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The Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature (LST) has been operationally produced for a decade since the Suomi National Polar-orbiting Partnership (SNPP) launched in October 2011. A comprehensive evaluation of its accuracy and precision will be helpful for product users in climate studies and atmospheric models. In this study, the VIIRS LST is validated with ground observations from multiple high-quality radiation networks, including six stations from the Surface Radiation budget (SURFRAD) network, two stations from the Baseline Surface Radiation Network (BSRN), and 13 stations from the Atmospheric Radiation Measurement (ARM) network, to evaluate its performance over various land-cover types. The VNP21A1 LST was validated against the same ground observations as a reference. The results yield a close agreement between the SNPP VIIRS LST and ground LSTs with a bias of −0.4 K and a RMSE of 1.96 K over six SURFRAD sites; a bias of −0.2 K and a RMSE of 1.93 K over two BSRN sites; and a bias of −0.1 K and a RMSE of 1.7 K over the 13 ARM sites. The time series of the LST errors over individual sites indicate seasonal cycles. The data anomaly over the BSRN site in Cabauw and the SURFRAD site in Desert Rock is revealed and discussed in this study. In addition, a method using Landsat-8 data is applied to quantify the heterogeneity level of each ground station and the results provide promising insights. The validation results demonstrate the maturity of the JPSS VIIRS LST products and their readiness for various application studies. [ABSTRACT FROM AUTHOR]

The land surface albedo product of visible infrared imaging radiometer suite (VIIRS) in National Oceanic and Atmospheric Administration (NOAA)'s operational system provides real-time, global daily mean surface albedo, which is a required parameter in the estimation of the daily shortwave net radiation budget. The global gridded VIIRS albedo product is derived from the level-2 granule surface albedo product, which is generated using a Direct Estimation Method. Special gridding and compositing algorithms were developed for aggregating the granular albedo data into the gridded albedo product. This paper describes the design and evaluation of the NOAA VIIRS gridded daily surface albedo product. The cloudy condition, retrieval path, retrieval method, and observing geometry are the criteria in deciding the priority order in the composition processing. The proposed albedo product possesses a complete spatial coverage over global land and ice surface and provides a timely response to surface dynamics. The validation of satellite retrieved daily mean albedo against ground counterparts over a series of well-maintained networks demonstrates the reliability of the composed albedo considering the interference of the seasonal surface heterogeneity conditions around each site. The inter-comparison between the S-NPP and NOAA-20 VIIRS albedo shows good agreement, except for a minor bias related to solar/view angle differences. The cross-comparison between VIIRS albedo and MODIS albedo shows good consistency with some deviations related to the controversy between their upstream snow mask. • Daily global gap-free albedo product from VIIRS operational at NOAA. • Direct estimation of albedo using clear-sky observations. • Comprehensive validation with in-situ observations indicates good product quality. • Major sources of product uncertainty and their mitigation are discussed. • Products from SNPP and NOAA-20 are highly consistent. [ABSTRACT FROM AUTHOR]

Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration's (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and the limited sample size. The VSIA data will serve for weather prediction applications and climate model calibrations. Combined with the historical observations from MODIS, current S-NPP VIIRS, and NOAA-20 VIIRS observations, VSIA will dramatically contribute to providing high-accuracy routine sea-ice albedo products and irreplaceable records for monitoring the long-term sea-ice albedo for climate research. [ABSTRACT FROM AUTHOR]

• Novel method for cloudy-sky albedo estimation involving passive microwave and climatological data. • Higher accuracy of 1 km cloudy-sky albedo compared to other methods (especially in snow) • Significantly improved performance for capturing ephemeral snow events under clouds. Land surface albedo (LSA) is an essential component of the surface radiation budget, and has been retrieved extensively as a basic remote sensing product; however, daily LSA products suffer from extensive data gaps primarily caused by cloud cover. Accordingly, several gap-filling methods were developed (e.g., spatiotemporal interpolation and data fusion with albedo climatology), although the traditional methods are limited by cloud scale and surface heterogeneity. Further, as the largest varying surface landscape feature, seasonal snow cover substantially influences LSA and represents a major uncertainty factor of gap recovery because previous studies failed to employ actual surface signals to capture such ephemeral but intense albedo changes under cloud cover. To address this issue, a three-step framework was proposed for estimating 1 km cloudy-sky LSA using passive microwave (PMW) data, albedo climatology, and Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky albedo: (1) All-sky snow albedo was estimated from PMW brightness temperatures using a statistical model, (2) Continuous albedo dynamics were generated by combining the all-sky snow albedo with snow-free climatological albedo, and (3) The 1 km cloudy-sky LSA was predicted after filtering 1 km VIIRS clear-sky LSA by the albedo dynamic series. PMW-derived snow albedo was assessed over the Contiguous US (CONUS), and the final 1 km cloudy-sky LSA was validated across 10 sites from SURFRAD and Core AmeriFlux in 2013. Based on the comparison with high-quality MODIS pixels, the estimated snow albedo yielded an overall RMSE of 0.064 over CONUS, with a bias of −0.010 (R2 = 0.845). The recovered 1 km cloudy-sky LSA produced RMSEs of 0.074 (0.137) for all (snow) samples, a significant improvement over the Global Land Surface Satellite (GLASS) gap-free albedo products especially on snow cases (p-value = 0.027). Corresponding RMSE in calculating surface net radiation was also decreased by 38.91 W·m−2; and anomalous snow samples were corrected as well. The temporal analysis and all-sky LSA mapping suggest that the recovered LSA has satisfactory spatiotemporal continuity, and successfully captured details of spatiotemporal variability, especially for ephemeral snow events. This study provides an innovative solution to recover gaps in LSA data, and considerably improves the LSA accuracy under cloud cover, which can inform snow melting modeling, hazard forecasting, and irrigation management. [ABSTRACT FROM AUTHOR]