Land surface temperature (LST) is of fundamental importance to many aspects of the geosciences, for example, net radiation budget at the Earth surface, monitoring state of crops and vegetation, as well as an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. Satellite LST measurements provide unique data sources for regional and global coverage in fairly good temporal, spatial resolution, and time span. Therefore, LST is one of the baseline products in both Joint Polar-orbiting Satellite System (JPSS) and Geostationary Operational Environmental Satellite-R Series (GOES-R) satellite missions. This article introduces methodology of the LST measurements for the JPSS and GOES-R satellite missions, including the step by step procedures of the algorithm development and validation. Specifically, in order to evaluate the JPSS and GOES-R LST products, ground observations from SURFRAD, BSRN, GMD, and KIT network projects are utilized to estimate the in situ LSTs. The evaluation is performed with respect to the surface type, day/night condition, seasonality, site characteristics, and cross-satellite products. Overall results indicate that the JPSS LST is generally consistent to the MODIS LST, and the GOES-R LST is expected to meet the mission's requirement.

There are a set of satellite land surface products produced to public use at the U.S. NOAA/NESDIS, including (but not limited to) land surface temperature (LST), land surface albedo (LSA) [1], vegetation indices (VIs) [2], green vegetation fraction (GVF) [3] and leaf area index (LAI). These products are of fundamental importance to many aspects of geoscience disciplines, e.g., the net radiation budget at the Earth surface, monitoring state of crops and vegetation, and serving as important indicators of both the greenhouse effect and the physics of land-surface processes at local through global scales [4] [5] [6]. In particular, LST, LSA and LAI are listed as essential climate variables (ECVs) by the Global Climate Observing System of the World Meteorological Organization [7]. LST, LSA, VIs and GVF are baseline products produced for years at NOAA/NESDIS, through observations from the U.S. Joint Polar-orbiting Satellite System (JPSS).In the past two years, significant product improvements and quality assurance have been accomplished on the above land surface products. First, the VI and GVF algorithm package has been optimized which greatly reduced the processing time and improved robustness of the production; meanwhile, an innovative high resolution (up to 20 meters, daily) VI algorithm has been developed. Second, an all-weather LST algorithm has been tested and evaluated; operational all-weather LST production is expected in 2025. Third, a LAI production package has been approved and is in the operational installation process; experimental LAI data production is expected by late 2024. Fourth, a bare soil (background of the vegetation surface) albedo climatology dataset has been developed which improves quality of the albedo data; a BRDF data production algorithm package has been developed and is in the operational installation process. Fifth, a daily data monitoring system has been run in the science team, in addition to production monitoring in the operational unit, which particularly checks scientific significance of the products for LST and LSA; as a result, global LST anomaly report is generated monthly. Finally, in-situ data collection, filtering and analysis process has been improved, which is the base of our product quality. This presentation provides a comprehensive summary of all above-mentioned in detail.

In this study, we introduce the gridded VIIRS LST product focusing on its quality assessment and applications. The quality assessment is performed using ground observations over various land surface types (e.g., cropland, desert, grassland, shrubland, water, and snow/ice) from five ground observation networks, including Surface Radiation budget network (SURFRAD), Atmospheric Radiation Measurement (ARM), Baseline Surface Radiation Network (BSRN), Programme for Monitoring of the Greenland Ice Sheet (PROMICE), and National Data Buoy Center (NDBC) covering a wide latitude range across the globe. Four years of satellite LST data from 2019 to 2022 were used for validation and the results indicate good agreement with ground observations, with an accuracy ranging from -0.86 K to 0.78 K and RMSE ranging between 1.03 K to 2.36 K. We present statistical and time series analysis including a seasonal variation analysis. Additionally, cross-satellite comparisons were conducted with MYD11A1 LST and VNP21A1 LST product. One day in each month was selected for comparison at a global scale. The results suggest that MYD11A1 has the lowest LST estimates for both daytime and nighttime, while the L3 VIIRS LST is intermediate between MYD11A1 and VNP21A1 during daytime and close to VNP21A1 at nighttime.The gridded LST data has been utilized in many applications, such as a critical input for developing the soil moisture product and global evapotranspiration product with high spatial resolution, and monitoring extreme weather events, etc.

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.

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 (R-2 = 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.

Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/. Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). The 500m global surface blue-sky daily albedo climatology dataset follows the basic MODIS product format and employed the sinusoidal projection. It includes historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.

Surface incident shortwave radiation (ISR) is an important component of the surface radiation budget. We refined the optimization method developed for polar-orbiting satellite data [1] and applied it to estimate ISR from the new generation geostationary Advanced Himawari Imager (AHI) onboard the Himawari-8/9 satellite and Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R Series. Validation of the AHI ISR estimation at 2-km resolution showed an $R^{2}$ of 0.93, bias of 0.52 W/m 2 , and RMSE of 106.52 W/m 2 for instantaneous estimates; an $R^{2}$ of 0.95, bias of −0.12 W/m 2 , and RMSE of 22.49 W/m 2 for daily mean ISR; and a bias of −0.18 W/m 2 and RMSE of 7.72 W/m 2 for monthly mean ISR. Validation of the ABI ISR at 2-km spatial resolution showed an $R^{2}$ value of 0.93, bias of 8.71 W/m 2 , and RMSE of 102.30 W/m 2 for instantaneous estimates; an $R^{2}$ of 0.95, bias of −2.38 W/m 2 , and RMSE of 27.17 W/m 2 for daily mean ISR; and a bias of 1.40 W/m 2 and RMSE of 14.75 W/m 2 for monthly mean ISR. Our study also demonstrated that AHI and ABI observations have realized much better estimations for hourly and diurnal ISR than previous polar-orbiting satellite data because of their higher frequency of sampling on the atmospheric conditions.

Land surface albedo plays a critical role in climate, hydrological and biogeochemical modeling, and weather forecasting. It is often assigned in models and satellite retrievals by albedo climatology look‐up tables using land cover type and other variables; however, there are considerable differences in albedo simulations among models, which partially result from uncertainty in obsolete albedo climatology. Therefore, this study introduces a new global 500 m daily blue‐sky land surface albedo climatology data set under both actual and snow‐free surface conditions utilizing 20‐year Moderate Resolution Imaging Spectroradiometer products from Google Earth Engine. In situ measurements from 38 long‐term‐maintained sites were utilized to validate the accuracies of different albedo climatology datasets. The root‐mean‐square error, bias, and correlation coefficient of the new climatology are 0.031, −0.003, and 0.96, respectively, which are more accurate than the Global Land Surface Satellite, GlobAlbedo, and 16 model datasets. Data intercomparison suggests that ERA5 exhibits better performance than Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and 14 Coupled Model Intercomparison Projects Phase 6 models. However, it contains positive biases in the snow‐free season, while MERRA2 underestimates the snow albedo. Global albedo variation associated with basic surface plant functional types was also characterized, and snow impact was considered separately. Temporal variability analysis indicates that traditional climatology datasets with coarser temporal resolutions (≥8 days) cannot capture albedo variation over areas with distinct snow seasons, especially in central Eurasia and boreal regions. These results confirm the high reliability and robustness of the new albedo climatology in model assessment, data assimilation, and satellite product retrievals.

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.

Incident surface shortwave radiation (ISR) is a key parameter in Earth's surface radiation budget. Many reanalysis and satellite-based ISR products have been developed, but they often have insufficient accuracy and resolution for many applications. In this study, we extended our optimization method developed earlier for the MODIS data with several major improvements for estimating instantaneous and daily ISR and net shortwave radiation (NSR) from Visible Infrared Imaging Radiometer Suite observations (VIIRS), including (1) an integrated framework that combines look-up table and parameter optimization; (2) enabling the calculation of net shortwave radiation (NSR) as well as daily values; and (3) extensive global validation. We validated the estimated ISR values using measurements at seven Surface Radiation Budget Network (SURFRAD) sites and 33 Baseline Surface Radiation Network (BSRN) sites during 2013. The root mean square errors (RMSE) over SURFRAD sites for instantaneous ISR and NSR were 83.76 W/m(2) and 66.80 W/m(2), respectively. The corresponding daily RMSE values were 27.78 W/m(2) and 23.51 W/m(2). The RMSE at BSRN sites was 105.87 W/m(2) for instantaneous ISR and 32.76 W/m(2) for daily ISR. The accuracy is similar to the estimation from MODIS data at SURFRAD sites but the computational efficiency has improved by approximately 50%. We also produced global maps that demonstrate the potential of this algorithms to generate global ISR and NSR products from the VIIRS data.

An urban air temperature model is presented using the enterprise GOES-16 land surface temperature product. The model is constructed by fitting the difference between ground-truth air temperature data against satellite LST using a Gaussian function. A time-match algorithm aligns the ground and satellite measurements within 5-min of one another, and the resulting matched values are compared over ten months to investigate their correlation. Land cover, latitude, longitude, local time, and elevation are input to a regressive neural network to fit each unique GOES-16 pixel according to ground-based properties. Over 150 ground stations and satellite pixels throughout the continental U.S. are used near urban areas to construct the diurnal Gaussian relationship and approximate air temperature. Statistics from a five month validation period generates an RMSE of 2.6 K, a bias of 0.8 K, and R2 of 0.86, which are in strong competition with other studies at lower resolution, less geographic integration, and less temporal resolvability. The algorithm also produced strong spatial correlations with a high resolution numerical model, resulting in a mean RMSE value of 2.1 K for nearly 7000 pixels. The overall presentation of this model aims to simplify the calculation of air temperature from satellite LST and create a successful model that performs well in heterogeneous environments. The improvement of urban air temperature calculations will also result in improved satellite land surface products such as relative humidity and heat index.

Land surface emissivity (LSE) is a key parameter for the determination of land surface temperature (LST) from thermal remotely sensed data. A new LSE product has been developed at the National Oceanic and Atmospheric Administration (NOAA), College Park, MD, USA, to enhance the LST product for the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite R-Series (GOES-R) missions as well as to support the forecasting models. A 1-km resolution bare ground emissivity was derived from the historical Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset (ASTER-GED) and Moderate Resolution Imaging Spectroradiometer (MODIS) LSE product. It is then spectrally adjusted to the split window (SW) channels of Visible/Infrared Imager Radiometer Suite (VIIRS) onboard JPSS and Advanced Baseline Imager (ABI) from GOES-R. VIIRS daily green vegetation fraction and snow fraction products are subsequently used to account for the dynamic variations. Validation results indicate that the product has a good agreement with in situ observations, with an emissivity difference less than 0.006 at the bare surface sites and a difference less than 0.007 at one cropland site with three growth stages. The intercomparison with NASA VIIRS daily LSE product proves a good agreement with a standard deviation of less than 0.012. To evaluate the LSE performance in LST retrieval, a whole year VIIRS LST is produced and validated over the SURFace RADiation budget observing network (SURFRAD) sites. The results indicate that the LSE works well with LST RMSE of 1.78 (daytime) and 1.58 K (nighttime). The new LSE algorithm has been integrated to NOAA-20 LST with the status of provisional maturity and will be applied on GOES-16/17 LST product in the near future.

The enterprise Land Surface Temperature (LST) algorithm has been operationally implemented for Visible Infrared Imager Radiometer Suite (VIIRS) onboard both NOAA 20 (N20) and Suomi National Polar-orbiting Partnership (S-NPP) satellite since September, 2019. This study presents the validation of the two LST products. The ground based measurements from Baseline Surface Radiation Network (BSRN) and Surface Radiation Budget Network (SURFRAD) were used to estimate the quantitative uncertainty of the LST product. The validation results present a similarly close agreement between ground observations and satellite estimations from both N20 and SNPP VIIRS LST products. The accuracy is about -0.4 K for N20 and -0.3 K for S-NPP and the precision is about 1.9 K for both LST products over SURFRAD sites. Similar performance is achieved over BSRN sites. In addition, the global inter-comparison of the two LST products were presented and analyzed.

Land surface temperature (LST), a legacy (continuity) National Weather Service (NWS) user requirement, is also an essential climate Variable (ECV) required by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO). Therefore, LST is produced as one of the baseline products from the Geostationary Operational Environmental Satellites R-Series (GOES-R) mission. It is retrieved from observations of the Advanced Baseline Imager (ABI) thermal infrared channels, using a split-window approach. There are several LST products produced in the GOES-R hourly product package including a full-disk (FD) LST product at 10-km spatial resolution, a contiguous US (CONUS) LST product at 2-km spatial resolution, and LSTs for two adjustable mesoscale (MESO) sectors (e.g., 1000-km by 1000-km region) at 2-km spatial resolution. Comprehensive evaluation of GOES-16 LST products has been performed using visual inspection, in situ validation, and cross-satellite comparison analyses. The products meet the mission requirement of 2.5 K in accuracy and 2.3 K in precision, in most cases of the validation. Validation of a longer time series of data sets with multiyear seasonal and annual variations are needed to further evaluate the product's performance. This chapter details the LST product quality and its current status.

Satellite Land surface temperature (LST) is defined as the radiative skin temperature of the land. It has been widely used in many aspects of the geosciences, e.g., studies of net radiation budget at the Earth surface, monitoring state of crops and vegetation. It is an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. Thus, LST has been listed as an Essential Climate Variable (ECV) in the Global Climate Observation System (GCOS). LST is one of the baseline products for the GOES-R series satellites measured from the Advanced Baseline Imager (ABI). The algorithm derivation was developed at NOAA/NESDIS center for SaTellite Applications and Research (STAR), based on a traditional split-window technique. It is primarily estimated from the top-of-atmosphere (TOA) brightness temperature (BT) at one ABI thermal infrared channel and corrected by the BT difference to the near-by thermal infrared channel. Quality of the LST estimation may vary depending on cloud fraction, water vapor, view zenith angle, etc. Such quality information, recorded as quality flags and metadata, is provided with the LST product for user reference, product monitoring and evaluation analysis.Comprehensive evaluation of the GOES-R LST product has been conducted using radiative transfer simulation datasets and proxy ABI data, before the launch of the first GOES-R satellite (i.e. GOES-16). Since then we have performed its evaluation using over one year of the on-orbit ABI SDR and LST dataset, towards its beta and provisional validated maturity levels. Quality flags and metadata of the LST product are tested and verified with local independent computation. LST retrievals were compared to in-situ LST data derived from the SURFRAD station measurements. This presentation shows our evaluation results, as well as the ABI LST derivation details, which are helpful in users’ product applications

Land surface temperature (LST) and its diurnal variation are the critical factors in many aspects of climate study, surface energy balance, and environmental applications. Several satellite-based LST products are available for retrievals from regional to a global scale. However, due to the differences in sensor configurations and retrieval algorithms, these products may not necessarily be consistent. In this letter, the consistency of spatial and temporal skin temperature variations from two infrared satellite platforms has been evaluated over the contiguous United States (CONUS). Comparisons are made between the LST products from the newly launched Geostationary Operation Environmental Satellite R Series (GOES-R) advanced baseline imager (ABI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both Aqua and Terra satellites which are polar orbiting. Overall, both products show a general agreement in their diurnal variations with differences mostly under 2 K. However, a temperature-dependent inconsistency has been detected. The MODIS LST product seems to estimate higher temperatures in the summer months while the GOES product estimates higher temperatures during the winter months. Moreover, the maximum observed diurnal differences could reach up to 10 K in mountainous regions. The results suggest that the corresponding temperature differences should be accounted for when LST diurnal variations are compared or generated from satellite observations.

The Advanced Himawari Imager (AHI) onboard the recently launched next generation geostationary satellite, Himawari-8, provides an opportunity to improve Land Surface Phenology (LSP) detections over the Asia-Pacific region. In this paper, we detected four phenological transition dates (PTDs) using the three-day Two-band Enhanced Vegetation Index (EVI2) time series from AHI based on the Hybrid Piecewise Logistic Model-Land Surface Phenology Detection (HPLM-LSPD) algorithm. The four PTDs are Start of Spring (SOS), End of Spring (EOS), Start of Fall (SOF) and End of Fall (SOF). We evaluated the four AHI-derived PTDs against those detected using eight-day EVI2 time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting satellite Terra, and three-day Green Chromatic Coordinate (GCC) time series from the Phenological Eyes Network (PEN) at six sites in central and northern Japan. The evaluation was performed by conducting regression analyses, and calculating root mean square difference (RMSD) and bias between satellite- and PEN-derived PTDs. First, the difference in the spatial variations of SOS and EOF timing between naturally vegetated areas, and urban areas and croplands indicates the anthropogenic footprints on LSP. Second, the RMSD of either AHI PTDs or MODIS PTDs against PEN PTDs were higher in the fall (i.e., SOF and EOF) than those in spring (i.e., SOS and EOS). Third, the later EOS and earlier SOF derived from satellite EVI2 relative to those derived from PEN GCC might be caused by the difference in the sensitivity of GCC and EVI2 to the increases in leaf area index (LAI) over high-LAI canopies. Fourth, the higher temporal resolution of AHI EVI2 only helped reduce the RMSD during spring compared to the RMSD for MODIS. In contrast, the RMSD for AHI PTDs and MODIS PTDs were comparable in fall. Finally, the between-sensor correlation in the spatiotemporal variability of the four PTDs was higher for SOS and EOF than those for EOS and SOF.

The Earth's climate is largely determined by its energy budget. Since the 1960s, satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere (TOA) and the surface. Besides the broadband sensors that have been traditionally used for monitoring Earth's Energy Budget (EEB), data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components. This paper provides a comprehensive review of the satellite missions, state-of-the art estimation algorithms and the satellite products, and also synthesizes current understanding of the EEB and spatio-temporal variations. The TOA components include total solar irradiance, reflected shortwave radiation/planetary albedo, outgoing longwave radiation, and energy imbalance. The surface components include incident solar radiation, shortwave albedo, shortwave net radiation, longwave downward and upwelling radiation, land and sea surface temperature, surface emissivity, all-wave net radiation, and sensible and latent heat fluxes. Some challenges, and outlook such as virtual constellation of different satellite sensors, temporal homogeneity tests of long time-series products, algorithms ensemble, and products intercomparison are also discussed.