Publications

Niclos, R; Perez-Planells, L; Coll, C; Valiente, JA; Valor, E (2018). Evaluation of the S-NPP VIIRS land surface temperature product using ground data acquired by an autonomous system at a rice paddy. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 135, 1-12.

Abstract
The S-NPP VIIRS Land Surface Temperature (LST) product attained the stage V1 of validation maturity (provisional validated) at the end of 2014. This paper evaluates the current VIIRS V1 LST product versus concurrent ground data acquired at a rice paddy site from December 2014 to August 2016. The experimental site has three different seasonal and homogeneous land covers through the year, which makes the site interesting for validation activities. An autonomous and multiangular system was used to record continuous ground data at the site. The data acquired at zenith angles similar to the VIIRS viewing angles were used for the validation to avoid possible differences between satellite and ground views due to angular dependences of the LSTs. Concurrently to surface data, downwelling sky radiances were measured at different incidence angles by the system, which were used to improve the cloud screening of the validation dataset, since cloud leakage was identified in previous validation studies as an important issue for further improvement. The validation results show good performance for the VIIRS V1 LST product at zenith angles <= 40 degrees, with systematic uncertainties within +/- 0.5 K and accuracies around 1.2 K. These values are within the threshold requirements established for the VIIRS LST product, and they are better than the validation results published previously for the beta version of the product or using VIIRS data reprocessed with the calibrated algorithm coefficients implemented from April 2014. As the VIIRS LST algorithm has regression coefficients dependent on land cover type, the impact of land cover misclassifications on VIIRS LST data accuracy was also evaluated. It was expected that changing the surface type assigned by the VIIRS product to more appropriate types at the site pixels should improve the validation results. However, the improvement was limited, likely due to the reduced range of variability of the emissivities considered for the different land cover types in the regression process of the VIIRS LST algorithm coefficients. The results reveal the difficulties and uncertainties involved in the LST retrieval when using a LST algorithm with surface type dependent coefficients. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

DOI:
10.1016/j.isprsjprs.2017.10.017

ISSN:
0924-2716