Now I can see the layout: green forest at top, cabin (warm W block) center-left, a large bright area (#/+) bottom — that's likely the pond/water but rendered bright. The dirt color I sampled may be the pond bottom showing through transparent water, or the pond is actually the bright area at bottom.

The map shows:
- Top: green (trees)
- Center-left: warm cabin (W)
- Bottom half: large bright (#/+) region — this is the pond/water area reflecting the bright sky

The pond water is there, but since it's fully transmissive at 0.04 roughness with transmission weight 1, it shows the bright sky reflection and the darker bottom. The sample at pond center (0.67,0.2 in NDC = row 1232, which is near the bottom) shows 0.42,0.37,0.30 — that's in the bright region but muted.

Let me render again and look at whether the water looks like water. The render needs a proper water look. Let me check the actual appearance with a crop of the bottom region, and also fix the camera to compose better. First, let me examine the water appearance in a crop.

import bpy, os
import numpy as np

p = r"<home>\AppData\Local\Temp\check_render4.png"
img = bpy.data.images.load(p)
w, h = img.size
px = np.array(img.pixels[:], dtype=float).reshape((h, w, 4))[..., :3]

# crop lower third of image
crop = px[int(h*0.65):, :, :]
print('lower third mean', crop.reshape(-1,3).mean(axis=0).round(3))
print('lower third std', crop.reshape(-1,3).std(axis=0).round(3))

# Check specific rows: row 1232 (pond center projection), columns around 1000-1100
row = 1232
band = px[row-40:row+40, 900:1150]
print('pond band mean', band.reshape(-1,3).mean(axis=0).round(3))
print('pond band std', band.reshape(-1,3).std(axis=0).round(3))
bpy.data.images.remove(img)