Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison

Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some well-known Single-Channel (SC) and Split-Window (SW) algorithms, adapted to Landsat 8/TIRS, under the conditions of a high-contrast semiarid agroecosystem. The recently released Landsat 8 Level-2 LST product (L8_ST) has also been included in the performance analysis. Ground measurements of surface temperature were taken for the evaluation during the summers of 2018-2019 in the cropland area of the Barrax test site, Spain. A dataset of 44 ground samples and 11 different L8/TIRS dates/scenes was gathered, covering a variety of crop fields and surface conditions. In addition, a simplified Single Band Atmospheric Correction (L-SBAC) was introduced based on a linearization of the atmospheric correction parameters with the water vapor content (w) and a redefinition of the emissivity threshold for the emissivity correction in the study site. The best results show differences within +/- 4.0 K for temperatures ranging 300-325 K. Statistics for the L-SBAC result in a RMSE of +/- 1.8 K with negligible systematic deviation. Similar results were obtained for the other SC and SW algorithms tested, whereas an overestimation of 1.0 K was observed for the L8_ST product because of inappropriate assignment of emissivity values. These results show the potential of the proposed linearization approach and set the uncertainty for LST estimates in high-contrast semiarid agroecosystems.

Automated summary

This study evaluates the performance of several algorithms for estimating land surface temperature (LST) from Landsat 8/TIRS under high-contrast semiarid agroecosystem conditions. A simplified single band atmospheric correction method was proposed and showed potential for LST estimation, while also setting the uncertainty for LST estimates in high-contrast semiarid agroecosystems.