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Introduction to Remote Sensing
Published in Caiyun Zhang, Multi-sensor System Applications in the Everglades Ecosystem, 2020
Passive multispectral sensors collect images using visible, infrared, and/or thermal wavelengths with several bands. Sensors can be mounted on aircraft to get high-resolution aerial photography, on Unmanned Aerial vehicles (UAVs) or drones to get very high-resolution digital imagery or on spacecraft to collect satellite imagery. The unique feature of multispectral sensors is that they collect data using a few spectral channels, resulting in a coarse spectral resolution of data. Thus, multispectral instruments are also known as broadband sensors. The spectral resolution is the number of bands used by a sensor. It describes the ability of a sensor to define wavelength intervals. The finer the spectral resolution, the narrower the wavelength range for a specific channel or band. For example, the sensor of Landsat 8 Operational Land Imager (OLI) has 9 bands collecting data over visible, infrared, and thermal spectral channels. In this section, multispectral data (from aircraft, drone, and spacecraft platforms) are provided in more detail.
Remote Sensing Sensors and Platforms
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
One of the world's best-known families of remote sensing satellites is Landsat, which is operated by the USA and has evolved over the past 40 years. Landsat 1, which was also called the “Earth Resources Technology Satellite” until 1975, is the first satellite in the Landsat family and was launched in 1972, dedicated to periodic environmental monitoring. The Landsat 1 satellite had two sensors, called the Return Beam Vidicon (RBV) and the Multispectral Scanner (MSS). The MSS sensor was designed to capture images in the red, blue, and green spectra at 60 m resampled resolution over four separate spectral bands between 500 and 1,100 nm. The other two successive satellites (Landsat 2-3), were launched in 1975 and 1978, respectively. The same sensors were deployed onboard the Landsat 2, while the spectral capability of the MSS sensor on Landsat 3 was extended to measure radiation between 1,050 and 1,240 nm. Following the success of Landsat 1-3, the Landsat 4 was launched in 1982 with improved spectral and spatial resolution. The RBV was replaced with the thematic mapper (TM) sensor, providing seven bands from 450 to 2,350 nm with 30-m resolution pixels. In addition, the revisiting time of the satellite was improved from 18 days to 16 days. Launched in 1985, Landsat 5 is a duplicate of Landsat 4, and its TM sensor remains active 25 years beyond its designated lifetime. The next two satellites (Landsat 6-7) were launched in 1993 and 1999, respectively. However, Landsat 6 did not reach its orbit due to launch failure. These two satellites were equipped with the Panchromatic (PAN), Enhanced Thematic Mapper (ETM), and Enhanced Thematic Mapper Plus (ETM+) sensors, providing a spatial resolution of 15-m panchromatic and 30-m multispectral images. In addition, the latest generation Landsat satellite, Landsat 8, was launched in 2013 with a two-sensor payload, including the Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TIRS). Landsat 8 OLI and TIRS images are comprised of nine spectral bands with a spatial resolution of 30 m for bands 1 to 7 and 9 (Table 3.1) (Barsi et al., 2014). The ultra-blue band 1 is useful for coastal and aerosol studies and band 9 is useful for cirrus cloud detection (Barsi et al., 2014). Thermal bands 10 and 11 are useful for providing more accurate surface temperatures and are collected at 100 m (Barsi et al., 2014).
A new approach to LST modeling and normalization under clear-sky conditions based on a local optimization strategy
Published in International Journal of Digital Earth, 2022
Majid Kiavarz, Mohammad Karimi Firozjaei, Seyed Kazem Alavipanah, Quazi K. Hassan, Yoann Malbéteau, Si-Bo Duan
The Landsat 8 satellite was launched on 11 February 2013, which has two main sensors, namely Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The OLI sensor collects images using nine spectral bands at different wavelengths of visible, near-infrared, and shortwave with a resolution of 30 meters. TIRS bands of Landsat 8 are acquired at the 100-meter resolution but resampled to the 30-meter resolution by the cubic convolution method. The cubic convolution uses a cubic transform to interpolate between the control points of known geographical positions to find the geographical coordinates of pixels in the input image. For each pixel in the output image, the algorithm calculates a weighted average of the sixteen closest input pixels and transfer this to the new image. The obtained Landsat images from the USGS included the Level-1 Precision Terrain (L1TP) data. The geo-registration was consistent and an RMSE ≤0.5 pixels was achieved (Weng et al. 2019).
COVID-19 lockdown effect on land surface temperature and normalized difference vegetation index
Published in Geomatics, Natural Hazards and Risk, 2021
In the study, level-1 eighteen Landsat 8 OLI/TIRS data for April and May from 2013 to 2020 were obtained from the United States Geological Survey (USGS) Data Centre (https://www.earthexplorer.usgs.gov). Red, NIR, and TIR bands were required for the research work. The spatial resolution of band 4, band 5, and band 10 of OLI/TIRS data are 30, 30, and 100 m, respectively. The original TIR band 10 was resampled into 30 m spatial resolution by the USGS data centre using the cubic convolution resampling method for further application. The entire research work was performed by using the ArcGIS 9.3 software (https://www.esri.com). The spatial analyst tools of ArcGIS software were used for the raster calculations, correlation analysis, and LST analysis. The following sub-sections are included in the whole methodology section: (1) estimation of LST, (2) determination of NDVI.
The Optical Trapezoid Model (OPTRAM)-based soil moisture estimation using Landsat 8 data
Published in Journal of Spatial Science, 2023
Rajat Pandey, Jyoti Sarup, Shafique Matin, Suresh Band Goswami
Multiple satellite images of LANDSAT-8 with the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) bands were used ranging from May 2009 and October 2019 to get the pre- and post-monsoon data with the minimum cloud cover of the study area. The imagery has been downloaded from the USGS Earth Explorer site (https://earthexplorer.usgs.gov/). . The OLI sensor collects image data for nine shortwave spectral bands over a swath width of 190 km with a spatial resolution of 30 m for all bands, except the 15 m PAN band. The widths of several OLI bands are refined with ETM+ bands to avoid atmospheric absorption characteristics (source: LANDSAT-8 data user’s handbook, 2018).