13.1 Remote sensing principles and data sources
5 min read•july 30, 2024
Remote sensing revolutionizes hydrology by providing a bird's-eye view of water resources. It uses electromagnetic radiation to gather data on water bodies, soil moisture, and land cover changes without physical contact. This tech is a game-changer for tracking water movement and availability across vast areas.
Satellites, aircraft, and ground sensors collect data that feed into hydrological models. These tools help scientists map water bodies, estimate soil moisture, and predict runoff. While remote sensing offers unparalleled coverage, it still needs ground truthing to ensure accuracy in hydrological applications.
Remote sensing principles in hydrology
Fundamentals of remote sensing
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- Remote sensing acquires information about an object or phenomenon without physical contact, using electromagnetic radiation
- Electromagnetic radiation interacts with the Earth's surface and atmosphere through absorption, transmission, reflection, and scattering, forming the basis for remote sensing techniques
- Spectral signatures of different Earth surface features and materials enable their identification and characterization using remote sensing data (vegetation, water, soil)
- Passive remote sensing systems detect natural energy reflected or emitted from the observed scene (sunlight, thermal radiation)
- Active remote sensing systems emit energy and detect the radiation reflected or backscattered from the target (radar, )
Applications of remote sensing in hydrology
- Remote sensing applications in hydrology include:
- Mapping and monitoring water bodies (lakes, rivers, wetlands)
- Monitoring snow and ice cover (glaciers, snowpack)
- Estimating soil moisture content (surface and root-zone)
- Measuring evapotranspiration rates (water loss from land surface to atmosphere)
- Detecting land use/land cover changes (urbanization, deforestation, agricultural expansion)
- Remotely sensed data serves as input for hydrological models, such as:
- Estimating precipitation amounts and distribution (rainfall, snowfall)
- Predicting runoff and streamflow (surface water hydrology)
- Assessing groundwater recharge and storage (subsurface hydrology)
- Modeling water and energy balance components (evapotranspiration, soil moisture, snow water equivalent)
Remote sensing data sources
Satellite-based remote sensing platforms
- satellites provide moderate (15-100 m) and temporal resolution (16 days), suitable for long-term hydrological studies (land cover change, water resource management)
- offers daily global coverage at coarse spatial resolution (250-1000 m), useful for large-scale hydrological monitoring (drought, flood, snow cover)
- Sentinel missions, such as Sentinel-1 (SAR) and Sentinel-2 (multispectral), provide high spatial resolution (10-60 m) and frequent revisit times (5-12 days), beneficial for detailed hydrological mapping (water bodies, soil moisture, vegetation)
- Precipitation radar systems, like TRMM and GPM, measure global precipitation at various spatial and temporal scales, essential for hydrological modeling and forecasting
- Gravimetric satellites, such as GRACE and GRACE-FO, estimate changes in water storage, including groundwater and surface water, based on Earth's gravity field variations
Airborne and ground-based remote sensing techniques
- Airborne remote sensing platforms, like unmanned aerial vehicles (UAVs) and manned aircraft, acquire high-resolution imagery for localized hydrological studies (river morphology, wetland mapping, precision agriculture)
- Ground-based remote sensing techniques, such as radar and lidar, provide high-resolution data for detailed hydrological characterization and validation of satellite-based observations (soil moisture, snow depth, vegetation structure)
- Ground-based sensors and networks, including weather stations, streamflow gauges, and soil moisture probes, offer in-situ measurements for calibrating and validating remotely sensed hydrological variables
Remote sensing techniques for hydrology
Optical and thermal remote sensing
- Optical remote sensing is suitable for mapping and monitoring surface water extent, snow and ice cover, and land use/land cover changes
- Visible and near-infrared bands are sensitive to water's spectral signature, enabling water body delineation
- Shortwave infrared bands help distinguish snow and ice from clouds and other bright surfaces
- Multispectral and hyperspectral data allow for detailed land cover classification and change detection
- Thermal infrared remote sensing is useful for estimating evapotranspiration and detecting groundwater discharge
- Land surface temperature (LST) derived from thermal imagery is a key input for evapotranspiration models (energy balance, vegetation index)
- Temperature anomalies in surface water bodies can indicate groundwater upwelling or cold-water refugia
Microwave and gravimetric remote sensing
- Microwave remote sensing, particularly synthetic aperture radar (SAR), provides information on soil moisture, water level, and flood extent
- SAR backscatter is sensitive to soil moisture content, allowing for estimation of surface and root-zone moisture
- SAR interferometry (InSAR) can measure water level changes in wetlands, rivers, and reservoirs
- SAR polarimetry helps distinguish flooded vegetation from dry land and open water
- Gravimetric remote sensing estimates changes in water storage at regional to global scales
- Temporal variations in Earth's gravity field measured by GRACE and GRACE-FO reflect changes in water mass distribution
- Gravimetric data can quantify groundwater depletion, ice sheet and glacier mass loss, and large-scale hydrological fluxes (precipitation, evapotranspiration, runoff)
Advantages vs limitations of remote sensing in hydrology
Advantages of remote sensing in hydrological modeling
- Remote sensing provides spatially continuous and temporally consistent data over large areas, improving model parameterization and calibration
- offers wall-to-wall coverage of hydrological variables (precipitation, evapotranspiration, soil moisture)
- Long-term remote sensing archives enable analysis of hydrological trends and variability (climate change, land use impacts)
- Remote sensing captures the spatial heterogeneity of hydrological processes, which is difficult to achieve with ground-based measurements alone
- High-resolution remote sensing data resolves fine-scale variations in hydrological properties (soil texture, vegetation cover, topography)
- Spatially distributed hydrological models benefit from remote sensing inputs that represent the landscape's diversity
- Remote sensing enables near-real-time monitoring and forecasting of hydrological conditions, particularly in data-scarce regions or during extreme events
- Satellite-based precipitation estimates (GPM, TRMM) support flood forecasting and early warning systems
- Soil moisture and snow cover data from remote sensing improve and water supply forecasts
Limitations and challenges of remote sensing in hydrological modeling
- Uncertainties associated with the accuracy and precision of remotely sensed data can propagate into hydrological model outputs
- Retrieval algorithms for hydrological variables (soil moisture, evapotranspiration) rely on assumptions and simplifications that introduce errors
- , cloud masking, and data quality control are critical for ensuring the reliability of remote sensing inputs
- Differences in spatial and temporal resolutions between remote sensing data and hydrological models may require data resampling or aggregation
- Hydrological models often operate at finer spatial scales (hillslope, catchment) than satellite-based observations (pixel, grid)
- of remote sensing data (days to weeks) may not match the time steps of hydrological models (hours to days)
- The need for ground-based data for calibration and validation of remote sensing-based hydrological estimates
- In-situ measurements of hydrological variables (streamflow, soil moisture, snow depth) are essential for assessing the accuracy of remote sensing retrievals
- Field campaigns and sensor networks are required to establish the relationship between remote sensing observables and hydrological quantities
- Assimilating remote sensing data into hydrological models requires careful consideration of the data characteristics, model structure, and assimilation techniques
- The choice of data assimilation method (direct insertion, Kalman filter, particle filter) depends on the model complexity and data properties
- Bias correction and error characterization of remote sensing data are necessary for effective assimilation
- Adequate computational resources and expertise are needed to implement remote sensing data assimilation in hydrological modeling frameworks
Key Terms to Review (18)
Atmospheric correction: Atmospheric correction refers to the process of removing atmospheric effects from remotely sensed data to obtain accurate surface reflectance values. This technique is essential in remote sensing because atmospheric conditions, such as scattering and absorption, can distort the data collected by sensors, leading to inaccuracies in interpreting land cover and environmental conditions. By applying atmospheric correction, the integrity of remote sensing data is enhanced, allowing for more reliable analysis and decision-making in various applications.
Drought monitoring: Drought monitoring is the process of assessing and tracking the severity and duration of drought conditions using various data sources and techniques. It involves analyzing climatic, hydrologic, and vegetation data to evaluate water availability and identify potential impacts on ecosystems, agriculture, and water resources. By utilizing remote sensing technologies and integrating them with geographic information systems (GIS), effective drought monitoring provides timely information for decision-making and management strategies.
Electromagnetic spectrum: The electromagnetic spectrum is the range of all types of electromagnetic radiation, which includes visible light, radio waves, infrared radiation, ultraviolet light, X-rays, and gamma rays. Each type of radiation has a different wavelength and frequency, influencing how it interacts with matter. This spectrum is crucial for remote sensing as it allows the detection and interpretation of various environmental features through the analysis of emitted or reflected energy.
Flood mapping: Flood mapping is the process of creating detailed visual representations of flood-prone areas, which helps in understanding the extent and depth of potential flooding events. These maps are crucial for risk assessment, emergency management, and urban planning, providing valuable information to authorities and communities to mitigate flood risks and improve preparedness.
Geospatial Data Abstraction Library (GDAL): GDAL is an open-source software library that provides tools for reading, writing, and manipulating geospatial raster and vector data formats. It serves as a vital bridge between different types of geospatial data, making it essential for handling diverse datasets in remote sensing applications. By enabling access to a wide array of formats and providing powerful processing capabilities, GDAL supports various workflows in remote sensing and geospatial analysis.
Image classification: Image classification is the process of assigning a label or category to an image based on its content. This technique is crucial in remote sensing as it helps in interpreting data collected from various sensors, allowing for the identification and analysis of land cover types, vegetation, and urban areas. By analyzing spectral information from images, different land features can be categorized, leading to valuable insights for environmental monitoring and resource management.
Landsat: Landsat is a series of Earth-observing satellite missions jointly managed by NASA and the U.S. Geological Survey, designed to collect detailed images and data about the Earth's surface. These satellites have been providing consistent, high-resolution imagery since 1972, making them essential for monitoring land use, natural resources, and environmental changes over time. Landsat data plays a crucial role in remote sensing and serves as a vital tool for integrating with geographic information systems (GIS) to enhance hydrological modeling.
Lidar: Lidar, which stands for Light Detection and Ranging, is a remote sensing technology that uses laser light to measure distances and create precise, three-dimensional information about the Earth's surface. This technique is crucial for gathering high-resolution data that can be used in various applications, including mapping, surveying, and environmental monitoring. Lidar integrates well with GIS, enhancing spatial analysis and supporting detailed hydrological modeling through accurate terrain representation.
MODIS: MODIS, or the Moderate Resolution Imaging Spectroradiometer, is a key instrument aboard NASA's Terra and Aqua satellites that captures data across multiple spectral bands to monitor and analyze various Earth processes. It plays a crucial role in remote sensing by providing valuable information on land cover, vegetation, temperature, and other environmental variables that are vital for hydrological modeling. MODIS enhances the understanding of hydrological cycles by offering frequent and consistent observations of Earth's surface.
Normalized difference vegetation index (ndvi): The normalized difference vegetation index (NDVI) is a remote sensing measurement used to assess the health and density of vegetation. It is calculated using the difference between near-infrared and red light reflectance, helping to identify vegetation cover and monitor changes in plant health over time. NDVI values range from -1 to 1, where higher values indicate healthier, denser vegetation, while lower values can signify sparse or stressed vegetation.
Normalized difference water index (ndwi): The normalized difference water index (NDWI) is a remote sensing measurement used to assess water content in vegetation and to identify water bodies. By comparing the reflectance of green light (typically from the visible spectrum) and near-infrared light, NDWI helps distinguish between water-covered areas and land, which is crucial for monitoring changes in water resources, vegetation health, and environmental conditions.
Open Geospatial Consortium (OGC): The Open Geospatial Consortium (OGC) is an international organization that fosters collaboration among different industries and organizations to improve the accessibility and interoperability of geospatial data and services. The OGC develops standards that enable the integration of diverse data sources, including remote sensing technologies, to facilitate better decision-making and enhance the utilization of geospatial information in various applications.
Radiometric resolution: Radiometric resolution refers to the ability of a sensor to detect and differentiate between varying levels of energy reflected from surfaces, essentially measuring the sensitivity of the sensor to incoming radiation. This sensitivity determines how finely a sensor can record the intensity of electromagnetic energy, impacting the quality of the data collected in remote sensing applications. Higher radiometric resolution means the sensor can capture more subtle variations in energy levels, allowing for better discrimination between different surface materials and conditions.
Satellite imagery: Satellite imagery refers to the images of the Earth's surface captured by satellites orbiting the planet. These images are essential for observing and analyzing various environmental processes, including precipitation, land use changes, and geographical features, making them invaluable tools in scientific research and resource management.
Spatial Resolution: Spatial resolution refers to the smallest unit of area that can be resolved or represented in a dataset, determining the level of detail or granularity captured in spatial data. A higher spatial resolution indicates more detail, allowing for finer distinctions between features on the Earth's surface, while a lower spatial resolution captures broader, less detailed representations. This concept is crucial in various measurement techniques, remote sensing applications, and geographic information systems as it influences the accuracy and usability of spatial data.
Spectral analysis: Spectral analysis is a technique used to analyze the frequency components of signals, often through the study of their spectra. In the context of remote sensing, it helps in interpreting data from various electromagnetic wavelengths, allowing scientists to identify and quantify different materials and processes on Earth's surface by examining how they interact with different wavelengths of light.
Surface reflectance: Surface reflectance is a measure of how much sunlight is reflected off the surface of the Earth, typically expressed as a percentage or fraction of incoming solar radiation. This concept is crucial for interpreting remote sensing data, as it provides insight into the characteristics of various land surfaces, such as vegetation, water, and urban areas, by quantifying the amount of light that is reflected back to sensors.
Temporal frequency: Temporal frequency refers to the rate at which data is collected or observed over time, typically expressed in terms of events per unit of time. This concept is crucial in understanding how frequently remote sensing data can capture changes in the environment, enabling effective monitoring and analysis of various phenomena such as land use changes, vegetation growth, and hydrological patterns.