TY - JOUR
T1 - Comparison Spatial Pattern of Land Surface Temperature with Mono Window Algorithm and Split Window Algorithm
T2 - 4th International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2017, LISAT-FSEM 2017
AU - Bunai, Tasya
AU - Rokhmatuloh,
AU - Wibowo, Adi
N1 - Funding Information:
This study was conducted in Department Geography, Faculty of Mathematics and Natural Science Universitas Indonesia.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/5/16
Y1 - 2018/5/16
N2 - In this paper, two methods to retrieve the Land Surface Temperature (LST) from thermal infrared data supplied by band 10 and 11 of the Thermal Infrared Sensor (TIRS) onboard the Landsat 8 is compared. The first is mono window algorithm developed by Qin et al. and the second is split window algorithm by Rozenstein et al. The purpose of this study is to perform the spatial distribution of land surface temperature, as well as to determine more accurate algorithm for retrieving land surface temperature by calculated root mean square error (RMSE). Finally, we present comparison the spatial distribution of land surface temperature by both of algorithm, and more accurate algorithm is split window algorithm refers to the root mean square error (RMSE) is 7.69° C.
AB - In this paper, two methods to retrieve the Land Surface Temperature (LST) from thermal infrared data supplied by band 10 and 11 of the Thermal Infrared Sensor (TIRS) onboard the Landsat 8 is compared. The first is mono window algorithm developed by Qin et al. and the second is split window algorithm by Rozenstein et al. The purpose of this study is to perform the spatial distribution of land surface temperature, as well as to determine more accurate algorithm for retrieving land surface temperature by calculated root mean square error (RMSE). Finally, we present comparison the spatial distribution of land surface temperature by both of algorithm, and more accurate algorithm is split window algorithm refers to the root mean square error (RMSE) is 7.69° C.
UR - http://www.scopus.com/inward/record.url?scp=85047751042&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/149/1/012066
DO - 10.1088/1755-1315/149/1/012066
M3 - Conference article
AN - SCOPUS:85047751042
SN - 1755-1307
VL - 149
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012066
Y2 - 9 October 2017 through 11 October 2017
ER -