A Python and R blend for processing Sentinel-1 images, getting SAR-based vegetation indices, and sampling raster
Erli Pinto dos Santos
Repository intro
The primary purpose of this repo is the need for a pipeline for downloading and preprocessing Sentinel-1 Ground Range Detected (GRD) images, computing Dual-polarization SAR vegetation indices, and sampling (with points coordinates) the processed scenes over a given Area of Interest (AOI). So, you are gonna find here both Spyder and RStudio (IDEs) projects, which means the repo is a blend of Python and R resources, and their scripts to do the above-mentioned steps.
The repository, its Spyder and RStudio projects, and its codes were built upon the requirements:
1) To bring Python and R capabilities to deal with raster products. The radar products processing is feasible using Python resources, while raster sampling is faster using R resources.
2) It uses the packages: asf_search (Python 3.9), for downloading satellite products, main radar satellites, from the Alaska Satellite Facility; snappy (Python 3.6), the Python implementation of the SeNtinel Application Platform, from the European Space Agency (SNAP-ESA), which contains the Sentinel-1 Toolbox; and the terra package (R version 4.2.1), for dealing with raster and vectors fastest than other resources.
3) I tried not to personalize the pipeline, as you can personalize your way and needs. This means that you are free to change it on your way, e.g., changing Sentinel-1 algorithms, methods, AOI, etc.
4) I advise you to peek rapidly at the below-presented flowcharts, as they mean to summarize what the codes exactly do.
How does it work and what does it do?
Script 01: geographical search and batch download of SAR data in the Alaska Satellite Facility (ASF) dataset:
This code uses asf_search resources to do a geographical search within the ASF SAR data catalog, learn more about how its features work in:
2) Bulk Download Sentinel 1 SAR Data
3) (in Portuguese) Download simultâneo de várias imagens de SAR (como Sentinel-1) via Python
WARNING: To download bulk products, use a Python 3.9 environment.
Script 02: reading and visualizing a single product band:
WARNING: From here and forward you will need a Python 3.4 or 3.6 environment, it is a SNAP project requirement. Check it out at:
1) Getting Started with SNAP Toolbox in Python: https://towardsdatascience.com/getting-started-with-snap-toolbox-in-python-89e33594fa04
2) Install ESA SNAP ToolBox along with Current Updates and Snappy Python on UBUNTU 18.04 for Satellite Imagery Analysis: https://kaustavmukherjee-66179.medium.com/install-esa-snap-toolbox-along-with-current-updates-and-snappy-python-on-ubuntu-18-04-696a5104e7f
3) Configure Python to use the SNAP-Python (snappy) interface: https://senbox.atlassian.net/wiki/spaces/SNAP/pages/50855941/Configure+Python+to+use+the+SNAP-Python+snappy+interface
Script 03: preprocessing of Sentinel-1 SAR products (from removing thermal noise to orthorectification):
Script 04: subsetting scenes using a polygon area of interest:
It is an optional script designed to save disc space by subsetting scenes. Skip this step if you're not interested.
Script 05: computing SAR dual-pol vegetation indices:
For fast array computations, this script just read BEAM-DIMAP raster products using snappy and transforms them to NumPy arrays, in order to compute the Dual-pol SAR vegetation indices. The indices are: Cross-Ratio (CR, Frison et al. (2018)), Dual-polarization SAR vegetation index (DPSVI, Periasamy (2018)), the modified DPSVI (DPSVIm, dos Santos et al. (2021)), the normalized difference polarization index (Pol, Hird et al. (2017)), the modified Radar Vegetation Index (RVIm, Nasirzadehdizaji et al. (2019)), the dual-polarimetric descriptors for Sentinel-1 (Bhogapurapu et al., 2021), and the Dual-polarization RVI for Sentinel-1 GRD products (DpRVIc, (Bhogapurapu et al. (2022)).
References
dos Santos, E. P., da Silva, D. D., & do Amaral, C. H. (2021). Vegetation cover monitoring in tropical regions using SAR-C dual-polarization index: seasonal and spatial influences. International Journal of Remote Sensing, 42(19), 7581–7609. https://doi.org/10.1080/01431161.2021.1959955
Bhogapurapu, N., Dey, S., Bhattacharya, A., Mandal, D., Lopez-Sanchez, J. M., McNairn, H., López-Martínez, C., & Rao, Y. S. S. (2021). Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 20–35. https://doi.org/10.1016/j.isprsjprs.2021.05.013
Bhogapurapu, N., Dey, S., Mandal, D., Bhattacharya, A., Karthikeyan, L., McNairn, H. and Rao, Y.S., 2022. Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data. Remote Sensing of Environment, 271, p.112900. https://doi.org/10.1016/j.rse.2022.112900
Frison, P.-L., Fruneau, B., Kmiha, S., Soudani, K., Dufrêne, E., Toan, T. Le, Koleck, T., Villard, L., Mougin, E., & Rudant, J.-P. (2018). Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology. Remote Sensing, 10(12), 2049. https://doi.org/10.3390/rs10122049
Hird, J., DeLancey, E., McDermid, G., & Kariyeva, J. (2017). Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sensing, 9(12), 1315. https://doi.org/10.3390/rs9121315
Nasirzadehdizaji, R., Balik Sanli, F., Abdikan, S., Cakir, Z., Sekertekin, A., & Ustuner, M. (2019). Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage. Applied Sciences, 9(4), 655. https://doi.org/10.3390/app9040655
Periasamy, S. (2018). Significance of dual polarimetric synthetic aperture radar in biomass retrieval: An attempt on Sentinel-1. Remote Sensing of Environment, 217(September), 537–549. https://doi.org/10.1016/j.rse.2018.09.003
Script 06: sampling raster products using R:
After processing raster products, use this script to sample raster bands either using coordinates of the points or the coordinates of the points and a set of buffers around them.
WARNING: it will work properly only using R version >= 4.2.1.
Citing it: use the reference peer reviewed paper:
I receive numerous requests to reproduce this work, and I'm happy to grant them all, I just ask you to attribute the original work to our repository. Give us credits - for any use of our code - by citing our peer-reviewed article:
Santos, Erli Pinto dos, Michel Castro Moreira, Elpídio Inácio Fernandes-Filho, José Alexandre M. Demattê, Emily Ane Dionizio, Demetrius David da Silva, Renata Ranielly Pedroza Cruz, Jean Michel Moura-Bueno, Uemeson José dos Santos, and Marcos Heil Costa. 2023. "Sentinel-1 Imagery Used for Estimation of Soil Organic Carbon by Dual-Polarization SAR Vegetation Indices" Remote Sensing 15, no. 23: 5464. https://doi.org/10.3390/rs15235464