add statement of need to docs #37

docs/appendix/references.md

@@ -2,10 +2,14 @@
 
 Florinsky, I. V. (2009). Computation of the third‐order partial derivatives from a digital elevation model. _International journal of geographical information science_, 23(2), 213-231. https://doi.org/10.1080/13658810802527499
 
+Foerste, C., _et al._ (2014). EIGEN-6C4 the latest combined global gravity field model including GOCE data up to degree and order 2190 of GFZ potsdam and GRGS toulouse. GFZ Data Services. https://doi.org/10.5880/icgem.2015.1
+
 Howat, I., _et al._ (2022a). The Reference Elevation Model of Antarctica – Strips, Version 4.1. _Harvard Dataverse_ https://doi.org/10.7910/DVN/X7NDNY
 
 Howat, I., _et al._ (2022b). The Reference Elevation Model of Antarctica – Mosaics, Version 2, _Harvard Dataverse_ https://doi.org/10.7910/DVN/EBW8UC
 
+Hoyer, S., & Hamman, J. (2017). xarray: N-d labeled Arrays and Datasets in Python. _Journal of Open Research Software_, 5(1), 10. https://doi.org/10.5334/jors.148
+
 MacGregror, J. A. _et al._ (2024). Geologic Provinces Beneath the Greenland Ice Sheet Constrained by Geophysical Data Synthesis. _Geophysical Research Letters_, 51, e2023GL107357. https://doi.org/10.1029/2023GL107357
 
 Mark, R. K. (1992). Multidirectional, oblique-weighted, shaded-relief image of the Island of Hawaii. _United States Geological Survey_. Open-File Report 92-422. https://doi.org/10.3133/ofr92422
@@ -14,12 +18,16 @@ Morlighem, M. _et al._ (2022a). IceBridge BedMachine Greenland, Version 5 [Data
 
 Morlighem, M. _et al._ (2022b). MEaSUREs BedMachine Antarctica, Version 3 [Data Set]. _NASA National Snow and Ice Data Center Distributed Active Archive Center_. https://doi.org/10.5067/FPSU0V1MWUB6
 
+Noh, M. J., & Howat, I. M. (2017). The surface extraction from TIN based search-space minimization (SETSM) algorithm. _ISPRS Journal of Photogrammetry and Remote Sensing_, 129, 55-76. https://doi.org/10.1016/j.isprsjprs.2017.04.019
+
 Nuth, C. and Kääb, A. (2011) Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change, _The Cryosphere_, 5, 271–290, https://doi.org/10.5194/tc-5-271-2011
 
 Porter, C., _et al._ (2022). ArcticDEM - Strips, Version 4.1. _Harvard Dataverse_. https://doi.org/10.7910/DVN/OHHUKH
 
 Porter, C., _et al._ (2023), ArcticDEM, Version 4.1, _Harvard Dataverse_. https://doi.org/10.7910/DVN/3VDC4W
 
+Scheick et al., (2023). icepyx: querying, obtaining, analyzing, and manipulating ICESat-2 datasets. _Journal of Open Source Software_, 8(84), 4912, https://doi.org/10.21105/joss.04912
+
 Shiggins, C. J., _et al._ (2023). Automated ArcticDEM iceberg detection tool: insights into area and volume distributions, and their potential application to satellite imagery and modelling of glacier–iceberg–ocean systems, _The Cryosphere_, 17, 15–32, https://doi.org/10.5194/tc-17-15-2023
 
-Zevenbergen, L. W. and Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth surface processes and landforms, 12(1), 47-56.
+Zevenbergen, L. W. and Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth surface processes and landforms, 12(1), 47-56. https://doi.org/10.1002/esp.3290120107

docs/getting_started/why_pdemtools.md

@@ -0,0 +1,13 @@
+# Why pDEMtools?
+
+> This page reproduces the 'Statement of Need' of the [_Journal of Open Source Software_ manuscript](https://joss.theoj.org/papers/2a10e67d2709a6cfb672538b4a21726e) for `pdemtools`.
+
+[ArcticDEM](https://www.pgc.umn.edu/data/arcticdem/) and [REMA](https://www.pgc.umn.edu/data/rema/) are high-resolution, time-stamped 2-metre-resolution DEMs of the polar regions provided by the Polar Geospatial Center (PGC). They are extracted by applying stereo auto-correlation techniques ([Noh & Howat, 2017](https://doi.org/10.1016/j.isprsjprs.2017.04.019)) to pairs of submetre Maxar satellite imagery. The data includes Worldview-1, Worldview-2, Worldview-3, and GeoEye-1, beginning in 2007 (ArcticDEM) or 2009 (REMA) and ongoing to the present day. Products are available as tens of thousands of time-stamped 'strips' ([Porter _et al._ 2022](ttps://doi.org/10.7910/DVN/OHHUKH); [Howat _et al._ 2022a](https://doi.org/10.7910/DVN/X7NDNY)) constructed from individual scene pairs, or as a single mosaic ([Porter _et al._ 2023](https://doi.org/10.7910/DVN/3VDC4W); [Howat _et al._ 2022a](https://doi.org/10.7910/DVN/EBW8UC)) compiled from the combined stack of strips. Strips allow users to perform change detection by comparing data from different seasons or years, whilst mosaics provide a consistent and comprehensive product over the entire polar regions.
+
+As Earth Science has moved into the 'big data' era, increasing amounts of Arctic- and Antarctic-focused resources are available as public, cloud-optimised datasets. New approaches are providing Python tools to act as combined API and processing tools, such as [`icepyx`](https://icepyx.readthedocs.io) ([Scheick _et al._ 2023](https://doi.org/10.21105/joss.04912)) or [`pypromice`](https://pypromice.readthedocs.io) ([How _et al._ 2023](https://doi.org/10.21105/joss.05298)). From 2022 (ArcticDEM v4.1 and REMA v2), the PGC DEM products are [hosted](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/pgc-data-stac.json) as Cloud Optimised GeoTIFFs (CoGs) in a SpatioTemporal Asset Catalog (STAC), a standardised structure for cataloguing spatiotemporal data. However, the PGC STAC is currently a 'static' rather than 'dynamic' STAC, which means there is no convenient Application Programming Interface (API) for searching the datasets in response to user queries. This limits the ability of users to programmatically interact with ArcticDEM and REMA data in a quick and efficient manner. The `pdemtools` package has two aims: the first is to provide a Python-focussed alternative for searching and downloading ArcticDEM and REMA data, emulating dynamic STAC query tools such as [`pystac`](https://pystac.readthedocs.io); whilst the second is to provide commonly used processing functions specific to the needs of ArcticDEM and REMA users (a focus on ice sheet and cryosphere work), as well as the particular strengths of the ArcticDEM and REMA datasets (high-resolution and multitemporal).
+
+The `pdemtools` `search()` tool and `load` module allow for convenient access to the ArcticDEM and REMA datasets. Mosaics can be downloaded from a one-line `load.mosaic()` function, whilst the `search()` function allows for convenient filtering of a locally downloading ArcticDEM/REMA strip index according to variables such as date, region of interest, spatial coverage, temporal baseline, source sensors, accuracy, and cross-track data. The results of searches are returned as a [`geopandas`](https://geopandas.org) dataframe, and can be downloaded using the `load.from_search()` function. Elevation models are returned as [`xarray`](https://docs.xarray.dev) DataArrays [Hoyer _et al. 2017](https://doi.org/10.5334/jors.148) with geospatial metadata via the [`rioxarray`](https://corteva.github.io) extension - a standard format for storing and processing n-dimensional geospatial data within the geospatial Python community. By utilising standardised formats, the aim is to allow the user to quickly move beyond `pdemtools` into their own analysis in whatever format they desire, be that `xarray`, `numpy` or `dask` datasets, DEM analysis Python packages such as [`xdem`](https://xdem.readthedocs.io/) for advanced coregistration or [`richdem`](https://richdem.readthedocs.io) for flow analysis, or exporting to geospatial file formats for analysis beyond Python.
+
+After download, there exist a number of (pre-)processing steps that are near universally common in topographic analyses. These include geoid-correction, co-registration of time-series data, and/or the construction of terrain parameters such as hillshade, slope, aspect, and curvature. `pdemtools` contains pre-built functions to perform these processing steps, as well as further functionality specific to ArcticDEM and REMA use cases. For instance, we include functions to quickly extract the EIGEN-6C4 geoid [Foerste _et al._ 2014](https://doi.org/10.5880/icgem.2015.1) and Greenland/Antarctic bedrock masks directly from local versions of the Greenland and Antarctic BedMachine datasets ([Morlighem _et al_, [2022a](https://doi.org/10.5067/GMEVBWFLWA7X), [2022b](https://doi.org/10.5067/FPSU0V1MWUB6)), reprojecting and resampling the data to match the target DEM. Options for ingesting user-provided mask and geoid data are also provided. Additionally, partial derivatives of the surface used to calculate terrain parameters ($\frac{\partial z}{\partial x}$, $\frac{\partial z}{\partial y}$, $\frac{\partial^2 z}{\partial x^2}$, $\frac{\partial^2 z}{\partial y^2}$, $\frac{\partial^2 z}{\partial x \partial y}$) are calculated following [Florinsky (2009)](https://doi.org/10.1080/13658810802527499), as opposed to more common methods such as [Zevenbergen and Thorne (1987)](https://doi.org/10.1002/esp.3290120107). The newer approach computes partial derivatives of elevation based on fitting a third-order polynomial, by the least-squares approach, to a 5 $\times$ 5 window as opposed to the more common 3 $\times$ 3 window. This is more appropriate for high-resolution DEMs: curvature over a 10 m window for the 2 m resolution ArcticDEM/REMA strips will lead to a local denoising effect that limits the impact of noise common in high-resolution photogrammetric products. These methods are also adapted into a co-registration routine, which otherwise follows the commonly used approach of [Nuth & Kääb (2011)](https://doi.org/10.5194/tc-5-271-2011).
+
+We aim to grow `pdemtools` by implementing new methods developed by the ArcticDEM and REMA research community. For instance, we currently include sea-level-filtering and iceberg detection routines outlined by @shiggins_automated_2023, and invite community contributions or requests of other routines that will be of use to users of `pdemtools`. Ongoing research projects making use of `pdemtools` are applying ArcticDEM and REMA data to the mapping of crevasses, ice cliff heights, and subglacial lakes, as well as the initiation of ice sheet models. It has also been used within training exercises at the 2024 Polar Geospatial Center Data Workshop, contributing to a growing international network of `pdemtools` users.

docs/index.rst

@@ -42,6 +42,7 @@ Rather than introducing custom classes, pDEMtools will always try and return DEM
    :maxdepth: 2
    :caption: Getting Started:
 
+   getting_started/why_pdemtools.md
    getting_started/install.md
    getting_started/supplementary_datasets.md
    getting_started/cite.md

paper/paper.bib

@@ -53,17 +53,17 @@ @article{how_pypromice_2023
 	pages = {5298},
 }
 
-@misc{howat_remamosaic_2022,
-	title = {The {Reference} {Elevation} {Model} of {Antarctica} - {Mosaics}, {Version} 2},
-	doi = {10.7910/DVN/EBW8UC},
+@misc{howat_remastrips_2022,
+	title = {The {Reference} {Elevation} {Model} of {Antarctica} - {Strips}, {Version} 4.1},
+	doi = {10.7910/DVN/X7NDNY},
 	publisher = {Harvard Dataverse},
 	author = {Howat, Ian and Porter, Claire and Noh, Myoung-Jon and Husby, Erik and Khuvis, Samuel and Danish, Evan and Tomko, Karen and Gardiner, Judith and Negrete, Adelaide and Yadav, Bidhyananda and Klassen, James and Kelleher, Cole and Cloutier, Michael and Bakker, Jesse and Enos, Jeremy and Arnold, Galen and Bauer, Greg and Morin, Paul},
 	year = {2022},
 }
 
-@misc{howat_remastrips_2022,
-	title = {The {Reference} {Elevation} {Model} of {Antarctica} - {Strips}, {Version} 4.1},
-	doi = {10.7910/DVN/X7NDNY},
+@misc{howat_remamosaic_2022,
+	title = {The {Reference} {Elevation} {Model} of {Antarctica} - {Mosaics}, {Version} 2},
+	doi = {10.7910/DVN/EBW8UC},
 	publisher = {Harvard Dataverse},
 	author = {Howat, Ian and Porter, Claire and Noh, Myoung-Jon and Husby, Erik and Khuvis, Samuel and Danish, Evan and Tomko, Karen and Gardiner, Judith and Negrete, Adelaide and Yadav, Bidhyananda and Klassen, James and Kelleher, Cole and Cloutier, Michael and Bakker, Jesse and Enos, Jeremy and Arnold, Galen and Bauer, Greg and Morin, Paul},
 	year = {2022},