Issue with ee_as_rast create raster with NaN

[rpkamakura]

• rgee version: 1.1.7
• R version: 4.3.2
• Operating System: Windows 10

At submit an issue, please attached the following information of your rgee session:

• [ Yes] You have the Python API installed (from terminal):

earthengine -h

• [ NO] You can find the credentials file on your system:

library(rgee)
ee_path <- path.expand("~/.config/earthengine/credentials")
file.exists(ee_path)

• [ Yes] You can run a simple EE command from R:

library(rgee)

# Initialize the Earth Engine module.
ee_Initialize()

# Print metadata for a DEM dataset.
print(ee$Image('USGS/SRTMGL1_003')$getInfo())

Attach your Python (reticulate) configuration:

library(reticulate)
py_config()

python:         C:/Users/rep31/AppData/Local/anaconda3/python.exe
libpython:      C:/Users/rep31/AppData/Local/anaconda3/python310.dll
pythonhome:     C:/Users/rep31/AppData/Local/anaconda3
version:        3.10.9 | packaged by Anaconda, Inc. | (main, Mar  1 2023, 18:18:15) [MSC v.1916 64 bit (AMD64)]
Architecture:   64bit
numpy:          C:/Users/rep31/AppData/Local/anaconda3/Lib/site-packages/numpy
numpy_version:  1.23.5
ee:             C:\Users\rep31\AppData\Local\ANACON~1\lib\site-packages\ee\__init__.p

NOTE: Python version was forced by RETICULATE_PYTHON

Description

I'm trying to save several images as rasters via drive. Whenever I try, it doesn't give me any errors but returns rasters with only NaN values. The ideal images I want to save are mostly empty with just a few areas with values (clipped to smaller polygons) that are disjoint, but I also tried saving them while clipping to a larger geometry (geeBbox below) and had the same problem.

The issue doesn't seem to be on the earth engine side because I can do Map$addLayer(image) for the version of the images right before I run ee_as_rast(image) and the images look great before conversion.

I'm going to put the simpler code here that just pulls NAIP imagery since that is easier to reproduce, but I have the same problem with some processed Sentinel-2 imagery.

What I Did

#create our focal region (for output geometry, is from a shapefile)
Region <- st_read("../01Data/ChicagoS23/FocalRegion/FocalRegion.shp")
bbox <- st_bbox(Region)
bbox_Poly <- st_as_sfc(bbox)
geeBbox <- ee$Geometry$Rectangle(bbox)

NAIP <- ee$ImageCollection("USDA/NAIP/DOQQ")$
  filterBounds(geeBbox)$
  filterDate("2019-01-01", "2023-12-31")

#to use to filter the images
randomPts_center <- ee$FeatureCollection$randomPoints(region=geeBbox, points= 100, seed= 0, maxError= 1)
randomPoints <- randomPts_center$map(function(f) {f$buffer(60, 10)})

#get NAIP Imagery
N19 <- ee$ImageCollection(NAIP)$filterDate("2019-08-01", "2019-08-10")$mosaic()$clip(randomPoints)
N21<- ee$ImageCollection(NAIP)$filterDate("2021-09-01", "2021-09-15")$mosaic()$clip(randomPoints)

#switch them to terra rasters
rastN19 <- ee_as_rast(N19, region=geeBbox, via = "drive")
rastN21 <- ee_as_rast(N21, region=geeBbox, via = "drive")

##write the rasters
writeRaster(rastN19, "../01Data/QualityCheck/NAIP_2019.tiff")
writeRaster(rastN21, "../01Data/QualityCheck/NAIP_2021.tiff")