ranking in Lake LiangZi was the highest in summer, while lowest in winter. However, the seasonal risk ranking was spring, summer, autumn, and winter in Lake LongGan. Based on the comparisons with monthly water quality classification data and results of the existing study, including trophic level, ecology risk, and algal extent, the MIRA method was valuable for accurate and spatially continuous identifying the risk of CABs in inland waters with potential eutrophication trends.

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

Lakes eutrophication have been a complex and serious problem for China\'s Yangtze River Basin. A series of algorithms based on different remote sensing dataset have been proposed to simulate the lakes trophic state. However, these algorithms are often targeted at a particular lake and cannot be applied to a watershed management. In this study, a Forel-Ule index (FUI) method based on Landsat 8 OLI image is proposed to simulate trophic state index (TSI) in three typical urban lakes (Dianchi, Donghu, and Chaohu) from 2013 to 2018. The results show that the Landsat 8 derived FUI can well represent the lake TSI with an accuracy of R2 = 0.6464 for the in situ experimental TSI dataset (N = 115) and R2 = 0.8065 for the lake average TSI dataset (N = 315). In the study period 2013-2018, the order of the simulated TSI is Dianchi > Chaohu > Donghu. Seasonal dynamics show differences where the percentage of eutrophic area in summer is significantly lower than the other seasons for Lake Dianchi and Chaohu. However, the percentage of eutrophic area for Lake Donghu is highest in summer and lowest in winter. To further detect the driving factors of eutrophication in study lakes, the Pearson correlation and multiple linear regression analyses were conducted. The results show that sunshine and temperature are, respectively, the most and the second most significant factors for Lake Dianchi with explanations of 14.8% and 22.0%; temperature and pollution are the main influencing factors for Lake Donghu (39.2% and 10.9% explanation, respectively) and Chaohu (57.2% and 60.7% explanations, respectively). In addition, the wind is another negatively significant factor for Lake Chaohu with an explanation of 31.3%. Our results serve as an example for other lakes in the Yangtze River Basin and support the formulation of effective strategies to reduce seasonal eutrophication.