u/Nicholas_Geo

Hi, I switched to New look and I can't figure out how to specify two rows in the quick links. In the old look I could easily do that. Please see the attached images. In fact, there is no option to add a second row in the New look. Is that the case or I missed something?

Hi,

I am trying to install GWmodel3 from the source repository on my Linux system, but the compilation fails because the libgwmodel submodule is missing.

$  inxi -Sxxx
System:
  Host: Lenovo-Z50-70 Kernel: 6.17.0-35-generic arch: x86_64 bits: 64
    compiler: gcc v: 13.3.0 clocksource: tsc
  Desktop: MATE v: 1.26.2 wm: marco v: 1.26.2 with: mate-panel
    tools: mate-screensaver vt: 7 dm: LightDM v: 1.30.0
    Distro: Linux Mint 22.3 Zena base: Ubuntu 24.04 noble

Already installed dependencies GSL, sf and RcppArmadillo when I created the R project I am working on, many months ago.

What I did

1. Downloaded GWmodel3-master.zip from GitHub.
2. Unzipped into my R project folder that uses renv.
3. Ran R CMD build GWmodel3-master → the DESCRIPTION was OK, but the build process failed with:

​

make: *** No rule to make target 'libgwmodel/src/gwmodelpp/spatialweight/BandwidthWeight.cpp', needed by '.../BandwidthWeight.o'. Stop.
ERROR: compilation failed for package ‘GWmodel3’

I checked the unzipped folder and confirmed that libgwmodel is missing.
The .gitmodules file exists, but the submodule content is not included in the ZIP archive.

I also tried to install the package using pak,same issue.

> sessionInfo()
R version 4.6.0 (2026-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Linux Mint 22.3

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=el_GR.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=el_GR.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=el_GR.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=el_GR.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Paris
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

loaded via a namespace (and not attached):
[1] compiler_4.6.0    tools_4.6.0       rstudioapi_0.19.0 renv_1.2.3 

R version 4.6.0 (2026-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Linux Mint 22.3

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=el_GR.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=el_GR.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=el_GR.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=el_GR.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Paris
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

loaded via a namespace (and not attached):
[1] compiler_4.6.0    tools_4.6.0       rstudioapi_0.19.0 renv_1.2.3 

Hi everyone,

I am preprocessing GOES-16 ABI Level 2 Land Surface Temperature data (OR_ABI-L2-LSTC) using the terra package in R. I want to ensure I am correctly filtering out clouds and bad pixels so that only high-quality data remains over my study area. I have never worked with such dataset before.

When inspecting the NetCDF attributes of the DQF (Data Quality Flag) layer using ncdf4, the file returns these bitwise flag meanings:

> print(dqf_values)
 [1]  0  0  2  0  4  0  8  0 16  0 32
> print(dqf_meanings)
[1] "good_retrieval_qf valid_input_data_qf invalid_due_to_bad_or_missing_input_data_qf valid_clear_conditions_qf invalid_due_to_cloudy_conditions_qf valid_LZA_qf degraded_due_to_LZA_threshold_exceeded_qf valid_land_or_inland_water_surface_type_qf invalid_due_to_water_surface_type_qf valid_land_surface_temperature_qf invalid_due_to_out_of_range_land_surface_temperature_qf"

If I blindly filter using dqf == 0, all of my lakes get completely masked out because the satellite automatically triggers the water surface flag (Bit 4 = 16) for them (at least that what an LLM said), even if the pixel retrieval is perfectly clear and valid.

To fix this, I am switching to a native terra bitwise approach to explicitly target and mask only clouds, bad data, and out-of-range values, while purposefully letting the water flag pass through:

library(terra)
r <- rast("path_to_goes_file.nc")

lst_raw <- r[["LST"]]
dqf     <- r[["DQF"]]

# Bitwise checks using terra's native operators
bad_data   <- (dqf & 2) == 2
cloudy     <- (dqf & 4) == 4
out_range  <- (dqf & 32) == 32

# Mask out errors, but ignore Bit 4 (16) so lakes are preserved
good_pixels_mask <- !bad_data & !cloudy & !out_range

lst_masked  <- mask(lst_raw, good_pixels_mask, maskvalues = FALSE)
lst_celsius <- lst_masked - 273.15

1. Does this bitwise logic look solid for capturing valid inland water temperatures alongside land?
2. In the attached image, near bottom, it appears stripes, is this normal in GOES-16 LST after applying DFQ?

Thanks in advance for verifying!

> sessionInfo()
R version 4.6.0 (2026-04-24 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8    LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                           LC_TIME=English_United States.utf8    

time zone: Europe/Paris
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ncdf4_1.24   terra_1.9-27

loaded via a namespace (and not attached):
 [1] compiler_4.6.0    cli_3.6.6         ragg_1.5.2        tools_4.6.0       rstudioapi_0.19.0 Rcpp_1.1.1-1.1    codetools_0.2-20 
 [8] textshaping_1.0.5 lifecycle_1.0.5   rlang_1.2.0       systemfonts_1.3.2

Edit 1

This the newest version, what do you think? I am only interested in good quality pixels:

pacman::p_load(terra, ncdf4, tmap, stars)

f <- "path/OR_ABI-L2-LSTC-M3_G16_s20180010822197_e20180010824571_c20180010826159.nc"

# ------------------------------------------------------------
# 1. Read with stars (GDAL handles GOES fixed-grid projection)
# ------------------------------------------------------------
s <- read_stars(f, sub = c("LST", "DQF"))

lst_raw <- s["LST"]
dqf     <- s["DQF"]

# ------------------------------------------------------------
# 2. DQF mask (still in Kelvin)
# ------------------------------------------------------------
dqf_arr   <- dqf[[1]]
bad_data     <- bitwAnd(dqf_arr,  2L) == 2L   # bad/missing input
cloudy       <- bitwAnd(dqf_arr,  4L) == 4L   # cloudy
lza          <- bitwAnd(dqf_arr,  8L) == 8L   # degraded LZA threshold
# water        <- bitwAnd(dqf_arr, 16L) == 16L  # water surface — was MISSING, if I want to remove water
out_range    <- bitwAnd(dqf_arr, 32L) == 32L  # out of range LST

# Water flag (16) is intentionally excluded to keep lakes
good_mask <- !(bad_data | cloudy | lza | out_range)
good_mask[is.na(good_mask)] <- FALSE

lst_k <- lst_raw
lst_k[[1]][!good_mask] <- NA

names(lst_k) <- "LST_K"

I am downscaling (i.e., increasing the spatial resolution) VIIRS nighttime lights imagery. The transfer function between the coarse and fine resolution is approximated by a Gaussian filter whose width σ varies with the per‑column viewing angle θ. This is an analytical, geometric relationship, not an estimate.

In the cross‑track direction (left→right), the sigma scales as

σ(θ)=σ_0/cos^2θ

where θ is the per‑pixel viewing angle. The filter should act only horizontally (along x), pooling all y‑values within the column uniformly. My earlier attempt simply multiplied the raster values by

1/cos^2θ

Given a value raster x and an angle raster theta with identical dimensions, what is the correct and efficient way in R (using terra) to apply a 1‑D Gaussian blur on each row, where the kernel’s standard deviation is determined by theta at the central pixel’s column?

Current (incomplete) attempt:

library(terra)

# ---- 1. Create example rasters ----
set.seed(42)
nrows <- 5
ncols <- 200

x <- rast(nrows = nrows, ncols = ncols, vals = runif(nrows * ncols))
theta_vals <- rep(seq(20, 26, length.out = ncols), each = nrows)
theta <- rast(nrows = nrows, ncols = ncols, vals = theta_vals)

# ---- 2. Spatially varying Gaussian blur function ----
# sigma0 = standard deviation of Gaussian at nadir (in pixel units)
# theta: raster of angles in degrees (same dimensions as x)
anisotropic_blur <- function(x, theta, sigma0 = 10) {
  # Convert to matrix (rows = y, cols = x)
  mat_x <- as.matrix(x, wide = TRUE)
  mat_angle <- as.matrix(theta, wide = TRUE)  # same dimensions
  nr <- nrow(mat_x)
  nc <- ncol(mat_x)
  mat_out <- matrix(NA_real_, nr, nc)
  
  # For each row, apply convolution with column-dependent sigma
  for (r in 1:nr) {
    row_vals <- mat_x[r, ]
    for (c in 1:nc) {
      theta_c <- mat_angle[r, c]               # angle at this pixel (degrees)
      sigma <- sigma0 / (cos(theta_c * pi/180)^2)
      halfwin <- ceiling(3 * sigma)            # kernel half‑width
      col_idx <- max(1, c - halfwin) : min(nc, c + halfwin)
      dist <- abs(col_idx - c)
      w <- exp(-0.5 * (dist / sigma)^2)
      w <- w / sum(w)
      mat_out[r, c] <- sum(w * row_vals[col_idx])
    }
  }
  # Return as raster with same properties
  rast(mat_out, crs = crs(x), ext = ext(x))
}

# ---- 3. Apply the filter ----
x_blurred <- anisotropic_blur(x, theta, sigma0 = 10)

# ---- 4. Plot side‑by‑side ----
par(mfrow = c(1, 2))
plot(x, main = "Original predictor")
plot(x_blurred, main = expression("Filtered: Gaussian "*sigma*" = "*sigma[0]/cos^2*theta))

I need to actually perform a 1‑D Gaussian convolution along rows with a column‑varying (σ). What is an idiomatic way to do this in terra?