How do you manage water resources with remote sensing?

1 Remote sensing data sources

When it comes to water resources engineering, there are many types of remote sensing data sources to choose from, based on the desired spatial and temporal resolution, spectral range, and accuracy. Optical sensors capture visible and near-infrared light reflected by the earth's surface, providing information on vegetation, land cover, water bodies, and snow cover. Examples include Landsat, Sentinel-2, and MODIS. Thermal sensors measure infrared radiation emitted by the earth's surface, offering insight into surface temperature, evapotranspiration, and soil moisture. ASTER, Landsat, and Sentinel-3 are a few examples. Microwave sensors use radio waves to penetrate clouds, vegetation, and soil and can provide data on precipitation, soil moisture, surface water, and groundwater. TRMM, GPM, SMAP, and GRACE are some of these sensors. Radar sensors use radio waves to measure the distance and velocity of objects; they can provide information about surface water, flood extent, and topography. SRTM, Sentinel-1, and NISAR are a few examples of radar sensors.

2 Remote sensing data processing

Remote sensing data processing involves transforming the raw data collected by the sensors into meaningful information for water resources engineering. This may include pre-processing, which involves correcting errors, distortions, and noise caused by the sensor, the atmosphere, and the terrain. Classification assigns the data to different categories or classes based on their spectral, spatial, or temporal characteristics. Extraction extracts specific features or parameters from the data that are relevant for water resources engineering, such as vegetation indices, albedo, surface temperature, evapotranspiration, soil moisture, precipitation, surface water, groundwater, or water quality indicators. Finally, analysis examines the extracted features or parameters to understand their spatial and temporal patterns, trends, relationships, and impacts through statistical analysis, spatial analysis, temporal analysis, hydrological modeling, or water balance assessment.

3 Remote sensing data integration

Integrating remote sensing data is a process of combining data from different sources, scales, or domains to improve the information content and accuracy for water resources engineering. This could include fusing data from various sensors or platforms to create a new data set with higher spatial, spectral, or temporal resolution. Assimilation is another technique which incorporates the data into a hydrological model to update or constrain the model state, parameters, or outputs. Lastly, validation involves comparing the data with independent observations or measurements to evaluate the data quality, accuracy, or uncertainty. All of these techniques can be used to enhance the accuracy of remote sensing data for water resources engineering.

4 Remote sensing data application

Remote sensing data application involves using the data for water resources engineering purposes, such as planning, design, and management. This includes monitoring changes and variations of water resources over time and space, assessing the quantity and quality of water resources, and managing the use and allocation of water resources. Specifically, this may involve drought monitoring, flood monitoring, irrigation monitoring, water quality monitoring, water resources assessment, water scarcity assessment, water footprint assessment, water security assessment, water resources planning, water resources allocation, water resources optimization, or water resources governance.

5 Remote sensing data benefits

Remote sensing data can offer a wide range of benefits for water resources engineering, including wide coverage, high frequency, low cost, and multidimensional information. It can cover large areas and regions that are difficult or impossible to access by conventional methods, providing a synoptic and consistent view of water resources. Remote sensing data also offers frequent and timely information about water resources, allowing it to capture dynamic and transient phenomena such as floods, droughts, or storms. Additionally, remote sensing data can reduce the cost and effort of collecting and maintaining ground-based data, as well as provide data for areas where ground-based data are scarce or unreliable. Finally, it can provide information about water resources from different perspectives and dimensions such as spatial, spectral, temporal, and vertical.

6 Remote sensing data challenges

Remote sensing data can present some challenges for water resources engineering, such as data quality, availability, interpretation, and integration. Data quality can be impacted by errors, uncertainties, and biases caused by the sensor, atmosphere, terrain, or processing methods. Data availability can be limited by sensor characteristics, orbit configuration, cloud cover, or data policy. Data interpretation can be complex and ambiguous to interpret and analyze, requiring expert knowledge and skills. Data integration can be heterogeneous and incompatible with other sources, scales, or domains requiring advanced methods for data fusion, assimilation, or validation.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?