After the cyclone made landfall, which villages and farms near me got flooded?

What you can answer

• Post-landfall flood extent through thick cloud — OPERA DSWx-S1 is derived from Sentinel-1 radar, which sees the ground even under the cyclone’s cloud shield when optical sensors are blind.
• Open-water vs. partial / inundated-vegetation flooding — the DSWx-S1 water classification (WTR) layer flags open water, inundated vegetation, and low/high-confidence water separately, so flooded rice paddies and mangrove fringe show up, not just open ponds.
• Which named villages and farm parcels overlap the flood — clip the flood mask to an AOI around a settlement and intersect with OSM building/landuse polygons to list affected places.
• Roughly how many people are in the flooded area — overlay free WorldPop population (1 km) on the flood mask and count, then name the flooded districts with geoBoundaries (verified: ~16.3M people in the default delta box, district Pirojpur).
• Storm rainfall driving the flood — GPM IMERG Half-Hourly Early run gives a near-real-time (~4 h latency) rainfall accumulation over the landfall window to explain where water pooled.
• Inland flood signal during the storm itself — CYGNSS GPS-reflectometry surface-reflectivity rises sharply over standing water and penetrates the storm, giving a coarse “is it flooding right now” cue between Sentinel-1 overpasses.
• Watch the cyclone make landfall, every 10 minutes — Himawari-9 (geostationary, 140.7°E) full-disk imagery covers the Western Pacific and eastern Indian Ocean including the Bay of Bengal (at the western edge of its disk), so you can pin the eye and the exact landfall timing. Free, no login. (For the western Indian Ocean / Mozambique use EUMETSAT Meteosat; for the Americas, NOAA GOES.)

What you can NOT answer with these datasets alone

• Flood depth or “is my house’s ground floor underwater” — DSWx-S1 maps water presence, not depth. You need a DEM (e.g. Copernicus GLO-30) plus a hydraulic model (HEC-RAS) for depth.
• Building-by-building or crop-by-crop damage — 30 m DSWx pixels and ~25 km CYGNSS footprints are far too coarse; damage assessment needs commercial high-res (Maxar, Planet, ICEYE).
• The exact hour of peak flood — Sentinel-1 revisit is ~6–12 days at a given spot, so DSWx-S1 captures snapshots, not the rising/falling curve. CYGNSS fills gaps only coarsely.
• Urban interior / drainage flooding — radar struggles in built-up areas (corner reflections stay bright over water); DSWx flags dense-urban pixels as unreliable.
• Saltwater vs. freshwater inundation — these sensors see water, not salinity, which matters a lot for farmland recovery.

Code template (Python, cloud-direct)

Verified locally. OPERA DSWx-S1 coverage over the Bay of Bengal begins in 2024, so pick a recent cyclone — the window below is Cyclone Dana (landfall 25 Oct 2024). Each granule is a UTM-projected COG tile; the water layer is *_B01_WTR.tif. Download the WTR tiles, mosaic them, then reproject/clip to your AOI. (PO.DAAC occasionally returns a transient 502 on cloud reads — just retry.)

import earthaccess, glob
import rioxarray
from rioxarray.merge import merge_arrays
import xarray as xr
import numpy as np

earthaccess.login(strategy="netrc")

# Bay of Bengal landfall corridor — Khulna/Barisal delta, Bangladesh.
# Replace temporal window with the days right after YOUR cyclone's landfall.
aoi = (89.0, 21.5, 91.0, 23.0)              # (W, S, E, N)
post_window = ("2024-10-25", "2024-10-27")  # first Sentinel-1 passes after Cyclone Dana

# 1. OPERA DSWx-S1 — radar surface water, delivered as Cloud-Optimized GeoTIFFs.
results = earthaccess.search_data(
    short_name="OPERA_L3_DSWX-S1_V1",
    bounding_box=aoi,
    temporal=post_window,
)
print(f"Found {len(results)} DSWx-S1 granules")

# 2. Download the granules and keep only the water-classification layer (B01_WTR).
earthaccess.download(results, local_path="./dswx")
wtr_tiles = sorted(glob.glob("./dswx/*_B01_WTR.tif"))

# 3. Mosaic the UTM tiles, then reproject + clip to the AOI.
tiles = [rioxarray.open_rasterio(t, masked=False).squeeze() for t in wtr_tiles]
da = merge_arrays(tiles).rio.reproject("EPSG:4326").rio.clip_box(*aoi)

# DSWx-S1 WTR class values:
#   0 = not water, 1 = open water, 2 = partial surface water,
#   252 = inundated vegetation, 253 = high backscatter (uncertain),
#   254 = no data (layover/shadow), 255 = fill
flood_mask = da.isin([1, 2, 252])           # open water + partial + inundated vegetation

flooded_px = int(flood_mask.sum())
flooded_km2 = flooded_px * (0.030 * 0.030)  # DSWx-S1 native pixel is ~30 m
print(f"Flooded pixels: {flooded_px:,}  ≈ {flooded_km2:.1f} km^2")

# 4. GPM IMERG Half-Hourly EARLY run — fast rainfall to explain the flood.
imerg = earthaccess.search_data(
    short_name="GPM_3IMERGHHE",
    bounding_box=aoi,
    temporal=("2024-10-24", "2024-10-26"),  # Cyclone Dana landfall day(s)
)
imerg_ds = xr.open_mfdataset([earthaccess.open([g])[0] for g in imerg],
                             group="Grid")            # IMERG vars live in the /Grid group
storm_total_mm = (imerg_ds["precipitation"] * 0.5).sum("time")  # 0.5 h * mm/hr -> mm
print(f"Max storm rainfall: {float(storm_total_mm.max()):.0f} mm")

# 5. (Optional) CYGNSS L2 — coarse inland flood signal through the storm.
cygnss = earthaccess.search_data(
    short_name="CYGNSS_L2_V3.2",
    bounding_box=aoi,
    temporal=("2024-10-24", "2024-10-26"),
)
# Rising surface_reflectivity over normally-dry land -> standing water during the storm.

# 6. Who's affected — free WorldPop population + geoBoundaries place names (no NASA login)
import requests
from rasterio.windows import from_bounds
import rasterio, geopandas as gpd
from shapely.geometry import Point

meta = requests.get("https://www.worldpop.org/rest/data/pop/wpic1km?iso3=BGD").json()
pop_url = next(f for f in meta["data"][-1]["files"] if f.endswith(".tif"))
open("bgd_pop_1km.tif", "wb").write(requests.get(pop_url).content)   # ~1 MB

with rasterio.open("bgd_pop_1km.tif") as src:
    pop = src.read(1, window=from_bounds(*aoi, transform=src.transform)).astype("float64")
    pop[pop == src.nodata] = np.nan
print(f"People in AOI: {np.nansum(pop):,.0f}")        # verified ~16.3M for this delta box

adm = gpd.read_file(requests.get(
    "https://www.geoboundaries.org/api/current/gbOpen/BGD/ADM2/").json()["gjDownloadURL"])
print("District:", adm[adm.contains(Point(90.0, 22.3))].iloc[0]["shapeName"])   # 'Pirojpur'
# People *in the flood*: resample `pop` onto the flood grid and sum where flood_mask is True.

# 7. List affected places: intersect flood_mask with OSM village/landuse polygons
#    (geopandas overlay) to turn pixels into a named list for responders.

# 8. (free, no login) Watch the cyclone make landfall — Himawari-9 geostationary, 10-min full-disk.
#    NOAA mirrors it on a public S3 bucket; pin the eye + landfall hour before/around the flood pass.
import s3fs
fs = s3fs.S3FileSystem(anon=True)
# AHI L1b full-disk, band 13 (clean IR), nearest 10-min slot to landfall:
slot = "noaa-himawari9/AHI-L1b-FLDK/2024/10/25/0000"
files = [f for f in fs.ls(slot) if "B13" in f][:1]
print("Himawari scene files:", files)   # render with satpy: Scene(reader='ahi_hsd') -> true-colour / IR

Expected output

• Flood-extent map: DSWx-S1 water pixels (open water, partial, inundated vegetation) overlaid on a basemap, clipped to the village/farm AOI.
• Rainfall map: IMERG Early storm-total accumulation (mm) over the landfall window, showing where the water came from.
• Affected-places list: village names and farm/landuse parcels whose footprints intersect the flood mask, ranked by flooded area.
• People affected: estimated population in the flooded area (WorldPop), with the flooded districts named (geoBoundaries).
• Statistics: total flooded area (km²), max storm rainfall (mm), and a count of settlements touched.
• Optional CYGNSS track: a coarse during-storm reflectivity overlay confirming inland flooding before the first clear Sentinel-1 pass.

Caveats

• DSWx-S1 maps water presence, not depth — a flagged pixel says “wet,” not “how deep.”
• Snapshots, not a time series — Sentinel-1 revisit (~6–12 days) means you see the flood when a pass happens, possibly missing the peak.
• Compare to a pre-storm baseline — perennial rivers, ponds, and tidal flats appear as “water”; difference against a dry-season DSWx scene to isolate new flooding.
• Urban and densely-vegetated areas are unreliable — DSWx flags high-backscatter and layover/shadow pixels; treat them as “unknown,” not “dry.”
• CYGNSS is coarse (~25 km) — it’s a regional “is it flooding” cue, not a parcel-level map.

Cross-DAAC composition

ASF / PO.DAAC (OPERA DSWx-S1) + GES DISC (GPM IMERG Early) + PO.DAAC (CYGNSS L2) — one Earthdata Login across all three.

Sources

• OPERA DSWx-S1 product: https://www.jpl.nasa.gov/go/opera/products/dswx-product/
• OPERA DSWx-S1 in Earthdata Search: https://search.earthdata.nasa.gov/search?q=OPERA_L3_DSWX-S1_V1
• GPM IMERG (GES DISC): https://gpm.nasa.gov/data/imerg
• CYGNSS mission & data (PO.DAAC): https://podaac.jpl.nasa.gov/CYGNSS
• NASA Disasters cyclone/flood response: https://disasters.nasa.gov/
• WorldPop population (free, no login): https://www.worldpop.org/
• geoBoundaries (free CC-BY admin boundaries): https://www.geoboundaries.org/
• Himawari on AWS (NOAA open data, no login): https://registry.opendata.aws/noaa-himawari/
• EUMETSAT Meteosat (western Indian Ocean / Africa geostationary): https://www.eumetsat.int/