How primed for wildfire is the land around me right now?

Wildfire needs fuel, and the driest fuel of all is vegetation that has stopped growing and started to cure — grass that has gone from green to gold, brush that has spent its summer water. NASA’s MOD13A2 gives a 16-day, 1 km map of NDVI (the Normalized Difference Vegetation Index) everywhere on land, so you can watch greenness fall through a dry season and see which hillsides have turned to tinder — even with no fire-weather station nearby.

Verified locally. For Northern California wine country (38.3–38.7 °N), the MOD13A2 1 km 16 days NDVI field averaged 0.413 across the AOI for the 1–16 September 2024 composite (1,786 valid 1 km pixels) — moderate greenness, the signature of cured grassland mixed with still-green vineyards and oak. NDVI alone is not a fire forecast: it tells you how dry the fuel is. The full danger picture pairs it with surface heat (MOD11 LST) and plant water stress (evapotranspiration, MOD16 or ECOSTRESS) — and, for an actual warning, the fire-weather agencies.

What you can answer

• How green (or cured) the vegetation is right now, and every 16 days back to 2000 — MOD13A2 NDVI (stored as integers; multiply by 0.0001 for true NDVI)
• Whether fuel is drier than a normal year — build a per-composite climatology from past years and plot this season’s NDVI anomaly (a deep negative anomaly means unusually cured, flammable vegetation)
• Where the dry patches are — at 1 km you can see which slopes, grasslands, or burn scars have lost their green while irrigated fields stay lush
• How fast green-down is happening — the slope of NDVI through late summer shows fuel curing in real time, the lead-up to peak fire season
• The heat-and-stress context — overlay MOD11 land surface temperature and MOD16/ECOSTRESS ET to add the surface-heat and water-stress dimensions of fire danger

What you can NOT answer with these datasets alone

• Whether a fire will start or spread — NDVI is a fuel-dryness proxy, not an ignition or spread model; that needs wind, humidity, fuel-moisture sticks, and fire-behavior tools (e.g. NFDRS)
• Live fuel moisture in percent — NDVI correlates with it but is not a calibrated moisture reading; field sampling or specialized retrievals are needed for the actual number
• Fine-scale fuel structure — 1 km cannot see a single dry ridgeline, a defensible-space gap, or ladder fuels under a canopy; pair with higher-resolution imagery (Sentinel-2, Landsat)
• The 16-day blind spot — MOD13A2 is a 16-day composite, so a sudden heat wave or wind event that cures fuel in days won’t show up until the next composite
• Active fires or smoke — that’s a different product (VIIRS/MODIS active-fire and FRP), not NDVI

Code template (Python, cloud-direct)

Verified locally. MOD13A2, variable 1 km 16 days NDVI, ships as HDF-EOS2 (HDF4) tiles in the MODIS Sinusoidal grid. The bundled GDAL in many environments lacks the HDF4 driver, so the most reliable read is pyhdf directly: pull the NDVI grid, multiply by 0.0001, mask the -3000 fill, and convert the sinusoidal pixel centers to lon/lat to clip your AOI. Lower / declining NDVI in the dry season = drier fuel.

import os, re, warnings, earthaccess, numpy as np
from pyhdf.SD import SD, SDC
warnings.filterwarnings("ignore")

# load Earthdata creds from .env without `source` (passwords can break the shell)
for line in open(".env"):
    m = re.match(r'\s*(?:export\s+)?([A-Z0-9_]+)\s*=\s*(.*)\s*$', line)
    if m: os.environ.setdefault(m.group(1), m.group(2).strip().strip('"').strip("'"))
earthaccess.login(strategy="environment")   # free Earthdata Login

W, S, E, N = -122.4, 38.3, -122.0, 38.7     # your land (NorCal wine country)
g = earthaccess.search_data(short_name="MOD13A2",
                            temporal=("2024-09-01", "2024-10-15"),
                            bounding_box=(W, S, E, N))
hdf = str(earthaccess.download(g[:1], local_path="/tmp/q43")[0])

sd  = SD(hdf, SDC.READ)
sds = sd.select("1 km 16 days NDVI")
dn  = sds[:].astype("float32")
fill, scale = sds.attributes()["_FillValue"], sds.attributes()["scale_factor"]  # -3000, 10000

# grid corners (sinusoidal meters) from the tile metadata
sm = sd.attributes()["StructMetadata.0"]
ulx, uly = [float(v) for v in re.search(r"UpperLeftPointMtrs=\(([^)]+)\)", sm).group(1).split(",")]
lrx, lry = [float(v) for v in re.search(r"LowerRightMtrs=\(([^)]+)\)",  sm).group(1).split(",")]
ny, nx = dn.shape
R = 6371007.181  # MODIS sinusoidal sphere radius (m)
xc = ulx + (np.arange(nx) + 0.5) * (lrx - ulx) / nx
yc = uly + (np.arange(ny) + 0.5) * (lry - uly) / ny
X, Y = np.meshgrid(xc, yc)
LAT = np.degrees(Y / R)
LON = np.degrees(X / (R * np.cos(np.radians(LAT))))

mask = (LON >= W) & (LON <= E) & (LAT >= S) & (LAT <= N)
ndvi = np.where(dn == fill, np.nan, dn / scale)
print("mean AOI NDVI:", round(float(np.nanmean(ndvi[mask])), 4))   # -> 0.4126 for Sep 2024
# lower & falling through the dry season = drier, more fire-primed fuel