Atmospheric water vapor retrieval from Landsat 8 thermal infrared images

Abstract

Atmospheric water vapor (wv) is required for the accurate retrieval of the land surface temperature from remote sensing data and other applications. This work aims to estimate wv from Landsat 8 Thermal InfraRed Sensor (TIRS) images using a new modified split‐window covariance‐variance ratio (MSWCVR) method on the basis of the brightness temperatures of two thermal infrared bands. Results show that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.3 g/cm2 for dry atmosphere (wv <2 g/cm2) conditions and better than 0.5 g/cm2 for wet atmosphere conditions. The method was applied at different locations with dry and moist atmospheres and was validated at 42 ground sites using AERONET (Aerosol Robotic Network) ground‐measured data and MODIS (Moderate Resolution Imaging Spectroradiometer) products. The results show that the retrieved wv from the TIRS data is highly correlated with the wv of AERONET and MODIS but is generally larger. This difference was probably attributed to the uncertainty of radiometric calibration and stray light coming outside from field of view of TIRS instrument in the current images. Consequently, the data quality and radiometric calibration of the TIRS data should be improved in the future.

A Practical Single‐Channel Algorithm for Land Surface Temperature Retrieval: Application to Landsat Series Data

“…To perform the GSC and PSC algorithms, L sen , T sen , ε , w , and T n (optional) should be obtained. L sen (W·m −2 ·sr −1 ·μm −1 ) and T sen (K) can be calculated from TIR data after radiative calibration; ε can be estimated using normalized difference vegetation index (NDVI) thresholds method (Sobrino et al, ); w (g/cm 2 ) can be obtained from Landsat 8 TIRS data using split‐window covariance‐variance ratio method (Ren et al, ; Wang et al, ), satellite AWV products or meteorological products (e.g., MODIS total column precipitable water vapor [MOD05] and NCEP); T n (°C) can be estimated by means of Geographic Information System and Remote Sensing (Cristóbal et al, ; Wang et al, ).…”

Improved Assessment of Atmospheric Water Vapor Content in the Himalayan Regions Around the Kullu Valley in India Using Landsat‐8 Data

“…In equation , the critical values of NDVI for vegetation and soil are represented by NDVI max and NDVI min , respectively, and P v is the vegetation fraction. Ren et al () introduced the modified SWCVR for the estimation of WVC incorporating a second‐order polynomial expression as shown in equation , where the coefficients ( a 1 , a 2 , and a 3 ) are determined using regression with a priori reference such as the Thermodynamic Initial Guess Retrieval (TIGR) atmospheric profiles (Chedin et al, ), meteorological observations, and or other spaceborne products of MODIS or other sensors (Wang et al, ). …”

“…In equation (3), the critical values of NDVI for vegetation and soil are represented by NDVI max and NDVI min , respectively, and P v is the vegetation fraction. Ren et al (2015) introduced the modified SWCVR for the estimation of WVC…”

“…However, these techniques provide scattered point observations of WVC and are thus unable to provide much insight in the regional or global distribution of WVC (Li et al, 2003(Li et al, , 2013. In literature, various methods have been proposed for estimating WVC categorized with respect to the remote sensing data, such as near infrared (Gao & Kaufman, 2003), thermal infrared (TIR; Li et al, 2003;Ren et al, 2015), hyperspectral (Barducci et al, 2004), and microwave data (Padmanabhan et al, 2009). Although the methods utilizing near-infrared channels are widely popular for estimation of WVC such as in Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 products, they are constrained by the availability of water vapor absorption channels and are often not applicable to certain multispectral sensors such as in Operating Linear Imager (OLI) sensor covers the thermal spectrum using two TIR channels at 11 and 12 μm, which are used for estimation of WVC using split window techniques.…”

Model of Relationships between Land Surface Temperature and Urban Built-Up Areas in Mueang Buriram District, Thailand

“…The analysis methods are shown in Equation 6 NIR = Reflection value of vegetation in near Infrared wave range (NIR) ρ Red = Reflection value of vegetation in red wave range (RED) ρ = Reflection value of the soil cover ρ = Reflectance multi-band of Landsat-8 A ρ = Reflectance add band of Landsat-8 Q col = Digital number (DN)Atmospheric Water Vapor AnalysisAtmospheric water vapor could be analyzed from the transmission of electromagnetic wave through the atmosphere with the thermal infrared sensor radiation range using brightness temperature values of b10 and b11. The analysis methods are shown in Equation 7[27].…”