Publications

Coll, C; Niclòs, R; Puchades, J; García-Santos, V; Perelló, M; Pérez-Planells, L (2024). Demonstrating the Suitability of the Radiance- Based Method for Assessing the Accuracy of MODIS Land Surface Temperature Products. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 62, 4412915.

Abstract
Validation of satellite land surface temperature (LST) products is usually performed through direct comparison with ground-measured LSTs (temperature-based or T-b validation) and with the radiance-based (R-b) method where reference ground LSTs are obtained from at-sensor radiances, atmospheric temperature and water vapor profiles, and emissivity measurements. While T-b is the preferred method, it is only applicable to a few, single-component covers that are thermally homogeneous from the ground measurement scale (a few m) to the satellite scale (usually 1 km). The indirect R-b method is an alternative to extend the LST validation at a global scale over cover types where the T-b method is not feasible. In this study, we used ground LST measurements taken in a thermally homogeneous site coincident with moderate resolution imaging spectroradiometer (MODIS) Terra and Aqua overpasses (86 matchups) to assess the accuracy of the R-b LSTs derived from MODIS data. Mean bias (ground minus R-b LSTs) of -0.1 K and root mean square error (RMSE) of 0.8 K were obtained, showing good performance against ground measurements. Then, we apply operationally the R-b method for validating MODIS Level 2 LST products M*D11 and M*D21 over six test sites comprising a varied range of surfaces (4267 cases). The emissivity values necessary for the R-b calculations were obtained from 1) the M*D11 and M*D21 emissivity product and 2) an independently modeled emissivity using ground measurements and vegetation cover fraction estimates. For the M*D11 product, we obtained an overall bias (product minus R-b LST) of -0.5 K and RMSE of 0.9 K with either the product or the modeled emissivities. However, a significant difference was found between daytime and nighttime bias (-1.0 and 0.0 K, respectively). The daytime cold bias of M*D11 was attributed to an ill-tuning of the algorithm for high surface temperature and atmospheric humidity conditions. For the M*D21 product, the overall bias (RMSE) was 0.2 K (0.6 K) with the product emissivity, and 0.6 K (1.1 K) with the modeled emissivity, both in nighttime and daytime. These results compare well with recent studies and contribute to the global assessment of MODIS LST uncertainty.

DOI:
10.1109/TGRS.2024.3454377

ISSN:
1558-0644