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The VIIRS global land surface phenology (GLSP) product aims for a science quality data standard that will enable continuity of a key Earth system data record from the VIIRS time series. This product provides consistent spatial and temporal estimates of the timing and magnitude of phenological development of the vegetated land surface across the globe, that is suitable for characterizing and understanding interannual-to-decadal scale changes in ecosystem responses to changes in the environment. The VIIRS Collection 1 (C1) GLSP product (VNP22Q2), at a spatial resolution of 500m, is produced using an algorithm refined from the Collection 5 MODIS product, which contains twelve phenological metrics (seven phenological dates and five phenological magnitudes), along with six quality assurance metrics characterizing the confidence of phenology retrievals for each pixel. The six phenophase transition dates in the VIIRS GLSP product are closely comparable to PhenoCam observations (Zhang et al., 2018a) and Landsat detections (30m) with a mean absolute difference of less than 10 days (Zhang et al., 2018b).

The VIIRS GLSP algorithm uses as inputs daily VIIRS Nadir BRDF (bidirectional reflectance distribution function)-Adjusted Reflectance (NBAR) data in combination with land surface temperature, snow cover, and land cover type at each pixel. It reconstructs the temporal trajectory of two band enhanced vegetation index (EVI2) to characterize the seasonal variation of land surface greenness using a physically-based hybrid piecewise logistic model.

Where t is time in the day of year (DOY), a is related to the vegetation growth time, b is associated with the rate of plant leaf development, c is the amplitude of EVI2 variation, d is the vegetation stress factor, and EVI2b is the background value. This model is used to retrieve all the GLSP metrics on an annual basis.

The products are provided in standard Hierarchical Data Format–Earth Observing System (HDF-EOS5) format. Currently, this collection is available from January 19, 2012 and forward. Products from the VIIRS sensor aboard JPSS-1 (J1) will be also be available starting in mid-2023.

References

• Zhang, X., Jayavelu, S., Liu, L., Fried, M., Henebry, G., Liu, Y., Schaaf, C., Richardson, A., Gray, J. (2018a). Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery. Agriculture and Forest Meteorology, Volumes 256-257, 15 June 2018, Pages 137-149.
• Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G.M., Friedl, M.A., Schaaf, C.B., (2018b). , Remote Sensing of Environment, Volume 216, 2018, Pages 212-229, ISSN 0034-4257.

The Blended-BBEP is a continuous product of biomass burning emissions, which is used to replace previous operational product. Previously, the Geostationary Operational Environmental Satellite Biomass Burning Emission Products (GBBEP) are produced from GOES-E and GOES-W fire products separately beginning in 2008. In these products, each emission species is stored in an individual ASCII file. Current Blended-BBEP is produced by blending fires detected from GOES-E, GOES-W, MODIS, and AVHRR. The outputs are written in both ASCII and netCDF files which include burned area and all emission species (PM2.5, CO, CH4, CO2, TNMHC, NH3, N2O, NOX and SO2).

Two burned area images are generated for visually monitoring the performance of the Blended-BBEP product. One is the spatial distribution of burned area, and the other is the statistic histogram of burned area. These two images provide the basic information of product quality. Specifically:

• Quality of biomass burning emissions is assessed using the spatial pattern of fire distribution, particularly looking for artifacts or non-physical behaviors (e.g., fires over water).
• Monitoring burned area statics for small, medium and large fires over different ecosystems.

The Blended-BBEP is running for North America and produced every six hours.

This dataset provides a reference of land surface phenology (LSP) at 30-m pixels for 78 regions of 10 x 10 km2 across a wide range of ecological and climatic regions in North America during 2019 and 2020. The data were derived by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near- surface PhenoCam time series (hereafter called HP-LSP). The HP-LSP dataset consists of two parts: (1) the 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series and (2) four key phenological transition dates that are greenup onset, maturity onset, senescence onset, and dormancy onset (accuracy less than or equal to five days). The PhenoCam network offers near-surface observations via the RGB (Red, Green, and Blue) imagery every 30 minutes. Each RGB imagery enables us to calculate as many as 100 Green Chromatic Coordinate (GCC) for generating a collection of localized vegetation dynamics. The HLS EVI2 time series with frequent gaps was fused with the most comparable PhenoCam GCC temporal shape selected from the GCC collection using the Spatiotemporal Shape Matching Model (SSMM) to create the synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to establish the physically-based hybrid piecewise logistic model (HPLM) for detecting phenological transition dates (phenometrics).

References

• Tran, K.H., X. Zhang, A.R. Ketchpaw, J. Wang, Y. Ye, and Y. Shen. 2022. . Remote Sensing of Environment 282:113275.
• Tran, K.H., X. Zhang, Y. Ye, Y. Shen, S. Gao, Y. Liu, and A. Richardson. 2023. HP-LSP: . Sci Data.10: 691, 2023.