Title :
Atmospheric water vapor retrieval from Landsat 8 and its validation
Author :
Huazhong Ren ; Chen Du ; Qiming Qin ; Rongyuan Liu ; Jinjie Meng ; Jing Li
Author_Institution :
Inst. of Remote Sensing & Geographic Inf. Syst., Peking Univ., Beijing, China
Abstract :
This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45 g/cm2 for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm2) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.
Keywords :
atmospheric humidity; calibration; moisture; AERONET ground-measured data; Landsat 8 TIRS data; Landsat 8 Thermal InfRared Sensor image; MODIS products; atmospheric moisture conditions; atmospheric water vapor retrieval; data quality; data systematic noise; ground sites; model analysis; modified split-window covariance-variance ratio method; radiative calibration error; Accuracy; Atmospheric modeling; Earth; MODIS; Remote sensing; Satellites; Landsat 8; MSWCVR; TIRS; split-window algorithm; water vapor;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
DOI :
10.1109/IGARSS.2014.6947119