Global Land and Sea-ice Surface Albedo Map (26 July 2019) Observed from NOAA-20 Satellite - click to enlarge
Team Lead: Yunyue (Bob) Yu
Surface Albedo (SURFALB), defined as the ratio between solar radiation reflected by Earth's surface and solar radiation incident at the surface, is a function of both solar illumination and the surface reflective properties. SURFALB is an essential variable linking the earth surface and the climate system. It is a unique property for studying how surface changes affect the energy balance and the overall climate system.
The first version VIIRS albedo product has been generated routinely from IDPS algorithm in the form of an environmental data record (VIIRS_EDR) since 2014 and named VIIRS Land Surface Albedo (LSA). Since 2019, the surface albedo producing has been officially updated to enterprise algorithm with the release of the Level-2 (L2) SURFALB product as VIIRS granule product (JPSS_GRAN). The official JPSS Surface Albedo EDR products can be accessed from CLASS. Since the launch of NOAA-20, the enterprise algorithm has two main updates. The maturity status of the NOAA-20 product generation is defined as beta, provisional and validated versions. The SURFALB beta and provisional productions were approved in July 2018 and March 2019, respectively. Validation results show the errors of the current SURFALB retrievals are well smaller than L1RD threshold.
Algorithm Science and Data Access
Products and data:
EDR Long Term-Monitoring
- Albedo ATBD, (PDF, 2.64 MB)
The VIIRS surface albedo is retrieved with a direct estimation method, which directly links surface broadband albedo with VIIRS TOA reflectance through statistical modeling. The training data used to establish the regression models are obtained through simulation of physical models. The first version of IDPS algorithm uses a spectral library as the input for atmospheric radiative transfer simulation. An assumption of Lambertian surface is implied in the process. It has been found that this simplification of surface reflectance will result in retrieval uncertainties and lead to angular dependence in some cases. To address this issue, a new look-up table (LUT) that considers the anisotropy of surface reflectance was established. A band construction method is used to obtain a BRDF database in VIIRS bands from MODIS BRDF data, where VIIRS reflectance is expressed as the linear combination of MODIS spectral reflectances. To obtain a representative training dataset, MODIS BRDF products with the highest quality are collected over various surface types throughout the year. The surface bidirectional reflection factor (BRF) in predefined angular bins is converted from MODIS bands to VIIRS bands for each record in the MODIS BRDF database. The derived VIIRS BRF data will be used as inputs to atmospheric radiative transfer to obtain TOA reflectance in these bins for various atmospheric conditions. Linear regression is then performed to obtain the coefficients that relate albedo to TOA reflectance. The regression coefficients are stored in a LUT, indexed by viewing geometry. In the enterprise version algorithm, the team made three main updates given the necessity of albedo in energy balance calculation: 1) updated the output from instantaneous albedo to daily-mean blue-sky albedo; 2) added sea-ice albedo coverage; and 3) designed an offline component to provide reliable fill value for cloudy pixels and gap-free albedo products.
The current operational L2 albedo algorithm produce granule-based albedo data which is distributed as a swath. The irregular grid in granule product is not convenient to use since the pixels have different sizes and varying latitude and longitude coordinates. Thus, the L2 albedo product will be further processed into a grid-based L3 albedo product as required. The L3 algorithm has passed the Critical Design Review (CDR), Test Readiness Review (TRR) in NOAA board, and will be operational after Algorithm Readiness Review (ARR), and Operational Readiness Review (ORR).
Scientists and modelers in many fields may need the SURFALB data, including NOAA NWS Environmental Modeling Center, USDA Agricultural Research Services, USDA Forest Service, STAR, and NCDC as well as universities and other research institutions throughout the world.
Calibration and Validation
The team has collected ground albedo measurements from various monitoring networks across the world for the validation of VIIRS albedo products. The surface stations, including AmeriFlux, BSRN, GC-Net, SURFRAD, and PROMICE, were used to validate their performance. High spatial-resolution satellite imagery was then used to evaluate the spatial representativeness of the ground measurements. The validation results from the selected spatially representative sites suggested that VIIRS albedo can meet the L1RD requirement and have comparable accuracy with MODIS albedo product (Wang et al., 2013, 2016; Peng et al., 2018; Zhou et al., 2016).
Although the VIIRS SURFALB has passed criteria of the provisional V1 release, the team is still working hard on some critical improvements. The planned algorithm improvement includes:
- Integrate the gridded albedo software package into the operational system;
- Validate and calibrate the algorithm to reduce the uncertainty in snow albedo retrievals ;
- Facilitate the generation of blended albedo product from both S-NPP and NOAA-20 observations;
- Improve the validation reliability through high-resolution albedo assistance;
- Promotion of applications of the JPSS albedo products with further contributions to weather services
Peng, J. J., Yu, Y. Y., Yu, P., & Liang, S. L. (2018). The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation. Remote Sensing, 10(11). [10.3390/rs10111826]
Wang, D. D., Liang, S. L., He, T., & Yu, Y. Y. (2013). Direct Estimation of Land Surface Albedo from VIIRS Data: Algorithm Improvement and Preliminary Validation. Journal of Geophysical Research-Atmospheres, 118(22), 12577-12586. [10.1002/2013jd020417]
Wang, D. D., Liang, S. L., Zhou, Y., He, T., & Yu, Y. Y. (2017). A New Method for Retrieving Daily Land Surface Albedo from VIIRS Data. IEEE Transactions on Geoscience and Remote Sensing, 55(3), 1765-1775. [10.1109/tgrs.2016.2632624]
Zhou, Y., Wang, D., Liang, S., Yu, Y., & He, T. (2016). Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps. Remote Sensing, 8(2). [10.3390/rs8020137]