Learn how to compare the spatial and spectral resolution of Landsat, Sentinel, and MODIS sensors for remote sensing applications. Find examples and resources to access and process the data.

La definicion de resolución espacial es incorrecta. Que un sensor tenga una resolución espacial de 10 metros no significa que "los objetos estén al menos a 10 metros de distancia". Es una reseña erronea. El satélite Copernicus Sentinel-2 tiene 10 metros en el espectro visible y no está a 10 metros de distancia de la Tierra sino a más de 780 kilometros de altitud. Que la resolución sea de 10 metros implica que el tamaño mínimo del pixel es de 10 metros y, por tanto, la unidad mínima que puede representar es de 10 metros. De igual forma, la resolución espacial del satélite Landsat (por defecto) tampoco es de 15 metros sino de 30 metros. Para poder convertir a 15 metros es necesario recurrir a una banda pancromática.

Sentinel-2 by European Space Agency (ESA), has a total of 13 Spectral Bands having a Spatial Resolution of 10m for 4 Bands, 20m for 6 Bands, and 60m for 3 Bands. Landsat-8 by NASA, has a total of 11 Spectral Bands (OLI + TIRS), having a Spatial Resolution of 15m for 1 Panchromatic Band, 30m for 8 Bands (15m and 30m Bands belongs to Operational Land Imager - OLI), and 100m for 2 Bands belonging to Thermal Infrared Sensor (TIRS). MODIS Satellite by NASA, has a total of 36 Spectral Bands, having a Spatial Resolution of 250m for 2 Bands, 500m for 5 Bands, and 1000m for the remaining 29 Bands. In terms of Spatial Resolution, less is better making Sentinel-2 the best Satellite Sensor out of the three in terms of Spatial Resolution.

Landsat sensors offer high spatial resolution (30m), suitable for detailed land analysis, with moderate spectral resolution. Sentinel satellites provide high spatial (10-60m) and spectral resolution, ideal for diverse applications. MODIS sensors have lower spatial resolution (250m-1km) but high spectral resolution, covering broad areas for global monitoring.

Sentinel-2 by European Space Agency (ESA), has a total of 13 Spectral Bands ranging between a Wavelength of 0.443 to 2.202 µm. Landsat-8 by NASA, has a total of 11 Spectral Bands (OLI + TIRS), ranging between a Wavelength of 0.43 to 12.51 µm. MODIS Satellite by NASA, has a total of 36 Spectral Bands ranging between a Wavelength of 0.405 to 14.385 µm. In terms of Spectral Resolution, more is better making MODIS the best Satellite Sensor out of the three. Having said that, there are other limitations in MODIS Satellite Sensor in which the other two Sensors are relatively better.

Landsat satellites provide moderate spatial resolution, typically ranging from 15 to 30 meters per pixel, allowing vast area observation of Earth's surface features. Sentinel missions offer spatial resolutions varying from 10 to 60 meters, providing a balance between spatial detail and coverage area. MODIS prioritizes broad coverage with a spatial resolution ranging from 250 to 1000 meters, suitable for monitoring large-scale environmental phenomena. However, MODIS shines in its spectral resolution, featuring 36 spectral bands covering a wide range of wavelengths from visible to thermal infrared regions, enabling comprehensive analysis of Earth's surface properties and atmospheric conditions.

Landsat vs Sentinel vs MODIS: A Comparison Guide

1 Spatial resolution

Spatial resolution is the size of the smallest feature that a sensor can detect on the ground. It is usually measured in meters or kilometers per pixel. For example, a sensor with a spatial resolution of 10 meters can distinguish objects that are at least 10 meters apart. A higher spatial resolution means more detail and clarity, but also more data and storage. Landsat sensors have a spatial resolution of 15 to 60 meters, depending on the band. Sentinel sensors have a spatial resolution of 10 to 60 meters, depending on the band and the mode. MODIS sensors have a spatial resolution of 250 to 1000 meters, depending on the band.

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Roberto Matellanes Ferreras

Copernicus dataviz🛰️
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La definicion de resolución espacial es incorrecta. Que un sensor tenga una resolución espacial de 10 metros no significa que "los objetos estén al menos a 10 metros de distancia". Es una reseña erronea. El satélite Copernicus Sentinel-2 tiene 10 metros en el espectro visible y no está a 10 metros de distancia de la Tierra sino a más de 780 kilometros de altitud. Que la resolución sea de 10 metros implica que el tamaño mínimo del pixel es de 10 metros y, por tanto, la unidad mínima que puede representar es de 10 metros. De igual forma, la resolución espacial del satélite Landsat (por defecto) tampoco es de 15 metros sino de 30 metros. Para poder convertir a 15 metros es necesario recurrir a una banda pancromática.

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Akhil Chhibber 🇮🇳 🇳🇱

| Machine Learning | MLOps | Remote Sensing | LLM | Computer Vision | LLMOps | GeoInformatics | Ex-Assistant Director, DDA | Ex-Space4Good | RAG | Urban Planning | Dutch HSM Recipient | Cloud Migration | 7k+ Followers |
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Sentinel-2 by European Space Agency (ESA), has a total of 13 Spectral Bands having a Spatial Resolution of 10m for 4 Bands, 20m for 6 Bands, and 60m for 3 Bands. Landsat-8 by NASA, has a total of 11 Spectral Bands (OLI + TIRS), having a Spatial Resolution of 15m for 1 Panchromatic Band, 30m for 8 Bands (15m and 30m Bands belongs to Operational Land Imager - OLI), and 100m for 2 Bands belonging to Thermal Infrared Sensor (TIRS). MODIS Satellite by NASA, has a total of 36 Spectral Bands, having a Spatial Resolution of 250m for 2 Bands, 500m for 5 Bands, and 1000m for the remaining 29 Bands. In terms of Spatial Resolution, less is better making Sentinel-2 the best Satellite Sensor out of the three in terms of Spatial Resolution.

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Syeda Urooj Fatima

Environment | Sustainability | RS | GIS | Climate Change | Forests | Oceans | Carbon | ESG | GHG | Data Visualization | Data Science | Research | Training
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Landsat sensors offer high spatial resolution (30m), suitable for detailed land analysis, with moderate spectral resolution. Sentinel satellites provide high spatial (10-60m) and spectral resolution, ideal for diverse applications. MODIS sensors have lower spatial resolution (250m-1km) but high spectral resolution, covering broad areas for global monitoring.

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Allan ROSS 🏞️🛰️⚡

Field Data Quality Engineer 🗺️ @ TotalEnergies OneTech SE 🛢️⚡🔋| R&D Geophysicist | Innovator | Neurodiversity | Fractalist 🪸
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When comparing the spatial and spectral resolution of Landsat, Sentinel, and MODIS sensors, several key differences emerge. Sentinel-2 offers the highest spatial resolution (up to 10m), capturing detailed imagery. Landsat has up to 30m resolution (15m for panchromatic), and MODIS has up to 250m. MODIS leads in spectral resolution with 36 bands, while Sentinel-2 and Landsat-8 have 13 and 11 bands, respectively. Sentinel-2 excels in band placement for vegetation analysis. The choice of sensor depends on the application: Sentinel-2 for detailed mapping, Landsat for long-term studies, and MODIS for broad-scale monitoring.

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Dario Alejandro Avila Briceño

Geoscientist | LATAM Account Executive at Planet | Using Space to help life on Earth
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La resolución espacial es un factor crucial al elegir el sensor adecuado para monitorear una área específica. Si estoy analizando extensas áreas donde el tamaño del píxel no es tan relevante, lo más común es utilizar MODIS o Landsat. Estos sensores permiten analizar grandes áreas y, dado que las imágenes tienen un menor peso, facilitan el procesamiento de datos en regiones muy amplias. Sin embargo, cuando necesito analizar áreas más pequeñas con mayor detalle y precisión, la mejor opción suele ser Sentinel-2. Si cuento con un presupuesto más amplio y requiero aún mayor definición, podría optar por imágenes de PlanetScope, que ofrecen una resolución de 3.7 metros por píxel.

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2 Spectral resolution

Spectral resolution is the number and width of the spectral bands that a sensor can record. Each band corresponds to a range of wavelengths of the electromagnetic spectrum, such as visible light, infrared, or microwave. Different bands can reveal different information about the surface features, vegetation, water, or atmosphere. A higher spectral resolution means more bands and more diversity, but also more complexity and noise. Landsat sensors have 8 to 11 bands, covering the visible, near-infrared, shortwave infrared, and thermal infrared regions. Sentinel sensors have 13 to 25 bands, covering the visible, near-infrared, shortwave infrared, and microwave regions. MODIS sensors have 36 bands, covering the visible, near-infrared, shortwave infrared, thermal infrared, and microwave regions.

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Akhil Chhibber 🇮🇳 🇳🇱

| Machine Learning | MLOps | Remote Sensing | LLM | Computer Vision | LLMOps | GeoInformatics | Ex-Assistant Director, DDA | Ex-Space4Good | RAG | Urban Planning | Dutch HSM Recipient | Cloud Migration | 7k+ Followers |
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Sentinel-2 by European Space Agency (ESA), has a total of 13 Spectral Bands ranging between a Wavelength of 0.443 to 2.202 µm. Landsat-8 by NASA, has a total of 11 Spectral Bands (OLI + TIRS), ranging between a Wavelength of 0.43 to 12.51 µm. MODIS Satellite by NASA, has a total of 36 Spectral Bands ranging between a Wavelength of 0.405 to 14.385 µm. In terms of Spectral Resolution, more is better making MODIS the best Satellite Sensor out of the three. Having said that, there are other limitations in MODIS Satellite Sensor in which the other two Sensors are relatively better.

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Dario Alejandro Avila Briceño

Geoscientist | LATAM Account Executive at Planet | Using Space to help life on Earth
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La resolución espectral es un concepto más complejo de entender, ya que no se refiere a la nitidez de una imagen o al tamaño del píxel, sino al rango del espectro visible o invisible que cubre el sensor. Una mayor resolución espectral significa que estamos cubriendo una mayor parte del espectro electromagnético, y cada rango proporciona información crucial según lo que se quiera analizar. Todos los sensores se complementan entre sí. Por ejemplo, MODIS ofrece 36 bandas espectrales, pero el tamaño del píxel es muy grande y no proporciona mucho detalle espacial. En cambio, Landsat tiene un tamaño de píxel más pequeño, lo que mejora la resolución espacial, aunque a costa de tener menos bandas espectrales.

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3 Trade-offs and applications

When comparing the spatial and spectral resolution of different sensors, you need to consider the trade-offs and the applications. Generally, there is a trade-off between spatial and spectral resolution: a sensor with a high spatial resolution usually has a low spectral resolution, and vice versa. This is because of the limitations of the sensor design, the data transmission, and the storage capacity. You also need to think about what kind of information you want to extract from the images, and what scale and frequency you need. For example, if you want to map land cover or land use at a regional or global scale, you might prefer a sensor with a low spatial resolution but a high spectral resolution, such as MODIS. If you want to monitor changes or anomalies at a local scale, you might prefer a sensor with a high spatial resolution but a low spectral resolution, such as Landsat. If you want to combine different types of information or detect features that are not visible in other bands, you might prefer a sensor with a moderate spatial resolution but a high spectral resolution, such as Sentinel.

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Dario Alejandro Avila Briceño

Geoscientist | LATAM Account Executive at Planet | Using Space to help life on Earth
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Siempre es bueno tener varios sensores dependiendo el estudio que quiera realizar. No obstante, al usar varios sensores de manera simultanea y buscando un dato especifico, puedo usar un método conocido como Downscalling que me ayuda a mejorar la resolución espacial de los datos

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4 Data access and processing

Another factor that you need to compare when choosing a sensor is the data access and processing. Different sensors have different policies and platforms for data distribution and availability. You need to check if the data are free, open, and easy to download and use. You also need to check if the data are pre-processed or raw, and what kind of tools and software you need to process them. For example, Landsat data are freely available from the USGS EarthExplorer website, and they are pre-processed to correct for geometric and radiometric distortions. Sentinel data are freely available from the Copernicus Open Access Hub website, and they are pre-processed to correct for geometric distortions. MODIS data are freely available from the NASA Earthdata website, and they are raw or partially processed, depending on the product.

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Dario Alejandro Avila Briceño

Geoscientist | LATAM Account Executive at Planet | Using Space to help life on Earth
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Una de las grandes limitantes a la hora de utilizar imágenes satelitales tanto de Landsat, sentinel y MODIS es precisamente lo que se menciona, tener en cuenta el acceso a la información es muy importante. Si se que las imágenes que descargaré ya están ortorectificadas y corregidas atmosfericamente, definitivamente eso me ahorrara mucho tiempo. Asimismo, si hay una API que pueda utilizar para automatizar las descargas y por ejemplo enviar toda la información que necesito a un cloud, también me facilitará el post procesamiento de la información

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5 Examples and resources

To help you compare the spatial and spectral resolution of Landsat, Sentinel, and MODIS sensors, here are some examples and resources that you can explore. You can use the Google Earth Engine platform to access and visualize the data from different sensors, and apply various algorithms and functions to analyze them. You can also use the Spectral Discovery Tool to compare the spectral characteristics and response curves of different sensors. You can also consult the official websites and documents of each sensor to learn more about their specifications, capabilities, and applications.

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Dr. Ekta Baranwal

Ph.D. (Remote Sensing and GIS) | ISRS & ISPRS Member | Top GIS Voice on LinkedIn | Researcher | Educator | Consultant | Author
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Landsat satellites provide moderate spatial resolution, typically ranging from 15 to 30 meters per pixel, allowing vast area observation of Earth's surface features. Sentinel missions offer spatial resolutions varying from 10 to 60 meters, providing a balance between spatial detail and coverage area. MODIS prioritizes broad coverage with a spatial resolution ranging from 250 to 1000 meters, suitable for monitoring large-scale environmental phenomena. However, MODIS shines in its spectral resolution, featuring 36 spectral bands covering a wide range of wavelengths from visible to thermal infrared regions, enabling comprehensive analysis of Earth's surface properties and atmospheric conditions.

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Akhil Chhibber 🇮🇳 🇳🇱

| Machine Learning | MLOps | Remote Sensing | LLM | Computer Vision | LLMOps | GeoInformatics | Ex-Assistant Director, DDA | Ex-Space4Good | RAG | Urban Planning | Dutch HSM Recipient | Cloud Migration | 7k+ Followers |
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Sentinel-2 by European Space Agency (ESA), has a Temporal Resolution of 5 days. It consists of two Satellites including Sentinel-2A and Sentinel-2B. Each Satellite has a revisit time of 10 days, but because they operate in-sync, together they provide a revisit time of 5 days. Landsat-8 by NASA, has a Temporal Resolution of 16 days, which means that it revisits a given point on the Surface of Earth every 16 days. MODIS Satellite by NASA, has a Temporal Resolution of 2 Days. This means it can provide Global Coverage of the Surface of Earth every 2 days. MODIS has the best Temporal Resolution out of the three Satellites. But, there are other limitations in MODIS Satellite Sensor in which the other two Sensors are relatively better.

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John Metzger

CaaS / Earth Monitoring (EM) and Geomatics / New Business Program Development
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The Sensors have their own "sensorality" -- characteristics, and like outputs may seem similar. Using each at it's native resolution is the place to start And, using them all in context is extremely enlightening ...

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Dario Alejandro Avila Briceño

Geoscientist | LATAM Account Executive at Planet | Using Space to help life on Earth
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Un ejemplo de plataforma para procesamiento de imágenes aparte de GEE, sería Sentinel Hub. Dentro de Sentinel hub puedo procesar imágenes de Landsat y Sentinel, hacer combinaciones de bandas a mi gusto con los custom scripts y además, de ser necesario puedo incorporar datos de Planet para procesar en la nube sin tener que descargar ningún raster de manera local

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How do you compare the spatial and spectral resolution of Landsat, Sentinel, and MODIS sensors?

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1 Spatial resolution

2 Spectral resolution

3 Trade-offs and applications

4 Data access and processing

5 Examples and resources

Remote Sensing

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