Remote-sensing, a technological innovation of geography science
Earth science, also known as geoscience, is the systematic study of the Earth's structure, processes, and history. This subject explores the physical composition, chemical dynamics, and biological systems of the Earth. It involves phenomena ranging from plate tectonics and resource distribution to natural disasters such as earthquakes and volcanic eruptions. Remote sensing, as an emerging technology, has formed a close and complementary collaborative relationship. Often referred to as the "eye of the sky" of Earth science, remote sensing provides multi-scale, multi- temporal, and multi-spectral observation capabilities. In turn, Earth science lends remote sensing data geoscientific significance, driving its transformation from "data" to "knowledge." Through high-quality data, remote sensing promotes earth science research from local to global and from qualitative to quantitative. This article will explain the technological innovation of remote sensing and describe three applications of this technology: natural hazards analysis, mineral exploration, and monitoring water quality. Finally, the article will evaluate the benefits and limitations of this revolutionary innovation.
The development of remote sensing technology has a long history, which reflects continuous technological innovation and breakthroughs. Rebecca (2019) points out that since Nadal photographed Paris with a hot air balloon in 1858, remote sensing has undergone a transformation from simple carriers such as pigeons and kites to systematic aerial photography. The First World War gave birth to military aerial photography, while the Cold War promoted the development of high-altitude reconnaissance platforms and advanced sensors, allowing remote sensing to achieve a leap from tactical reconnaissance to global observation. Arthur (2019) mentions that 1978 was a significant year in terms of satellite data sources for different applications because several new satellites with advanced instruments appeared. These satellites introduced important sensors for the first time, and the launch of Landsat 3 in the same year further expanded the land remote sensing data source. At that time, polar-orbiting satellites and geosynchronous satellites had established a mature data source for meteorological applications. In the late 20th century, artificial satellites such as Landsat and Nimbus enabled remote sensing to enter a multi-band, all-weather, and global stage. Subsequently, innovations in hyperspectral sensors, high-resolution imaging, and digital image processing have greatly improved our ability to identify objects and monitor the environment. In the 21st century, remote sensing technology has been deeply integrated with GIS, big data，and artificial intelligence. With the help of drones，satellites such as Sentinel , and cloud platforms like Google Earth Engine，remote sensing has achieved more efficient 、intelligent 、and extensive applications，becoming an important tool to support global decision-making. Modern remote sensing relies on advanced satellite platforms (such as Landsat and Sentinel) and UAV systems, equipped with multiple types of sensors, achieving high-resolution imaging of multiple bands such as visible light, infrared, microwave, radar, etc. It not only can obtain multispectral and hyperspectral data but also supports multi-source information such as radar (SAR) and laser radar (LiDAR). At the same time, the temporal and spatial resolution of modern remote sensing has been significantly improved, enabling daily and even higher-frequency dynamic monitoring. These advances mark that modern remote sensing has transformed from early static image acquisition to an intelligent, dynamic, and integrated means of Earth observation.
The operational framework of remote sensing is transforming selectromagnetic energy interactions into actionable geospatial intelligence through integrated sensor- to-analysis systems. In terms of Earth date(n.d.), simply put, remote sensing is to "see" things from a distance and collect information. How does NASA study the Earth and other planets? They use satellites, spacecrafts，and airplanes in space— these carriers are equipped with special "eyes" (remote sensing equipment) that can capture energy signals reflected or emitted by targets. According to Geogyan (n.d.), first, sunlight (or other energy sources) hits a target area (such as a piece of land). Objects on the ground (such as water, vegetation, and soil) reflect, absorb, or re-emit this energy. Then, sensors installed on satellites, drones, or aircraft are responsible for capturing this reflected or released energy and turning it into digital data. Finally, this data is transmitted back to the ground station and processed by computers into maps or pictures that we can understand. In the end, scientists can use this data for interpretation and analysis in many applications. After processing—classification— and visualization，or combined with other platforms or technologies，the collected data can become a key tool for managing and understanding the Earth system.
After a long period of development, remote sensing has gradually become a mature technology. Today, remote sensing is well-developed in many countries with their own guidelines and maintenance but similar structures. In the next section, three applications of modern remote sensing will be given and analysed.
The first application of remote sensing pertains to water quality monitoring, which encompasses several key aspects: the estimation or simulation of water quality parameters using spectral information; the identification of remote sensing technologies and products applicable for water quality assessment; and the integration of suitable machine learning techniques (Shashank et al. 2025). According to Mohit et al. (2024), primary factors influencing water quality include algae proliferation, turbidity levels, dissolved organic matter content, specific chemicals, pathogens presence, aquatic vegetation growth, and oil contamination. These variables alter the energy spectrum of radiation reflected from or emitted by the surfaces of ponds, lakes, and rivers. Such radiations are subsequently measured utilizing various satellite sensors. The accurate quantification of water quality parameters from spectral data necessitates robust algorithmic frameworks, primarily categorized into empirical and analytical models. Empirical models (EMs) employ statistical regression to correlate in-situ measured parameters with optimal spectral band combinations, enabling rapid large-scale assessment. For instance, Zhang (2022) leveraged Sentinel-2 imagery to derive chlorophyll-a (Chl-a) concentrations across 3,067 global lakes (2019-2021), revealing a positive correlation with surface water temperature. Conversely, analytical models (AMs) simulate light propagation through atmosphere-water systems using bio-optical theory and radiative transfer equations, formally describing the relationship between water constituents and subsurface radiance. This physics-based approach underpins mechanistic interpretation of inherent optical properties (IOPs) linked to water quality dynamics. (Desong et al, 2022)
Remote sensing technology has important theoretical and practical value in mineral resource exploration and can effectively identify geological features such as rocks and minerals, as well as simultaneously identify mining areas. This essay will describe the features of remote sensing in mineral exploration from two aspects: spectroscopic analysis of surface materials and mapping hydrothermal alteration zones. The first is the application of remote sensing in spectral analysis of minerals. Different minerals, hydrothermal zones, etc., have different spectral response mechanisms. This unique reflection pattern is the "spectral fingerprint" of the mineral. Gregory (2023) demonstrates that the spectral characteristics and curves of different types of rocks and their mineral content can distinguish the types of minerals in those rocks. For example, Sima Peyghambari and Yun Zhang (2021) state that hydroxyl-containing minerals show obvious spectral absorption in the short-wave infrared radiation region, while sulfates containing OH have characteristic spectral features in this same region. The second point is how remote sensing is used to map hydrothermal alteration zones. Hydrothermal alteration zones—geochemical halos formed by mineralizing fluid-rock interactions—serve as critical exploration proxies. Finding the alteration zone is like finding the "footprints" or "signposts" of mineral deposits because rocks in these zones contain specific mineral combinations that will show regular colour anomalies on remote sensing images. Remote sensing generates a "mineral anomaly map" by focusing on analysing sensitive bands related to these minerals. For example, Pour used multispectral remote sensing images to explore copper-gold mineralization in northeastern Greenland's Inglefield Mobile Belt at regional, local, and detailed scales. They successfully mapped hydrothermal alteration minerals such as iron oxide/iron hydroxide, silicate (Si-OH), and SiO2 mineral groups while discovering several high-potential areas for Cu-Au exploration. (Admin et al. 2021)
The third application of remote sensing—— landslide research generally uses multi- source remote sensing (such as radar, satellite, drone, etc.) to acquire data, and then evaluates key quantitative indicators. First, landslide monitoring commonly uses a combination of these three types of remote sensing: Optical remote sensing uses high-resolution images to identify surface cracks, vegetation anomalies, water changes, and other landslide precursors; radar remote sensing uses microwave penetration to monitor surface deformation (with an accuracy up to millimeter level), which is especially suitable for cloudy and rainy areas; and LiDAR obtains high-precision digital elevation models (DEM) through drones or airborne platforms, penetrating vegetation cover to reveal hidden landslide boundaries and terrain changes. Then data processing and feature extraction can be performed. Second, come to the part of quantitative indicators for assessing hazards. Huiyan et al. (2024) notes that by comparing images of remote sensing over time, changes in vegetation, surface cracks, or movement patterns can be detected, which may indicate the occurrence of landslides. DEM differences (LiDAR or photogrammetry) can indicate volume changes; digital elevation Models taken before and after a landslide show volume changes, landslide depth, and material flow. NDVI (Normalized Difference Vegetation Index) declines in vegetation health (from satellite imagery) can be an early sign of slope instability or a recent landslide. Martinez et al. (2021) conducted a study to map landslides triggered by the 2018 Anchorage earthquake in Alaska, USA. The researchers combined field data with high-resolution satellite imagery to identify landslide features. After determining the location of the landslide, they used multiple remote sensing techniques to define the back edge of the landslide landform. Then they evaluate the effectiveness of three remote sensing techniques: LiDAR elevation difference (DEM), Normalized Difference Vegetation Index (NDVI) difference, and Synthetic Aperture Radar (SAR) amplitude change detection.
The application of remote sensing technology in the fields of geological disaster analysis and resource exploration has shown significant value, but a comprehensive evaluation of its effectiveness requires an objective examination of the technical characteristics.
Remote sensing offers significant benefits over traditional survey methods by providing large-scale, real-time data that supports critical applications across many fields. Firstly, Lisa Barr discusses that the benefits of remote sensing lie in its global view and large-scale coverage. Traditional ground surveys are often limited by time, cost, and accessibility, making it difficult to assess large areas. Remote sensing platforms such as satellites and aircraft can collect data from entire continents at once, giving us a comprehensive understanding of the Earth. This is particularly important for monitoring large-scale phenomena such as deforestation, melting glaciers, and urban expansion. Taking the monitoring and governance decision support of red tide disasters in the Yangtze River Estuary as an example, geostationary satellites obtain images of the East China Sea region every 10 minutes, and multispectral satellites complete global coverage every 2 days, and can even accurately extract chlorophyll concentration and water temperature. The staff used 5-day remote sensing time-series images to discover the area of red tide and the precise spread area, as well as the displacement of the polluted area. Additionally, the database can compare images at intervals of five years from 1990 to 2020 (showing time series evolution). Finally, it has driven the country to promulgate the "Yangtze River Protection Law" and contribute to flood control efforts in the Yangtze River Estuary. (Lusan et al. 2013)
Second, another important advantage of remote sensing technology is its dynamic response capability. The authoritative technology platform. Tpointtech proposes that modern remote sensing systems have efficient data collection and transmission capabilities and can support disaster assessment decisions from seconds to hours (near real-time). When responding to sudden environmental crises such as hurricanes, forest fires, and offshore oil spills, the command centre can use high- frequency remote sensing data streams (such as satellite images and drone surveillance videos) to accurately identify key parameters such as the outbreak point, impact range, and degree of damage of the incident. This real-time environmental situation awareness capability significantly improves the capabilities of front-line rescue forces in task allocation, resource scheduling, and operational efficiency; thereby greatly improving the overall efficiency of the disaster response chain. Take Hurricane Katrina in 2005: NASA utilized Light Detection and Ranging (LiDAR) technology to map flood damage in New Orleans. This approach allowed for rapid assessment of water levels and infrastructure damage over a vast area, expediting recovery efforts (Jeff, 2006).
Despite its many benefits, Admin(n.d.) argues that remote sensing has several limitations. High costs associated with data acquisition, processing, and equipment maintenance can restrict its use, especially in developing regions. Accurate interpretation also requires specialized skills, which may not be widely available. Additionally, factors such as cloud cover, haze, and seasonal variations can reduce data quality and accuracy. Limited spatial and spectral resolution in some sensors further constrains the level of detail obtainable, affecting the reliability of results in certain applications. On the other hand, Abudlmohsen(2024) further points out that remote sensing systems based on satellites and manned aerial platforms are significantly limited in their efficiency when managing local construction projects with nonlinear characteristics—such as residential buildings, commercial buildings，and industrial facilities—due to their low temporal resolution. The smaller the monitoring target is，the higher the accuracy required for image registration. Low temporal resolution has a substantial negative impact on the analysis of different types of civil engineering problems.
In summary, remote sensing has revolutionized earth science by providing unprecedented multi-scale observation capabilities, enabling the transition from fragmented data to actionable geoscientific knowledge. Its evolution—from aerial photography to AI-integrated satellite constellations—has unlocked transformative applications: quantifying global water quality parameters via spectral analysis, identifying mineral deposits through hydrothermal alteration mapping, and enabling millimeter-scale landslide monitoring. While its global perspective and real-time disaster response offer unparalleled advantages, limitations persist in spatial- temporal resolution, atmospheric interference, and technical accessibility.
Nevertheless, by merging with complementary technologies like GIS and machine learning, remote sensing remains indispensable for understanding Earth systems and informing sustainable resource management and hazard mitigation strategies worldwide.