VIIRS LAI/FPAR (VNP15)¶

VIIRS Leaf Area Index / FPAR (VNP15A2H, VJ115A2H, VJ215A2H) Version 2 (500 m, 8-day composites)

Available

At a glance¶

Note on period: VNP15A2H (Suomi NPP) starts in January 2012 (the first file for the Korean tile is the composite beginning 2012-01-17), VJ115A2H (NOAA-20) on 2018-01-01 and VJ215A2H (NOAA-21) on 2023-02-10. A CMR check of the Korean tile (h28v05) on 2026-10-13 found the 2026-09-14 to 21 composite was the latest for all three. VNP15A2H is expected to stop when Suomi NPP data delivery ends in November 2026.

Overview¶

Global leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) produced by porting the MODIS LAI/FPAR algorithm to the VIIRS sensor to continue the MODIS record. VIIRS red (I1) and near-infrared (I2) reflectances are inverted with sensor-specific 3D radiative-transfer look-up tables, with an empirical NDVI backup when the main algorithm fails. It uses the same 500 m sinusoidal tiles and 8-day compositing as MODIS, so the two can be compared directly, and is issued separately for Suomi NPP, NOAA-20 and NOAA-21.

What changes with the successor¶

VIIRS LAI/FPAR V1 (VNP15A2H V001) → VIIRS LAI/FPAR V2 (VNP15A2H, VJ115A2H, VJ215A2H V002)¶

Transition: 2023-08

Version 2 (Suomi NPP and NOAA-20) was released in August 2023; V1 production stopped on 17 June 2024 and its distribution ended on 8 April 2025. A NOAA-21 product (VJ215A2H) was added to V2 in February 2026.

Sources: VIIRS Version 2 LAI/FPAR products released (Earthdata notice, 2023-08-21) · VIIRS V1 SR, VI, LAI/FPAR, LST and Active Fires products decommissioned (Earthdata notice, 2025-04-08)

Available variables¶

From LP DAAC collection information and the VNP15 User Guide. All three satellite products use the same names, which, unlike MODIS, have no _500m suffix. Values are stored as unsigned 8-bit integers that must be multiplied by the scale factor, and 249–255 are non-vegetation and fill codes.

instantaneous mean accumulated maximum minimum static — instantaneous: value at the time stamp · mean/accumulated/maximum/minimum: over the indicated interval · static: does not change with time

LAI and FPAR (500 m, 8-day composites)¶

The full variable list is in the official documentation.

How to get the data¶

• NASA Earthdata Search — VJ215A2H (NOAA-21, recommended) — Web search and HTTPS download / earthaccess
  • Short names are VJ215A2H (NOAA-21), VJ115A2H (NOAA-20) and VNP15A2H (Suomi NPP), version 002. Files are HDF-EOS5 (.h5) tiles. Because Suomi NPP data delivery ends in November 2026, LP DAAC recommends VJ215A2H as primary and VJ115A2H as secondary.
• NASA Earthdata Search — VNP15A2H (Suomi NPP, 2012 onward) — Web search and HTTPS download / earthaccess
  • Only the Suomi NPP product covers 2012–2017. Existing files appear to remain downloadable, but no distribution policy for after delivery ends has been announced yet.
• AppEEARS (area and point extraction) — Web and API — area (GeoTIFF, NetCDF4) or point (CSV) extraction with optional reprojection
  • Delivers just your region without having to handle tiles or the HDF5 structure.

Example code¶

Downloads NOAA-20 VJ115A2H for July 2024 with earthaccess and reads LAI from the central Korean tile (h28v05). To avoid hard-coding paths inside the HDF-EOS5 file, h5py is used to find the datasets named Lai and FparLai_QC. Unlike MODIS, SCF_QC sits in bits 0–2.

# pip install earthaccess h5py numpy
import earthaccess
import h5py
import numpy as np

earthaccess.login()  # free NASA Earthdata account
results = earthaccess.search_data(
    short_name="VJ115A2H", version="002",
    temporal=("2024-07-01", "2024-07-31"),
    bounding_box=(126.5, 35.5, 128.5, 37.5),   # (west, south, east, north)
)
files = earthaccess.download(results, "./vj115a2h")

def find(h5, name):
    hits = []
    h5.visititems(lambda k, v: hits.append(k) if isinstance(v, h5py.Dataset) and k.endswith("/" + name) else None)
    return h5[hits[0]][()]

for f in sorted(map(str, files)):
    if ".h28v05." not in f:
        continue
    with h5py.File(f, "r") as h5:
        raw = find(h5, "Lai").astype("float32")
        qc = find(h5, "FparLai_QC")
    lai = np.where(raw <= 100, raw * 0.1, np.nan)   # 249-255 = non-vegetation / fill
    scf = qc & 0b111                                 # 0 or 1 = main (RT) algorithm (bit position differs from MODIS)
    lai[scf > 1] = np.nan
    print(f.split("/")[-1], "mean LAI:", np.nanmean(lai))

Practical tips¶

• The FparLai_QC bit layout differs from MODIS. In VIIRS, bits 0–2 are SCF_QC (0 main algorithm, 1 main algorithm with saturation, 2 and 3 NDVI backup, 4 not produced), bit 3 is dead detector and bits 4–7 are biome. Reusing the MODIS (qc >> 5) & 7 code ruins the quality screening.
• Always remove stored values 249–255 (255 fill, 254 inland water, 253 barren or sparse vegetation, 252 permanent snow and ice, 251 wetland, 250 urban, 249 unclassified) and apply the scale factors (LAI × 0.1, FPAR × 0.01).
• VNP15A2H (Suomi NPP) ends in November 2026. VJ115A2H continues from 2018 and VJ215A2H from 2023, so choose a reference satellite for long-term work in advance and state where the satellite changes.
• In Boston University's comparison (VIIRS V1), VIIRS differed more from MODIS where reflectance saturates, such as tropical forest, and its main-algorithm success rate varied more between years because of input reflectance issues. Read interannual changes in dense forest together with the quality flags.
• The biome type in bits 4–7 lets you stratify statistics into forest, grassland, cropland and so on without a separate land-cover product. Note that this is the algorithm's eight-biome input classification, not a general land-cover map.
• The files are HDF5 (HDF-EOS5), so MODIS pyhdf code will not open them. Use h5py, xarray (h5netcdf) or GDAL.

Notices and change history¶

References for your paper¶

Dataset citation¶

VJ215A2H V002 (NOAA-21) — DOI 10.5067/VIIRS/VJ215A2H.002

@misc{vj215a2h_v002,
  author    = {Myneni, Ranga},
  title     = {{VIIRS/JPSS2} Leaf Area Index/{FPAR} 8-Day {L4} Global 500m {SIN} Grid {V002}},
  year      = {2026},
  publisher = {NASA Land Processes Distributed Active Archive Center},
  doi       = {10.5067/VIIRS/VJ215A2H.002}
}

VJ115A2H V002 (NOAA-20) — DOI 10.5067/VIIRS/VJ115A2H.002

@misc{vj115a2h_v002,
  author    = {Myneni, Ranga},
  title     = {{VIIRS/JPSS1} Leaf Area Index/{FPAR} 8-Day {L4} Global 500m {SIN} Grid {V002}},
  year      = {2023},
  publisher = {NASA Land Processes Distributed Active Archive Center},
  doi       = {10.5067/VIIRS/VJ115A2H.002}
}

VNP15A2H V002 (Suomi NPP) — DOI 10.5067/VIIRS/VNP15A2H.002

@misc{vnp15a2h_v002,
  author    = {Myneni, Ranga},
  title     = {{VIIRS/NPP} Leaf Area Index/{FPAR} 8-Day {L4} Global 500m {SIN} Grid {V002}},
  year      = {2023},
  publisher = {NASA Land Processes Distributed Active Archive Center},
  doi       = {10.5067/VIIRS/VNP15A2H.002}
}

Reference papers¶

VIIRS LAI/FPAR algorithm and implementation — DOI 10.1109/TGRS.2017.2775247

@article{yan2018viirslai,
  author  = {Yan, Kai and Park, Taejin and Chen, Chi and Xu, Baodong and Song, Wanjuan and Yang, Bin and Zeng, Yelu and Liu, Zhao and Yan, Guangjian and Knyazikhin, Yuri and Myneni, Ranga B.},
  title   = {Generating Global Products of {LAI} and {FPAR} From {SNPP-VIIRS} Data: Theoretical Background and Implementation},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume  = {56},
  number  = {4},
  pages   = {2119--2137},
  year    = {2018},
  doi     = {10.1109/TGRS.2017.2775247}
}

VIIRS–MODIS LAI/FPAR consistency (2012–2016) — DOI 10.3390/f9020073

@article{xu2018viirsmodis,
  author  = {Xu, Baodong and Park, Taejin and Yan, Kai and Chen, Chi and Zeng, Yelu and Song, Wanjuan and Yin, Gaofei and Li, Jing and Liu, Qinhuo and Knyazikhin, Yuri and Myneni, Ranga B.},
  title   = {Analysis of Global {LAI/FPAR} Products from {VIIRS} and {MODIS} Sensors for Spatio-Temporal Consistency and Uncertainty from 2012--2016},
  journal = {Forests},
  volume  = {9},
  number  = {2},
  pages   = {73},
  year    = {2018},
  doi     = {10.3390/f9020073}
}

Acknowledgements¶

Following the EOSDIS Data Use and Citation Guidance, LP DAAC asks you to cite the DOI of each satellite product you used (VNP15A2H, VJ115A2H, VJ215A2H). For the algorithm, also cite Yan et al. (2018); for continuity with MODIS, Xu et al. (2018).

Information on the datasets used in this study was compiled with the help of the Korean Climate Data Navigator (KCDN) (Sim, 2026; https://doi.org/10.5281/zenodo.23300923).

Links¶

• VNP15 User Guide V1.2 (PDF)
• VNP15 Algorithm Theoretical Basis Document (ATBD, PDF)
• Earthdata catalog — VNP15A2H V002
• Earthdata catalog — VJ215A2H V002
• VIIRS LAI/FPAR validation page (VIIRS Land Team)
• NOAA NESDIS — cessation of Suomi NPP data delivery