NOAA will soon use the new Visible Infrared Imager Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) as its primary polar-orbiting satellite imager. Employing a near real-time processing system, NOAA will generate a series of Environmental Data Records (EDRs) from VIIRS data. For example, the VIIRS Land Surface Temperature (LST) EDR will estimate the surface skin temperature over all global land areas and provide key information for monitoring Earth surface energy and water fluxes. Because both VIIRS and its processing algorithms are new, NOAA is conducting a rigorous calibration and validation program to understand and improve product quality. This paper presents a new validation methodology to estimate the quantitative uncertainty in the LST EDR, and contribute to improving the retrieval algorithm. It employs a physically-based approach to scaling up point LST measurements currently made operationally at many field and weather stations around the world. The scaling method consists of the merging information collected at different spatial resolutions within a land surface model to fully characterize large area (km × km scale) satellite products. The approach can be used to explore scaling issues over terrestrial surfaces spanning a large range of climate regimes and land cover types, including forests and mixed vegetated areas. The methodology was tested successfully with NASA/MODIS data, indicating an absolute error for MODIS LST products of 2.0 K at a mixed agricultural site (Bondville, IL) when accounting for scaling, and higher than 3 K without scaling. The VIIRS LST EDR requires a 1.5 K measurement accuracy and 2.5 K measurement precision. Ultimately, this validation approach should lead to an accurate and continuously-assessed VIIRS LST product suitable to support weather forecast, hydrological applications, or climate studies. It is readily adaptable to other moderate resolution satellite systems.
International audienceThis study fits within the overall research on the usage of space remote sensing data to constrain land surface models (LSMs) (also called SVAT models for soil–vegetation–atmosphere transfer). The goal of this paper is to analyze the potential of using thermal infrared (TIR) remote sensing data for LSM calibration. LSMs are characterized by a large number of parameters and initial conditions that have to be specified. This model calibration is generally performed at a local scale by minimization between measurements and time series difference. Recent studies have shed light on the use of multiobjective approaches for performing calibration and for analyzing the model's sensitivity to input parameters. Such an approach has been implemented in the SEtHyS LSM (for “Suivi de l'Etat Hydrique des Sols,” the French acronym for soil moisture monitoring) with the objective of assessing the information contributed by having knowledge of the remote sensing surface brightness temperature. For this purpose, the model calibration was performed in three different cases at field scale corresponding to different calibration design. The analysis of these numerical experiments permits the authors to show the contribution and the limits of TIR remote sensing data for LSM calibration, in various environmental conditions. The perspectives underline the potential of using a dynamic calibration methodology, taking advantage of the time-varying model parameters' influence
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.