13.1 Remote sensing principles and data sources

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, lidar)

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

• Landsat satellites provide moderate spatial resolution (15-100 m) and temporal resolution (16 days), suitable for long-term hydrological studies (land cover change, water resource management)
• MODIS 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
  • Satellite imagery 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 drought monitoring 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
  • Atmospheric correction, 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)
  • Temporal frequency 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