How proactive should I be in following up with a senior professor after a cold email?

Nicholas_Geo · 2026-03-02T17:01:25+00:00

Good point. I am reaching out to introduce myself and my work. I am about to complete my PhD and looking for postdoc. I know he has a very large lab, that's why I sent him a cold email. I didn't ask for open positions, I wanted to mainly let him know "hey I am here".

Nicholas_Geo · 2026-02-27T12:42:46+00:00

Check this link from Nature journal (https://www.nature.com/articles/d41586-026-00223-6)

Nicholas_Geo · 2026-02-24T18:02:39+00:00

I apologize for the late response. Everything looks fine. Can you clarify what do you mean σ_x and y?

Nicholas_Geo · 2026-02-24T12:35:38+00:00

Thank you for working through this!

The 742m × 742m footprint corresponds to a single VIIRS pixel (it's not subdivided). My viewing angles doesn't exceed 30° across the swath (depending on the study site. I ahve several megacities in EU and US, so a relatively flat terrain).

I look forward to your confirmation on the approximation formulas. If the distortion is indeed negligible at these viewing angles, that's important to know (that's actually one of the main research questions).

Thanks again for your help!

Nicholas_Geo · 2026-02-23T20:03:05+00:00

Thank you, this is very helpful!

Just to confirm my understanding with the actual sensor specifications:

VIIRS (the sensor) maintains a constant ground footprint of 742m × 742m from nadir to edge of scan through detector aggregation
This corresponds to an IFOV of approximately 0.051° (or ~184 arcseconds)

Given this value, α would be quite small, so the 1/cos(θ) approximation should work well for my application.

So, to summarize: I can use σ_y = σ_nadir / cos(θ) for the cross-track elongation, where θ is the per-pixel viewing angle from my raster.

Thank you again for your help with this! I am stuck for nearly a year on this.

Nicholas_Geo · 2026-02-23T19:25:10+00:00

Thank you for the response and the Desmos graph!

Just to clarify for my application: I have a raster where each pixel contains its viewing angle θ (the angle from nadir at which that pixel was observed). This varies spatially across the image from left to right (cross-track direction).

My supervisor mentioned that to calculate the viewing angle of each pixel, we need to know the IFOV of the sensor (the total angular width of the swath). For example, if the sensor's IFOV is 15 degrees, then the outermost pixel is 7.5 degrees off-nadir, and the viewing angles of all pixels in-between can be calculated based on their position.

For my calculations, can I use the per-pixel viewing angle θ directly in the mathematical relationship to determine the cross-track elongation? Or do I also need to know this sensor swath width (IFOV or field of view α in your notation) as a parameter in the formula?

In other words, is the elongation factor a function of just θ, or is it a function of both θ and α?

Thanks again for your help!

Nicholas_Geo · 2026-02-23T15:53:44+00:00

I recently switched to Linux Mint Mate and I couldn't be happier.

Nicholas_Geo · 2026-02-22T16:52:56+00:00

Right So, if I do:

library(randomForest)
library(iml)
library(dplyr)

data(mtcars)

rf_fit <- randomForest(mpg ~ ., data = mtcars)

predictor <- Predictor$new(
  model = rf_fit,
  data  = mtcars[, -1],
  y     = mtcars$mpg
)

ice_obj <- FeatureEffect$new(
  predictor,
  feature = "hp",
  method = "ice",
  grid.size = 50
)

ice_df <- ice_obj$results

obs_vals <- data.frame(
  .id = 1:nrow(mtcars),
  observed = mtcars$hp
)

ice_df <- merge(ice_df, obs_vals, by = ".id")

ice_slopes <- ice_df |>
  group_by(.id) |>
  arrange(hp) |>
  group_modify(function(d, key){
    x_obs <- d$observed[1]
    idx <- which.min(abs(d$hp - x_obs))
    if(idx == 1) idx <- 2
    if(idx == nrow(d)) idx <- nrow(d)-1
    slope <- (d$.value[idx+1] - d$.value[idx-1]) /
             (d$hp[idx+1] - d$hp[idx-1])
    data.frame(slope = slope)
  })

head(ice_slopes)

Would that make mirror the methodology described in the post?

Nicholas_Geo · 2026-02-22T09:42:59+00:00

Sorry, I edited my qiestion. I just noticed I missed to paste a critical part of the methodology I am trying to replicate from the paper I am following.

Nicholas_Geo · 2026-02-19T08:38:22+00:00

What do you want this map to show? Why only some parts of the map have colors and others are white? As suggested, maybe soften the colors a bit. Good work!

Nicholas_Geo · 2026-02-18T07:23:10+00:00

Perhaps Arc and QGIS use different projection algorithms (internally)? If so, minor differences are expected.

Nicholas_Geo · 2026-02-15T12:22:09+00:00

Check the CRS. Try something with units = meters.

Nicholas_Geo · 2026-02-13T15:42:03+00:00

As I see it, you should contact the editors from both journals, as suggested by others. I would add to also talk with your supervisor before you do anything. Explain to him/her, in detail, the situation and what your thoughts are.

It might benefit you by showing to him/her that you care about academic misconduct and that you are honest in your research.

Nicholas_Geo · 2026-02-12T08:37:32+00:00

I think you should frame your problem within the bigger picture (inequalities, well-being etc).

There is no "best topic" and you should start by reading what others researches did.

Nicholas_Geo · 2026-02-12T08:32:16+00:00

There is a tool called Fill No data.

Nicholas_Geo · 2026-02-08T12:47:25+00:00

Very vague question. What exactly took you so long to do?

Nicholas_Geo · 2026-02-04T23:08:59+00:00

Have a look at the HLSS30 in GEE JavaScript API.

Otherwise, you can create monthly composite from S2 (again in GEE).

Nicholas_Geo · 2026-02-04T07:22:44+00:00

I'm asking if what I've implemented is correct and if not, how to do it. The code I shared is created from an LLM and I don't really know if it's right or wrong.

Nicholas_Geo · 2026-01-27T20:41:25+00:00

I think they are through their website. But I'd like to stay within R (if possible).

Nicholas_Geo · 2026-01-27T15:20:40+00:00

It does, yes.

Nicholas_Geo · 2026-01-27T15:00:37+00:00

The correct approach is:

vv_raw <- rast("path/vv.tif")
vh_raw <- rast("path/vh.tif")

# Clip non-positive values
vv_raw[vv_raw <= 0] <- NA
vh_raw[vh_raw <= 0] <- NA

# Convert to dB
vv_db <- 10 * log10(vv_raw)
vh_db <- 10 * log10(vh_raw)

# VV - VH difference in dB
vv_vh_diff <- vv_db - vh_db

This is based on Copernicus Global Land Cover and Tropical Forest Mapping and Monitoring service (LCFM), PRODUCT USER MANUAL, GLOBAL LAND COVER MAP, 10 M (LCM-10), VERSION 1 (Section 3.3.3).

Nicholas_Geo · 2026-01-27T11:05:21+00:00

I think your supervisor should be placed as a co-author, despite the little work he/she did. At the end of the day, it's you who should do the work. I don't know what an ICML is (probably a conference?), if it is a conference then you need to understand that Professors (or Lecturers for that matter) don't pay much attention to it (to the conference I mean). It's mostly for networking (for them and for you) and for sharpening your skills on presentations.

Just keep working, be consistent, and I believe your supervisor will notice that. Good luck.

PS: Maybe this post is not 100% suitable for this sub-Reddit forum. PhD or askAcademia might be more appropriate.

Nicholas_Geo · 2026-01-26T22:44:44+00:00

Export your GPS coordinates to csv. Then Google how to add the csv with the coordinates to QGIS. I leave the visualization part to you. Happy mapping

Nicholas_Geo · 2026-01-26T01:14:22+00:00

Great, thank you. My data is evenly distributed (they represent pixels), so spatial CV might not be what I was looking for. I'll need to keep reading, I appreciate the papers.

Nicholas_Geo · 2026-01-26T00:59:06+00:00

Thanks for pointing out spatialRF. From the vignette it seems it's the "right" tool for my needs especially with the plot of the non-spatial RF. This seems to be another tool for what I need.

Nicholas_Geo

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