April 22, 2024

Eyes in the Sky: A Beginner’s Guide to Satellite Remote Sensing (SRS) for Natural Assets

Eyes in the Sky: A Beginner’s Guide to Satellite Remote Sensing (SRS) for Natural Assets

In this blog post, we will explore the world of satellite remote sensing (SRS) for natural assets and help you understand the relationship between a satellite, its sensor or instrument, and the underlying data acquisition method.

Content Overview
  • What is remote sensing?
  • Satellite Remote Sensing (SRS) Taxonomy
  • A Satellite’s Energy Source: Passive vs. Active
  • Exploring Data Acquisition Methods
  • Satellites and their Sensors & Instruments
  • Utilising Remote Sensing for Assessing Natural Assets: Exemplary Datasets
  • Geospatial Analysis on Maya
What is remote sensing?

As defined by United States Geological Survey (USGS), ‘remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance’. Satellites are equipped with instruments or sensors designed to measure various types of radiation.

Satellite Remote Sensing Taxonomy
Credit: Maya Climate

The taxonomy starts by outlining broader, more generalised categories and principles of satellite remote sensing and then narrows down to specific examples of satellites and their equipment. Please note that the listing of satellites along with their sensors or instruments is not exhaustive – there are additional satellites that utilise the mentioned data acquisition methods.

1. A Satellite's Energy Source: Passive vs. Active 🌞

Satellites operate by using sensors to collect information about the Earth from space. They can use either passive or active sensing techniques to gather data. The basic difference between passive and active sensing is the source of radiation they use to acquire data: passive sensors rely on natural radiation that is reflected or emitted from the Earth’s surface or atmosphere, while active sensors emit their own signal to measure the Earth’s properties.

Credit: Maya Climate
2. Exploring Data Acquisition Methods 👀
2.1 Electromagnetic radiation 🌈

First of all, understanding how satellites work requires a basic knowledge of electromagnetic radiation: Think of electromagnetic radiation as a collection of waves that carry energy from one place to another. These waves can have different lengths and energy levels, and together, they make up what’s called the electromagnetic spectrum. 

For satellite remote sensing, visible, infrared, and microwave radiation is especially relevant. The Earth, for example, mostly emits infrared radiation: just beyond what our eyes can see, we feel these waves as heat. During the day, the Earth absorbs a lot of sunlight and heats up. During day and night, even without the Sun, the Earth releases this heat as infrared radiation. Satellites detect this radiation via their sensors or instruments and convert it into data that can be analysed for various applications.

Credit: Pettorelli et al., 2018

While we won’t delve further into the electromagnetic spectrum, the graphic will help you understand the terms used throughout the rest of this blog post.

2.2 Optical, Thermal, LiDAR and SAR Technologies ⚙️
Optical Imaging (Passive)

Uses visible, near-infrared, and shortwave infrared light from the sun, which is why optical satellite imagery is often best captured during daylight hours and under clear sky conditions. For the visible spectrum, you can imagine it like taking a photo of the Earth.

The process works as follows: 

  1. The sun emits light that travels to the Earth, covering a wide range of the electromagnetic spectrum, including visible, near-infrared, and shortwave infrared radiation.
  2. This sun strikes objects on the Earth’s surface.
  3. The objects absorb some of the sunlight and reflect the rest.
  4. The amount of light is captured by the optical sensor on the satellite.
  5. The sensors record the intensity of the light across different wavelengths, which is then used to create images and derive information about the surface features.
Thermal Infrared Sensing (Passive)

Measures the thermal radiation/heat emitted from Earth’s surface in the infrared spectrum. Every object emits some level of infrared energy, and this energy increases with temperature. The temperature data can be mapped to a grayscale or colour scale and combined to form an image. In these thermal images, warmer areas might appear brighter or in a different colour depending on the chosen colour scale.

Light Detection and Ranging, LiDAR (Active)

Illuminates the Earth’s surface with laser light (usually near-infrared light waves, invisible for the human eye) and then measures how long it takes for the light to return after reflecting off a surface. Distance to the object is determined by recording the time between transmitted and backscattered pulses and by using the speed of light to calculate the distance travelled.

Synthetic Aperture Radar, SAR (Active)

A form of radar technology that uses microwaves to create images of the Earth’s surface and to measure the Earth’s properties. SAR systems emit microwave pulses and then capture the reflected signals to determine the distance to objects. SAR imagery is produced by analysing both the intensity of the microwave pulse upon return and the time it takes for them to travel back to the satellite.

2.3 Limitations 👎
  • Optical technologies are significantly impacted by clouds, as they can block or scatter the sunlight that these sensors rely on to detect and measure reflected radiation from the Earth’s surface. Optical sensors do require daylight to work because they rely on sunlight to illuminate the Earth's surface.
  • Thermal sensing is impacted by clouds, as they can absorb and emit infrared radiation, which can affect the accuracy of the Earth’s surface temperature detection. It does not require daylight as an external source, as it detects emitted infrared radiation from the Earth’s surface, which occurs both day and night.
  • LiDAR is affected by clouds as well, as clouds can scatter or absorb the laser pulses, reducing the clarity and accuracy of the data obtained. LiDAR does not require daylight to work since it uses its own laser light source.
  • SAR uses microwave radiation, which can penetrate clouds, fog, and rain without significant scattering or absorption, making SAR an all-weather, day-and-night suitable technology. Thus, SAR is the only technology among these that can reliably acquire data regardless of cloud cover, which is an advantage for Earth observation in regions that are frequently cloud-covered, such as in tropical or boreal areas.
3./4. Satellites and their Sensors & Instruments 🛰️
Credit: NASA
  • Sentinel-2 is part of the European Union’s Copernicus program and consists of two satellites (2A & 2B), launched in 2015 and 2017, providing high-resolution optical imagery for land monitoring. Its Multispectral Instrument (MSI) captures data in various spectral bands within the visible, near-infrared, and shortwave infrared parts of the spectrum. (Note: No thermal infrared and thus only optical.)
  • Terra and Aqua, two satellites launched by NASA in 1999 and 2002, are equipped with the Moderate Resolution Imaging Spectroradiometers (MODIS). MODIS collects both optical and thermal data across a wide range of the electromagnetic spectrum, from visible, near-infrared, and shortwave infrared to thermal infrared.
  • Landsat 7 was launched in 1999 and is part of the Landsat program, a long-term collaboration between NASA and United States Geological Survey (USGS) which started with Landsat 1 in 1972. Its main sensor is the ETM+ (Enhanced Thematic Mapper Plus) that captures high-quality images across multiple spectral bands, from visible to thermal infrared.
  • Landsat 8, launched in 2013, and Landsat 9, launched in 2021, are the latest in the Landsat series, both equipped with two different sensors each for thermal and optical data collection. The Thermal Infrared Sensor (TIRS) is designed to measure land surface temperature, while the Operational Land Imager (OLI) captures data in multiple spectral bands for observing Earth’s surface. 
  • The International Space Station (ISS) is a habitable artificial satellite. It hosts a range of instruments that change over time to meet different research priorities. Among these, the Global Ecosystem Dynamics Investigation (GEDI) instrument was particularly noteworthy. Installed on the ISS from mid-2019 to mid-2021, GEDI was specifically designed to capture three-dimensional structures of the Earth’s surface, such as forests. GEDI uses light detection technology (LiDAR) via laser pulses – a form of light in the near-infrared range of the electromagnetic spectrum, thus not visible to the human eye. Note that all other satellites and instruments mentioned in this blog post are still active and continue to provide data as part of their ongoing missions.
  • Sentinel-1 is a two-satellite mission (1A & 1B) from the European Space Agency, launched in 2014 and 2016, providing all-weather, day-and-night radar imagery (SAR) for land and ocean services as part of the Copernicus program. C-SAR uses C-band microwave radiation, a specific segment within the broader microwave range, for its imaging capability.
  • The Advanced Land Observing Satellite (ALOS), launched by the Japan Aerospace Exploration Agency (JAXA) in 2006, carries several instruments, including the Phased Array type L-band Synthetic Aperture Radar (PALSAR). It operates in the L-band spectrum of the microwave range and can penetrate cloud cover and vegetation canopies, making it particularly useful for studying tropical forests, mangrove cover and monitoring deforestation.

And that’s how it all comes together!

Credit: Maya Climate
Utilising Remote Sensing for Assessing Natural Assets: Exemplary Datasets 🗺️

While all satellites share the common feature of analysing the Earth’s surface from above, they vary in their suitability for different use cases. However, the distinction between them can often be fluid, with one dataset frequently utilising various satellite images to cover a broader range of years.

As mentioned before, GEDI data, employing LiDAR technology, is especially useful for detailed 3D mapping of surfaces, even in dense fields such as forest canopies. Thus, GEDI data is utilised in commonly used Biomass and Canopy Height datasets.

While the Dynamic World dataset by Google and the World Resources Institute utilises Sentinel-2 imagery for its land cover analysis, the WorldCover dataset by the European Space Agency (ESA) incorporates both Sentinel-1 and Sentinel-2 data. By combining optical and radar data that provide different perspectives on the same area, WorldCover can improve the accuracy of land cover change detection. However, it’s important to note that the WorldCover dataset is currently only available for the years 2020 and 2021. 

The Normalised Difference Vegetation Index (NDVI) is a widely used index that can be calculated from a variety of satellite imagery sources. It’s calculated as follows:

On Maya, users can choose NDVI analysis based on data from Sentinel-1 and -2 or Landsat 7 and 8. Each of these satellites is suitable for NDVI because they all capture the necessary spectral bands – specifically, red and near-infrared light – that are essential for assessing plant health. However, the output from each satellite may differ due to variations in resolution, spectral band accuracy, and revisit frequency. For example, Landsat provides a longer temporal record (from 2000 onwards) and moderate resolution, which is ideal for historical analysis, whereas Sentinel-2 offers higher resolution and more frequent updates, making it better for monitoring rapid changes in vegetation.

Geospatial Analysis on Maya 🐝

At Maya Climate, we leverage the power of satellite remote sensing to provide automated geospatial analysis for your natural assets. Utilising a diverse range of publicly available datasets sourced from the latest satellite imagery, we equip you with the tools to effectively assess and monitor these assets. Even better: You don't need to be a GIS expert yourself to gain full insights from satellite imagery! If you want to learn more about the specific datasets we utilise and how they can benefit your processes, stay tuned for your next blog post or contact our team of experts directly.

Geospatial "Fun" Fact 🗑️

Did you know that over 35,000 objects are currently being tracked in Earth’s orbit, of which only about 25% are operational satellites?

The rest are space debris, also known as space junk – defunct satellites and parts of rockets that aren’t operating anymore. Another 130 million smaller debris pieces are estimated to be in Earth’s orbit, too tiny to be tracked. This debris can move at speeds of over 25,000 km/h, making any collision, even of the tiniest pieces, potentially catastrophic. If you are interested in learning more about this topic, you can read about it in the recent United Nations University’s Interconnected Disaster Risk 2023 report. 

Written by Delphine-Marie Zacharias 🧡