Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods.

https://www.mdpi.com/2073-4441/14/7/1148/pdf?version=1649315107

Satellite Detection of Surface Water Extent: A Review of Methodology

In this study, a data-driven approach employed by utilizing the product called JRC-Global surface water mapping layers V1.4 on the Google Earth Engine (GEE) to map and monitor the effects of climate change on surface water resources. Key climatic variables affecting water bodies, including air temperature (AT), actual evapotranspiration (ETa), and total precipitation, were analyzed from 2000 to 2021 using the temperature-vegetation index (TVX) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The findings demonstrate a clear association between global warming and the shrinking of surface water resources in the LUB. According to the results, an increase in AT corresponded to a decrease in water surface area, highlighting the significant influence of AT and ETa on controlling the water surface in the LUB (partial rho of - 0.65 and - 0.68, respectively). Conversely, no significant relationship was found with precipitation and water surface area (partial rho of + 0.25). Notably, the results of the study indicate that over the past four decades, approximately 40% of the water bodies in the LUB remained permanent. This suggests a loss of around 30% of the permanent water resources, which have transitioned into seasonal water bodies, accounting for nearly 13% of the total. This research provides a comprehensive framework for monitoring surface water resource variations and assessing the impact of climate change on water resources. It aids in the development of sustainable water management strategies and plans, supporting the preservation and effective use of water resources. 

Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine

• UAV-acquired overlapping images were applicable to obtaining water depth map of clear tufa lakes. • Single bathymetric methods caused depth anomalies in water areas with uniform texture or shadows. • Photogrammetric and spectral-based bathymetric methods were combined using neural network (NN). • NN-based bathymetric methods realized fully-covered and non-contact bathymetry of tufa lakes. • Shallow NN model achieved highest bathymetric accuracy while deep NN model added more details. Accurate and updated bathymetric data is of great significance for the management and protection of alpine tufa lakes. In recent years, unmanned aerial vehicle (UAV)-borne optical remote sensing has become a cost-effective technique for obtaining water depth of small and clear waters like tufa lakes. UAV-based bathymetry can be categorized into photogrammetric approach and spectrally derived approach. Photogrammetric bathymetry is contactless but invalid in water areas with uniform texture, while spectral-based bathymetry requires a large amount of in-situ depth measurements. In this paper, we combined the strengths of the two bathymetric methods to retrieve the depth of clear tufa lakes using neural networks. The surface elevation and orthoimage were first produced from UAV-acquired overlapping images, and then water color-depth tie points were sampled in the orthoimage and refraction-corrected bathymetric map. Next, the shallow and deep neural networks were separately used to train the regression models for predicting water depth. Lastly, the combined bathymetric methods were compared with the single ones in terms of effective spatial coverage and bathymetry accuracy. The results indicated that the combined methods were superior to single bathymetric methods in fully-covered bathymetry of clear tufa lakes. The shallow neural network-based model achieved the highest accuracy, with the coefficient of determination (R 2 ) of 0.91 and the Root Mean Square Error (RMSE) of 1.12 m, whereas the deep neural network-based model increased the details of water depth distribution. 

Fully-covered bathymetry of clear tufa lakes using UAV-acquired overlapping images and neural networks

The interaction between a lake and groundwater has important implications to the quantity and quality of water in both environments. Quantification of lake-groundwater interaction (LGI) has been challenging in regions with limited in-situ data. LGI can be quantified by physically-based models, direct measurement of seepage, measurements of conservative chemical or isotopic tracers, and lake water balance. Despite the accuracy of the methods based on hydrochemical or isotopic measurements and analysis, they require extensive field data that are costly to collect in large lakes. Instead, the data required to quantify LGI by the lake water budget method can be obtained via typical ground measurements and satellite remote sensing. However, the influence on the estimated LGI imposed by the uncertainty associated with the other components of lake water budget (e.g., precipitation, evaporation, and inflow/outflow) is still poorly understood. In this study, we coupled a forward uncertainty propagation framework with the lake water budget to estimate the uncertainty of LGI using Latin-Hypercube sampling. We implemented the above framework in the hypersaline Lake Urmia (LU), located in the northwestern Iran, over five consecutive study periods between September 2017 and May 2020. The results revealed the exchange of flow from groundwater to the LU in all studied periods, ranging between 0.2 million cubic meters (MCM)/day and 7.6 MCM/day. The relative contribution of LGI in the water budget varied between 3% and 37%. The results of the uncertainty analysis further demonstrated that the cumulative probability of LGI < 0, which indicates flow direction from the LU to groundwater, was insignificant in all the study periods ranging between 0.001 and 0.35. 

Quantifying the uncertainty of lake-groundwater interaction using the forward uncertainty propagation framework: The case of Lake Urmia

Coupled water and salt balance models for Lake Urmia: Salt precipitation and dissolution effects

River flow forecasting is important in reservoir operation and water allocation to different uses such as irrigation. In this paper, a river flow forecasting model is developed based on Principal Component Regression (PCR) analysis using some earth data such as precipitation, river discharge, snow water equivalent, climate signals as well as snow cover area extracted from NOAA-AVHRR satellite images. Satellite data were related to period of 1989 to 2004 and were downloaded via Satellite Active Archive (SAR) and about 3-4 images per month were processed to determine SCA. To distinct snow from land, albedo data of channel 2, temperature data of channel 4, and channel 3 data were used, and to separate snow and cloud, channel 3 and channel 4 data were applied. Furthermore, to separate composite pixels of 2 or 3 phenomena, a linear mixing model was developed. The proposed model was applied to one of the Zayande-rood river inflow tributaries to the Zayande-rood River in Iran. In the proposed model, river flow is forecasted for three periods in a year. In the first period at the end of January, river flow volume is forecasted for the period of February -June using observed data. Furthermore, volume of March-July (second period) and April-July (third period) is forecasted at the end of February and March, respectively. The results show that extracted SCA from satellite images is an effective parameter that improves the accuracy ofriver flow forecasting models. KEY TERMS: Forecasting, Principal component regression, Snow Cover Area, Zayande-rood

Using satellite data to improve the accuracy of river flow forecasting models a case study of zayande-rood river basin, iran

Revisiting bathymetry dynamics in Lake Urmia using extensive field data and high-resolution satellite imagery

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Using satellite data to improve the accuracy of river flow forecasting models a case study of zayande-rood river basin, iran

Revisiting bathymetry dynamics in Lake Urmia using extensive field data and high-resolution satellite imagery