TY - GEN
T1 - Seasonal assessment of surface temperature with normalized vegetation index and surface albedo over pampa biome
AU - Käfer, P.S.
AU - Rocha, N.S.
AU - Diaz, L.R.
AU - Kaiser, E.A.
AU - Costa, S.T.L.
AU - Hallal, G.
AU - Veeck, G.
AU - Roberti, D.
AU - Rolim, S.B.A.
PY - 2020/11/4
Y1 - 2020/11/4
N2 - © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.Land surface temperature (LST) governs many biophysical processes at the land-atmosphere interface and the relationship vegetation-LST has been the premise of many studies. This paper purposed to correlate LST with normalized difference vegetation index (NDVI) and surface albedo in the grasslands of Pampa biome during winter and summer seasons. Four Landsat 8 scenes with clear-sky conditions were acquired from the US Geological Survey website and NDVI and surface albedo were calculated. Afterwards, LST was obtained using Split-window (SW) algorithm. Results showed that LST in winter season exhibited less variations between pixels in comparison to summer, where the heterogeneity of the environment is significantly more detectable. LST retrieved from Landsat 8 data was consistent with the actual temperature measured in the field, with differences varying between 1-1.6 K. The LST-Vegetation relationship in the Pampa grasslands varies with the season so that caution must be taken in assuming a regular behaviour between LST and remote sensing vegetation variables, such as empirical relationships that are widely used in many scientific fields.
AB - © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.Land surface temperature (LST) governs many biophysical processes at the land-atmosphere interface and the relationship vegetation-LST has been the premise of many studies. This paper purposed to correlate LST with normalized difference vegetation index (NDVI) and surface albedo in the grasslands of Pampa biome during winter and summer seasons. Four Landsat 8 scenes with clear-sky conditions were acquired from the US Geological Survey website and NDVI and surface albedo were calculated. Afterwards, LST was obtained using Split-window (SW) algorithm. Results showed that LST in winter season exhibited less variations between pixels in comparison to summer, where the heterogeneity of the environment is significantly more detectable. LST retrieved from Landsat 8 data was consistent with the actual temperature measured in the field, with differences varying between 1-1.6 K. The LST-Vegetation relationship in the Pampa grasslands varies with the season so that caution must be taken in assuming a regular behaviour between LST and remote sensing vegetation variables, such as empirical relationships that are widely used in many scientific fields.
U2 - 10.5194/isprs-archives-XLII-3-W12-2020-471-2020
DO - 10.5194/isprs-archives-XLII-3-W12-2020-471-2020
M3 - Conference contribution
VL - 42
T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SP - 471
EP - 476
BT - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
A2 - Hernandez Palma, H.J.
A2 - Cardenas Mansilla, C.A.
A2 - Frery, A.
A2 - Feitosa, R.Q.
PB - International Society for Photogrammetry and Remote Sensing
T2 - 2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020
Y2 - 22 March 2020 through 26 March 2020
ER -