Eko Susilo, Rizki Hanintyo, Adi Wijaya


The new Landsat generation, Landsat-8, is equipped with two bands of thermal infrared sensors (TIRS). The presence of two bands provides for improved determination of sea surface temperature (SST) compared to existing products. Due to its high spatial resolution, it is suitable for coastal zone monitoring. However, there are still significant challenges in converting radiance measurements to SST, resulting from the limitations of in-situ measurements. Several studies into developing SST algorithms in Indonesia waters have provided good performance. Unfortunately, however, they have used a single-band windows approach, and a split-windows approach has yet to be reported. In this study, we investigate both single-band and split-window algorithms for retrieving SST maps in the coastal zone of Wangi-Wangi Island, Wakatobi, Southeast Sulawesi, Indonesia. Landsat-8 imagery was acquired on February 26, 2016 (01: 51: 44.14UTC) at position path 111 and and row 64. On the same day, in-situ SST was measured by using Portable Multiparameter Water Quality Checker – 24. We used the coefficient of correlation (r) and root mean square error (RMSE) to determine the best algorithm performance by incorporating in-situ data and the estimated SST map. The results showed that there were differences in brightness temperature retrieved from TIRS band10 and band 11. The single-band algorithm based on band 10 for Poteran Island clearly showed superior performance (r = 69.28% and RMSE = 0.7690°C). This study shows that the split-window algorithm has not yet produced a accurate result for the study area.


Landsat-8; single-band algorithm; split-window algorithm

Full Text:



Arief, M., Adawiah, S. W., Parwati, E., Hamzah, R., & Prayogo, T. (2015). Development model of sea surface temperature extraction using Landsat- 8 satellite data, case study: Lampung Bay. Jurnal Penginderaan Jauh, 12, 107–122.

Fisher, J. I., & Mustard, J. F. (2004). High spatial resolution sea surface climatology from Landsat thermal infrared data. Remote Sensing of Environment, 90, 293–307.

Hansen, C. H., Williams, G. P., & Adjei, Z., (2015). Long-term application of remote sensing chlorophyll detection models: Jordanelle Reservoir case study. Natural Resources, 6, 123–129.

Irons, J. R., Dwyer, J. L, & Barsi, J. A. (2012). The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment, 122, 11-21.

Jimenez-Munoz, J. C., Sobrino, J. A, Skokovic, D., & Mattar, C. (2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, 11, 1840-1843.

Roy, D. P., Wulder, M. A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M. C., … Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154-172.

Syariz, M. A., Jaelani, L. M., Subehi L., Pamungkas, A., Koenhardono, E. S., & Sulisetyono, A. (2015). Retrieval of sea surface temperature over Poteran Island water of Indonesia with Landsat 8 TIRS Image: A preliminary algorithm. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (pp. 87–90).

Thomas, A., Byrne, D., & Weatherbee, R. (2002). Coastal sea surface temperature variability from Landsat infrared data. Remote Sensing of Environment, 81, 262–272.

Trisakti, B., Sulma, S., & Budhiman, S. (2004). Study of sea surface temperature (SST) using Landsat-7 ETM (In Comparison with sea surface temperature of NOAA-12 AVHRR). In Liu C-T (Ed.) Thirteenth Workshop of OMISAR (WOM-13) on validation and application of satellite data for marine resources conservation. Bali, Indonesia: Environmental Protection Administration

Walton, C. C., Pichel, W. G., Sapper, J. F, and May, D. A. (1998). The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. Journal of Geophysical Research: Oceans, 103, 27999–28012.

Xing, Q., Chen, C.-Q., & Shi, P. (2006). Method of integrating landsat-5 and landsat-7 data to retrieve sea surface temperature in coastal waters on the basis of local empirical algorithm. Ocean Science Journal, 41, 97–104.

Xufeng, X., Yang, L., Wentong, D., Zhonglin, W., Lianlong, Z., Zhen, S., & Huang, M. (2015). An algorithm to inverse sea surface temperatures at offshore water by employing Landsat 8/TIRS Data. In A.M. Lagmay (Ed.), 36th Asian Conference on Remote Sensing 2015 (ACRS 2015): Fostering Resilient Growth in Asia Philippines: Curran Associates, Inc, pp. 4504–4511.

Zhang, Y., Guindon, B., & Cihlar, J. (2002). An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images. Remote Sensing of Environment, 82, 173–187.


  • There are currently no refbacks.