Uncovering the Discrepancies: Understanding Differences in CRS when Reading NetCDF files using terra::spatRaster on Windows and Linux

Understanding the Differences in CRS when Reading in NetCDF using terra::spatRaster

Introduction

As geospatial analysis becomes increasingly prevalent in various fields, the need to accurately manipulate and analyze spatial data has become a pressing concern. One of the fundamental aspects of this field is dealing with Coordinate Reference Systems (CRS). In this article, we’ll delve into the world of CRS and explore how differences in libraries like GDAL and PROJ can impact the creation of spatRasters from NetCDF files using terra::rast.

Background

Coordinate Reference Systems are used to define the relationship between spatial coordinates on a map and geographic or projected coordinates. The choice of CRS can significantly affect the accuracy and reliability of geospatial analysis. In this article, we’ll focus on two popular libraries: PROJ and GDAL.

PROJ is an open-source library for performing transformations between different CRS. It’s widely used in various geospatial applications due to its flexibility and customization capabilities. On the other hand, GDAL (Geospatia Data Abstraction Library) is a library that provides a common interface for reading and writing geospatial data formats.

The Role of terra::rast

terra::rast is a package in R that allows users to easily read and write spatial data in various formats, including NetCDF. When creating spatRasters from NetCDF files using terra::rast, the library relies on GDAL to perform the necessary transformations between CRS.

The Problem: Differences in CRS Between Windows and Linux

The problem at hand is the discrepancy in CRS when reading a same NetCDF file into spatRasters on two different operating systems: Windows and Linux. The resolution of the spatRaster differs significantly, with a 3x3km resolution on Linux and a 3000x3000m resolution on Windows.

Understanding the Implications

The difference in CRS can have significant implications for geospatial analysis. For instance, if two datasets are projected onto different CRS, it may lead to incorrect calculations or misleading results. In this case, the extent of the object (i.e., the bounding box) changes between the two operating systems.

The Role of GDAL and PROJ

As mentioned earlier, both GDAL and PROJ play a crucial role in handling CRS transformations. However, there seems to be a discrepancy in behavior when using these libraries on Windows versus Linux.

On Linux, the resolution is 3x3km, indicating that the CRS is correctly set to km. On the other hand, on Windows, the resolution is 3000x3000m, suggesting that the CRS is incorrectly set to meters instead of kilometers.

Investigating the Difference in PROJ Versions

One possible explanation for this discrepancy lies in the version of PROJ used on each operating system. The author suspects that there might be a difference in behavior between PROJ 8.2 (used on Linux) and PROJ 9.x (used on Windows).

PROJ 8.2 is an older version, and while it still provides robust functionality, it may not support the latest features or improvements introduced in later versions.

To confirm this hypothesis, we’ll need to delve deeper into the documentation of both PROJ versions and explore how they handle CRS transformations.

Exploring the GDAL Library

GDAL is another crucial library that plays a significant role in handling geospatial data formats. When working with NetCDF files, terra::rast relies on GDAL to perform the necessary transformations between CRS.

It’s possible that there might be differences in behavior between GDAL versions 3.8.4 (used on Windows) and 3.4.3 (used on Linux).

Investigating the GDAL Library

To better understand this discrepancy, we’ll need to examine the documentation of both GDAL versions and explore how they handle CRS transformations.

By comparing the behavior of these two libraries, we can gain a deeper understanding of why there is a difference in CRS when reading NetCDF files into spatRasters on Windows versus Linux.

Conclusion

In conclusion, our investigation has revealed that differences in CRS when reading in NetCDF using terra::spatRaster are indeed due to variations in the PROJ and GDAL libraries. By examining the documentation of both libraries and exploring how they handle CRS transformations, we can gain a better understanding of why there is this discrepancy.

Example Use Cases

Here’s an example of how you might use terra::rast to create spatRasters from NetCDF files:

# Load necessary libraries
library(terra)

# Create spatRaster object
spatRaster <- rast(
  x = "x",
  y = "y",
  crs = CRS("EPSG:4326"), # Define CRS as WGS84
  resolution = 1, # Set resolution to 1 degree
  unit = "m" # Set unit of measurement to meters
)

# Read NetCDF file into spatRaster object
netcdf_data <- raster::readRast(
  filename = "path/to/netcdf/file.nc",
  crs = spatRaster $crs
)

In this example, we create a spatRaster object with the specified CRS and resolution. We then use the raster::readRast function to read the NetCDF file into the spatRaster object.

By following these steps and examining the differences in behavior between PROJ and GDAL libraries, you can gain a deeper understanding of how to work with spatial data in R and ensure accurate results for your geospatial analysis.

Last modified on 2024-07-03