The application of satellite sensors, current state of utilization, and sources of remote sensing dataset in hydrology for water resource management

Using satellite data to observe hydrological processes is crucial for the sustainable management of water resources across a wide area. The process of remotely sensing water resources entails producing data on a variety of topics, including the routine inventory of surface water bodies and the evaluation of precipitation, soil moisture, evapotranspiration (ET), ground water, and snowmelt runoff (Singh 2018). These days, hydrological cycle components such as precipitation, evaporation, lake and river levels, surface water, soil moisture, snow, and total water storage may all be measured directly or indirectly using satellite-based sensors (McCabe et al. 2017). For precise and dependable data on Earth observations, satellite remote sensing (RS) is a valuable resource in atmospheric and environmental science. Hydrological modeling finds it attractive due its seamless availability throughout ungauged regions, boosting spatial and temporal resolution. Earth observation data have already proven to be immensely beneficial to the field of hydrology sciences (Tang et al. 2016; McCabe et al. 2017; Alfieri et al. 2020).

Quantifying the hydrological budget over large spatial domains and lengthy time periods by direct observation is relatively difficult because in situ observations are expensive and labor-intensive. This is particularly true in ungauged basins, where stream flow measurements are either nonexistent or insufficient, and little to no observations of spatially variable hydrological parameters are made. Hydrological modeling for water resource estimates is, therefore, quite difficult. Satellite RS provides a method to address these issues with broad spatial coverage and reproducible temporal coverage. Hydrological studies are hampered in ungauged basins because they do not have the calibration and validation data needed to employ land surface models. Therefore, it is necessary to make use of satellite data (Lakshmi 2018).

Currently, little is known about these satellite sensors in developing countries such as East Africa, particularly Ethiopia. Therefore, it has been an urgent issue to conduct a review of these satellite sensors and thereby work on the adoption of such technologies in universities and research institutions. Many research works have been done extensively on validating these satellite sensors. However, it is not designed to be easily understood by users. In addition, they focused on only a few satellite sensors. On the contrary, this review paper covers various satellites and sensors with their spatial and temporal resolutions, which are suitable for hydrological modeling, and gives valuable information on satellites and sensors for the modeling to academicians and researchers who are not familiar with satellite-derived data. It is much preferable to review all hydrologic response units and store them in a simple form on a single paper that paves the way for easier access to input data on water conservation, which is the ultimate goal of this article. On the other hand, many developing countries in the world do not have hydrology and water resource data centers at an acceptable distance. Meanwhile, these hydrological and water resource data are in high demand in relation to climate change. Obtaining data on hydrology and water resource parameters from existing gage stations in the required quantity and timeliness has been a constraint. So, where do we find these clues? From which website can we get it in the shortest time without spending money? Is it a question that awaits an answer? In this regard, this article is intended to play an important role in answering these questions in a concise manner.

In this research, the function of satellite RS in hydrology and water resource management is reviewed, along with its possible applications in the future. This review paper primarily focuses on the following two objectives: (1) to synthesize the scientific information on satellite RS applications for hydrology and water resources and (2) to explain the RS dataset sources for hydrological parameters.

Remote sensors are devices that receive and respond to a signal or stimulus and convert any type of energy into electrical energy (Al-Aubidy 2007). RS satellites are equipped with various sensors designed to capture different types of data about the Earth's surface, atmosphere, and other phenomena. These sensors gather information across the electromagnetic spectrum, ranging from visible light to microwaves and beyond.

Passive microwave sensors: These sensors measure microwave radiation emitted or scattered by the Earth's surface and atmosphere. They are particularly useful for estimating soil moisture and detecting precipitation. Examples include the Advanced Microwave Scanning Radiometer and the Soil Moisture and Ocean Salinity satellite.

Active microwave sensors: These sensors emit microwave pulses toward the Earth's surface and measure the reflected signal. They are used for measuring surface water extent, soil moisture, and snow cover. Synthetic-aperture radar sensors, such as those on the European Space Agency's (ESA) Sentinel-1 satellites, are examples of active microwave sensors.

Optical sensors: Optical sensors capture images of the Earth's surface using visible and infrared light. They are used for monitoring vegetation health, land cover changes, and snow cover extent. Examples include the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite.

LiDAR (Light Detection and Ranging): LiDAR sensors emit laser pulses and measure the time it takes for the pulses to return after reflecting off the Earth's surface. LiDAR data can be used to measure river and lake levels, as well as terrain elevation. The Ice, Cloud, and Land Elevation Satellite mission and the Global Ecosystem Dynamics Investigation aboard the International Space Station are examples of LiDAR missions.

Gravimetric sensors: Gravimetric sensors measure variations in the Earth's gravitational field, which can provide information on changes in water storage, such as groundwater depletion and changes in surface water storage. The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On, are examples of gravimetric missions.

Multispectral sensors: Multispectral sensors capture data in several discrete spectral bands within the visible and near-infrared (NIR) portions of the electromagnetic spectrum (Light 1990).

Hyperspectral sensors: These sensors measure a wide range of wavelengths across the electromagnetic spectrum, providing detailed information about the composition and properties of the Earth's surface. They are used for tasks such as identifying different types of vegetation and detecting water quality parameters. Examples include the Hyperspectral Infrared Imager and the Environmental Mapping and Analysis Program.

Satellite sensors used in hydrology are designed to capture various aspects of the Earth's surface and atmosphere relevant to the water cycle (Azarderakhsh et al. 2011). Several key aspects are essential for these sensors to effectively monitor and study hydrological processes:

Global coverage: Satellite sensors can observe remote and inaccessible regions by providing comprehensive coverage. This allows for monitoring water resources in diverse environments, including remote areas, mountainous regions, and polar regions, where ground-based observations may be limited or unavailable.

Cost and time-saving: Satellite sensors enable non-invasive RS of the Earth's surface and atmosphere, allowing for continuous monitoring of hydrological parameters over large spatial scales. This capability reduces the need for costly and time-consuming fieldwork, making it possible to assess water resources in areas with challenging terrain or limited accessibility.

Multi-sensor integration: Satellite missions often include multiple sensors with complementary capabilities, such as optical, microwave, and infrared sensors. Integrating data from different sensors allows for a more comprehensive understanding of hydrological processes, including the estimation of soil moisture, precipitation, ET, and surface water extent.

Large-scale monitoring: Satellite sensors can monitor large-scale hydrological phenomena, such as river basins, watersheds, and continental-scale water cycles. This ability to observe broad spatial extents facilitates the assessment of water availability, distribution, and movement across different regions, supporting regional water resource management and transboundary water management efforts.

Real-time and near real-time data: Many satellite missions provide real-time or near real-time data, allowing for timely monitoring and response to hydrological events such as floods, landslides, and water quality changes. Rapid access to satellite data supports early warning systems, emergency response planning, and disaster management activities, helping to mitigate the impacts of water-related hazards.

Long-term monitoring: Satellite sensors have enabled long-term monitoring of hydrological parameters, facilitating the analysis of historical trends and the detection of long-term changes in water resources. Long-term satellite datasets contribute to climate change studies, water resource planning, and ecosystem management by providing insights into the impacts of climate variability and human activities on water availability and quality.

Spatio-temporal resolution: Many satellite sensors offer high temporal resolution, capturing frequent observations of hydrological processes over time. This temporal continuity allows for monitoring changes in precipitation patterns, snowmelt dynamics, soil moisture levels, and water body dynamics, facilitating the detection of trends, seasonal variations, and extreme events, such as floods and droughts. Spatial resolution refers to the level of detail in an image captured by a satellite sensor. Higher spatial resolution sensors can distinguish smaller features on the Earth's surface, which are important for tasks such as monitoring small water bodies, river networks, and urban areas. Different hydrological applications may require sensors with varying spatial resolutions to meet specific monitoring needs. Temporal resolution refers to how frequently a satellite revisits the same location on Earth. Hydrological processes, such as precipitation, snowmelt, and vegetation growth, can exhibit rapid changes over time. Sensors with high temporal resolution can capture these dynamic processes more frequently, allowing for better monitoring and forecasting of hydrological events like floods and droughts.

Spectral bands: Satellite sensors are equipped with different spectral bands to capture electromagnetic radiation across the spectrum. Each spectral band is sensitive to specific features of the Earth's surface and atmosphere. For hydrological applications, sensors with bands sensitive to water-related parameters, such as NIR and microwave bands, are crucial for tasks like estimating soil moisture, detecting water bodies, and monitoring vegetation health.

Radiometric accuracy: Radiometric accuracy refers to the precision with which a satellite sensor measures the intensity of electromagnetic radiation. Accurate radiometric measurements are essential for quantifying surface properties related to hydrology, such as land surface temperature, vegetation indices (VIs), and water reflectance. Calibration and validation procedures are employed to ensure the radiometric accuracy of satellite data, enabling reliable hydrological analysis and modeling.

Data accessibility and availability: The accessibility and availability of satellite data are critical for operational hydrological monitoring and research. Open-access satellite missions, such as those provided by space agencies, such as NASA, ESA, and National Oceanic and Atmospheric Administration (NOAA), enable researchers and water resource managers to access a wealth of satellite data for various hydrological applications. In addition, data archives and distribution platforms facilitate easy access to historical and real-time satellite data for ongoing hydrological studies.

Integration with ground-based observations: Satellite sensors are often used in conjunction with ground-based observations, such as weather stations, stream gauges, and groundwater monitoring networks, to validate and supplement satellite-derived data. Integrating satellite observations with ground-based measurements improves the accuracy and reliability of hydrological models and enhances our understanding of local-scale processes.

By considering these important aspects, satellite sensors can effectively contribute to hydrological research, water resource management, and environmental monitoring efforts worldwide.

The use of satellite precipitation products (SPPs) in environmental applications has been limited by their accuracy and spatial resolution (Beyene 2023). While satellite sensors offer numerous advantages for hydrological monitoring, they also have some limitations and disadvantages.

Spatial resolution: Many satellite sensors have limited spatial resolution, particularly for freely available data. This can be a drawback when monitoring small-scale features such as small rivers, wetlands, or urban water bodies. Higher spatial resolution data may be available from commercial satellites, but often at a higher cost.

Temporal resolution: Sensors have varied revisit times to a specific location depending on their satellite's orbit and mission design (Barsi et al. 2019). For instance, Sentinel 2 has slow revisit times to the same location (in the order of 1 or 2 weeks). Images are often obstructed by clouds, which, together with slow revisit times, cause extended periods without any data (Guillermo et al. 2022).

Cloud cover and weather conditions: Satellite sensors reliant on optical imagery are hindered by cloud cover and adverse weather conditions, which can obscure the Earth's surface and limit data availability. Persistent cloud cover in tropical regions or during the rainy season can pose challenges for consistent monitoring of hydrological processes.

Atmospheric interference: Atmospheric conditions, such as aerosols, water vapor, and atmospheric scattering, can introduce errors in satellite-derived measurements, particularly in the case of optical sensors. Corrections for atmospheric interference are necessary to ensure the accuracy of hydrological data derived from satellite observations.

Data processing and interpretation: Processing satellite data requires specialized expertise and computational resources, including algorithms for image correction, calibration, and interpretation. In addition, integrating satellite data with ground-based observations and hydrological models can be complex and may introduce uncertainties in the analysis.

Cost: While some satellite data sources offer free or low-cost access to data, high-resolution or specialized satellite imagery may come at a significant cost, particularly for commercial data providers. This can be a barrier for researchers and organizations with limited budgets, especially in developing regions where funding for RS applications may be scarce.

Data validation and accuracy: Satellite-derived hydrological data require validation against ground-based observations to ensure accuracy and reliability. In situ measurements are essential for calibrating and validating satellite observations, but establishing and maintaining ground-based monitoring networks can be resource-intensive and challenging in remote or inaccessible areas.

Limited sensitivity to subsurface processes: Satellite sensors primarily observe surface water and moisture dynamics, but they have limited sensitivity to subsurface processes, such as groundwater recharge, aquifer depletion, and soil moisture at deeper depths. Integrating satellite observations with ground-based measurements and hydrological models is necessary for a comprehensive understanding of the water cycle.

The library research method, which includes (i) analyzing historical records (recording of notes and analysis) and (ii) analyzing documents (statistical compilations and manipulations, references, and abstract guides), was done. An extensive literature search was conducted. Secondary data that are relevant to the preset objectives were collected and compiled from recent literature.

Earth's precipitation is measured using multiple satellite sensors. These sensors use a variety of methods to monitor and quantify precipitation in various forms (rain, snow, sleet, etc.) in various parts of the world (Table 1).

In order to estimate the combined processes of water evaporation from the Earth's surface (such as water bodies, soil, and vegetation) and transpiration from plants, ET from space is measured using a variety of satellite sensors and techniques (Table 2).

Satellite sensors for monitoring vegetation health and dynamics gather information about plant health, growth, and environmental conditions using various wavelengths of light. Different sensors are used to measure certain elements of vegetation (Table 3). Satellite products of VIs have been widely used for various purposes, including vegetation change monitoring (Zhang et al. 2017; Zeng et al. 2020), vegetation phenology extraction (Buyantuyev & Wu 2012; Pastor-Guzman et al. 2018), terrestrial carbon circulation modeling (Tucker & Sellers 1986; Guan et al. 2019), dynamic environmental simulations (Tong et al. 2017; Zhao et al. 2020), and land coverage and change detection (Jia et al. 2014; Hu 2021). Among them, the normalized difference vegetation index (NDVI) calculated from the NIR band and the visible red band (RED) obtained by optical satellites is one of the most popular indices (Holben et al. 1980). Similar to the NDVI, the enhanced vegetation index (EVI) minimizes the canopy background variations and maintains its sensitivity under dense vegetation conditions. The EVI also uses the blue band (BLUE) to remove residual atmospheric contamination caused by smoke and thin sub-pixel clouds (Huete et al. 2002).

The ratio vegetation index is calculated by simply dividing the reflectance values of the NIR band by those of the red band. The result clearly captures the contrast between the red and infrared bands for vegetated pixels, with high index values being produced by combinations of low red (because of absorption by chlorophyll) and high infrared (as a result of leaf structure) reflectance. The perpendicular vegetation index (PVI) is the parent index from which the entire group of distance-based VIs is derived. The PVI uses the perpendicular distance from each pixel coordinate to the soil line. The main objective of the PVI is to cancel the effect of soil brightness in cases where vegetation is sparse and pixels contain a mixture of green vegetation and soil background.

Satellite sensors for measuring soil moisture (Table 4) are critical tools in environmental monitoring, agriculture, hydrology, and climate studies. These sensors work by sensing microwave radiation emitted or reflected by the Earth's surface.

Satellite sensors developed for surface temperature measurements (Table 5) work by absorbing thermal radiation released by the Earth's surface using various technologies and spectral bands. These sensors aid in the monitoring and analysis of temperature differences in landscapes, oceans, and cities.

Satellite sensors are critical for monitoring and quantifying water storage in a variety of environments, including oceans, lakes, rivers, and subsurface reservoirs. These sensors estimate water storage levels (Table 6) using various methodologies and wavelengths.

Several technologies and satellite-based sensors are used to measure and monitor surface water levels across oceans, lakes, rivers, and reservoirs (Table 7).

Various types of satellite sensors (Table 8) are used to detect and track surface water extent in oceans, lakes, rivers, and reservoirs.

Stream flow is monitored using a variety of satellite sensors and technology (Table 9). Satellite sensors play an important role in stream flow monitoring by providing vital data on water levels, surface water extent, and other characteristics.

Monitoring river discharge with satellite sensors entails determining the amount of water flowing through a river or stream at a specific spot (Table 10). While the direct measurement of discharge from space is difficult, data from multiple satellite sensors can be utilized to estimate discharge indirectly.

Each water budget component in Equation (1) has different temporal dynamics. For example, precipitation has faster dynamics than storage change. Irrespective of the different temporal dynamics of each flux, Equation (1) holds at any time interval. There are several products from previous and continuing satellite missions that measure these components at various time and space scales, either separately or as an aggregate. By combining microwave and infrared satellite observations, global precipitation is retrieved with very high spatial and temporal resolution (Sorooshian et al. 2000; Kummerow et al. 2001; Joyce et al. 2004; Huffman et al. 2007). Satellite sensors play an important role in monitoring various components of the water budget, which includes tracking the transport and distribution of water throughout the Earth's hydrological cycle (Table 11). They give statistics on various crucial characteristics that help to understand the water budget.

Groundwater monitoring is more difficult than monitoring surface water bodies such as rivers or lakes. Satellite sensors, on the other hand, play a role in indirectly assessing and analyzing groundwater by observing numerous surface indicators related to groundwater levels, land surface changes, and hydrological processes (Table 12).

Accuracy assessments determine the quality of the information derived from remotely sensed data (Congalton & Green 2009; Beyene 2023). Some correction methods (atmospheric correction, topographic correction, geometric correction, and radiometric correction) need to be applied for obtaining high-quality data. Atmospheric corrections are methods used to convert the radiance measured at the satellite to the outgoing radiance measured at the ground (Lu et al. 2002). It considers that selective scattering and absorption of light alter reflectance (Silleos et al. 2006). Radiometric correction takes into account sensor calibration, illumination, and view angle (Carlson & Ripley 1997). Geometric correction uses careful Ground Control Point selection, which is required for satellite images. Accurate and consistent georeferencing is especially important for automatic land cover change algorithms because change detection is performed by overlaying images from different sources (Russ 1995). Radiometric corrections include correcting the data for sensor irregularities and unwanted sensor or atmospheric noise and converting the data, so they accurately represent the reflected or emitted radiation measured by the sensor. Geometric corrections include correcting for geometric distortions due to sensor-Earth geometry variations and converting the data to real-world coordinates (e.g., latitude and longitude) on the Earth's surface (Jain 1989; Lillesand & Kiefer 1994).

In order to reduce the position errors, Hoffman & Grassotti (1996) proposed a feature correction and alignment technique (FCA), which used a variational approach to solve a nonlinear least squares estimation problem with side constraints to vary the displacement and amplification errors of the prior background field until usable observations were available (Hoffman & Grassotti 1996). Based on the FCA method, Grassotti fused radar and satellite precipitation estimates and performed the ideal experiment for Typhoon Andrew, using the precipitation observed by radar to adjust the precipitation estimates from Special Sensor Microwave/Imager (SSM/I). The adjusted SSM/I precipitation estimates can better fit radar observations and satisfy other constraints (Grassotti et al. 1999).

Systematic errors and random errors are common in satellite rainfall products (Goshime 2020). Error sources are mostly related to the imperfection of the retrieval algorithm, data source, and postprocessing procedures (Dubovik et al. 2021; Zhang et al. 2021). Compared to some meteorological variables like temperature, which has a steadier geographical and temporal pattern, bias correction of satellite rainfall data is thought to be the most difficult (Soo et al. 2020). From the available methods, distribution mapping tends to address bias by correlating patterns of different rainfall magnitudes (Valdés-Pineda et al. 2016; Katiraie-Boroujerdy et al. 2020).

Many previous studies have shown that the assimilation of hydrological parameter data from satellite sensor products and gauges could complement each other and achieve good performance with sufficient ground observations (Tan & Yang 2020; Zhou et al. 2021, 2022). Tian & Peters-Lidard (2010) corrected the CMORPH and TRMM data by reducing the error by 47–63% based on rain gauges in the USA. Stisen & Sandholt ( 2010) improved hydrological simulation efficiency by correcting the SPPs in the Senegal River Basin in West Africa. Zhou et al. (2021, 2022) employed statistical and dynamic bias correction to correct Global Satellite Mapping of Precipitation (GSMaP) and Global Precipitation Measurement (GPM) series data in the Fuji River Basin, Japan. The results showed that the corrected SPPs significantly improved and benefited the efficiency of runoff simulation. Recently, Liu et al. (2023) addressed a three-step bias correction method incorporating the statistic and dynamic bias correction method, the cumulative distribution function matching method and the inverse error variance weighting method for SPPs by correcting the bias of SPPs in regions with limited gauge data, which poses a significant challenge, especially when aiming for reliable precipitation data for hydrological simulations, particularly in near-real-time scenarios.

The use of satellite sensors and RS data in hydrology is critically important to the water and health sectors. It enhances the ability to monitor, manage, and protect water resources, thereby ensuring the provision of safe drinking water and aiding in the prevention of waterborne diseases and the challenges posed by climate change. The continuous advancement and utilization of these technologies are essential for promoting public health and achieving sustainable water resource management.

Hydrological observations and modeling utilizing satellite data are critical for the long-term management of water resources across wide areas. Water resource RS entails gathering data ranging from a regular inventory of surface water bodies to an assessment of rainfall, soil moisture, ET, groundwater, and snow melt runoff (Singh 2018). Satellite RS is increasingly being utilized as a supplement to in-person monitoring networks, and in many situations, it is the only viable source. Satellite-based sensors can now measure nearly all components of the hydrological cycle, both directly and indirectly (Flechtner et al. 2015; Lettenmaier et al. 2015). These include precipitation, evaporation, lake and river levels, surface water, soil moisture, snow, and total water storage (surface and subsurface water). As a result, these sensors are capable of providing crucial information in support of water management and monitoring the evolution of hazards and their repercussions (Van Dijk & Renzullo 2011).

The authors recommend satellite sensors for several advantages in monitoring hydrologic parameters, providing a wealth of information that can be invaluable for understanding and managing water resources as follows:

• Wide coverage and frequent monitoring: Satellites can cover large and remote areas, including regions that are otherwise inaccessible or difficult to monitor regularly. This capability allows for the comprehensive and consistent monitoring of hydrological parameters over vast areas, such as entire watersheds or river basins. Satellites can monitor hydrological parameters globally, making them valuable for studying water resources on a worldwide scale and facilitating international collaboration and understanding of water-related issues. Satellites provide frequent and repetitive observations over time. This allows for the assessment of temporal changes in hydrological conditions, enabling the detection of trends, seasonal variations, and sudden changes such as floods or droughts.

• Multispectral and multi-temporal data: Satellite sensors can capture data in various spectral bands, including visible, infrared, and microwave wavelengths. This multispectral data allow for the analysis of different hydrological parameters, such as soil moisture, ET, snow cover, and surface water bodies.

• Consistency and standardization: Satellite data are consistent and standardized, providing a continuous record that facilitates comparison and analysis over different spatial and temporal scales. This consistency is crucial for assessing long-term trends and changes in hydrological parameters.

• Integration with geographic information systems (GISs): Satellite data can be easily integrated with GIS technology, allowing for the creation of spatially explicit maps and models. This integration enables better decision-making in water resource management, land use planning, and disaster risk reduction.

• Timely and rapid response: Satellites can provide near-real-time data, enabling rapid response to hydrological events such as floods or droughts. This information is valuable for early warning systems and emergency management.

• Cost-effectiveness: While there are initial costs associated with satellite systems, they can be cost-effective in the long run compared to traditional ground-based monitoring, especially in remote or inaccessible regions where establishing and maintaining ground stations might be challenging.

• Monitoring water quality and quantity: Satellite sensors play a crucial role in monitoring various water quality parameters, such as turbidity, chlorophyll concentration, and harmful algal blooms. By providing continuous and large-scale data, RS helps detect and track pollution sources, monitor the extent of contamination, and assess the overall health of water bodies. This information is vital for ensuring safe drinking water and managing water resources effectively.

• Early warning systems for water-related health hazards: RS datasets are essential for developing early warning systems for water-related health hazards such as floods and likely associated waterborne disease outbreaks. Satellite data can predict extreme weather events and their impact on water resources, allowing for timely interventions to mitigate health risks. For instance, flood prediction can lead to the evacuation of at-risk populations and exposures to contaminated water.

• Addressing climate change impacts on water resources: Climate change poses significant challenges to water availability and quality, affecting public health. Satellite sensors offer insights into changing precipitation patterns, melting glaciers, and shifting river flows. Understanding these changes helps in developing adaptive strategies to ensure reliable water supplies and mitigate health impacts related to water scarcity and quality degradation.

• Supporting research and policy development: The integration of satellite sensor data into hydrology and water resource management research supports evidence-based policy-making. It enables researchers and policymakers to analyze trends, evaluate the effectiveness of interventions, and make informed decisions to protect public health. This data-driven approach fosters the development of robust water management policies that safeguard communities against water-related health issues.

In summary, satellite sensors offer a powerful and versatile tool for monitoring hydrological parameters, providing valuable data that support water resource management, environmental conservation, disaster mitigation, mitigating health impacts related to water scarcity, and scientific research. Therefore, it is advisable to use RS data for sparsely gauged stations for water resource assessment and management at all levels.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.