Silver et al. (2025) rOPTRAM: An R package for satellite-derived soil moisture in rangelands using the OPTRAM model

Identification

• Journal: Environmental Modelling & Software
• Year: 2025
• Date: 2025-10-27
• Authors: Micha Silver, Zhe Dong, Ricardo Díaz‐Delgado, Arnon Karnieli
• DOI: 10.1016/j.envsoft.2025.106689

Research Groups

• The Jacob Blaustein Center for Scientific Cooperation, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sde Boker, Israel
• The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sde Boker, Israel
• The Remote Sensing Laboratory, The French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sde Boker, Israel
• Doñana Biological Station (EBD-CSIC), Spanish National Research Council, Seville, Spain

Short Summary

This paper introduces rOPTRAM, an R package that implements the OPtical TRapezoid Model (OPTRAM) for satellite-derived soil moisture estimation in rangelands. The package streamlines image acquisition, automates trapezoid edge delineation with multiple curve fitting options, and is validated across diverse rangeland sites.

Objective

• To present, describe, and demonstrate the rOPTRAM R package for assessing and mapping soil water content in rangelands worldwide, incorporating innovations such as automated imagery acquisition, programmatic trapezoid edge delineation, and multiple curve fitting options.

Study Configuration

• Spatial Scale: Regional to global scales, with high spatial resolution satellite imagery (10 meters for Sentinel-2) applied to areas of thousands of hectares. Validation performed at three specific rangeland sites.
• Temporal Scale: Time series of imagery covering periods of years, with satellite revisit times of every five days (Sentinel-2). Validation data spans from 2017 to present for two sites and from October 2023 for one site.

Methodology and Data

• Models used: OPtical TRapezoid Model (OPTRAM), implemented in the rOPTRAM R package. The package supports linear, exponential, and second-order polynomial curve fitting for trapezoid edges.
• Data sources:
  • Satellite Imagery: Sentinel-2 (Copernicus DataSpace Ecosystem API), Landsat (manual acquisition option). Utilizes Shortwave Infrared (SWIR) and Visible/Near-Infrared (VI) spectral bands.
  • In-situ Observations: Time Domain Reflectometer (TDR) sensors and Cosmic Ray Neutron Scanner (CRNS) instruments from the International Soil Moisture Network (ISMN) and National Ecological Observatory Network (NEON).

Main Results

• The rOPTRAM package successfully automates the OPTRAM model, including Sentinel-2 imagery acquisition and pre-processing, and programmatic delineation of trapezoid wet and dry edges.
• Three curve fitting options (linear, exponential, polynomial) are implemented, allowing users to select the best fit for varying landcover conditions.
• Validation at three rangeland sites (San Luis Wildlife Refuge, Doñana Park, Santa Rita Experimental Range) showed fair to good agreement between OPTRAM-derived soil moisture and in-situ measurements.
  • San Luis Wildlife Refuge (semi-arid): Linear fit yielded Kling-Gupta Efficiency (KGE) of 0.56 and R² of 0.45.
  • Doñana Park (semi-humid): Exponential fit yielded KGE of 0.23 and R² of 0.67.
  • Santa Rita Experimental Range (arid): Polynomial fit yielded KGE of 0.60 and R² of 0.44.
• The package includes usability enhancements such as customizable scatter plot coloring by geographic subregions or image date, aiding in the visualization of spatial and temporal soil moisture variations.

Contributions

• First R package (rOPTRAM) for the OPTRAM model, promoting reproducible research and integration into broader R-based analytical workflows.
• Automated acquisition and pre-processing of Sentinel-2 imagery via the Copernicus DataSpace Ecosystem (CDSE) API, significantly reducing manual effort and computational resources.
• Programmatic delineation of trapezoid edges, eliminating subjective visual interpretation and ensuring consistency and reproducibility.
• Introduction of multiple curve fitting options (linear, exponential, polynomial) for trapezoid edges, enhancing the model's adaptability to diverse vegetation covers and environmental conditions.
• Various usability enhancements, including options for vegetation indices, scatter plot visualization, and efficient handling of large datasets.

Funding

• European Union’s Horizon 2020 (grant agreement no. 871128)
• ICTS-Doñana (logistic and technical support, environmental and biodiversity data)

Citation

@article{Silver2025rOPTRAM,
  author = {Silver, Micha and Dong, Zhe and Díaz‐Delgado, Ricardo and Karnieli, Arnon},
  title = {rOPTRAM: An R package for satellite-derived soil moisture in rangelands using the OPTRAM model},
  journal = {Environmental Modelling & Software},
  year = {2025},
  doi = {10.1016/j.envsoft.2025.106689},
  url = {https://doi.org/10.1016/j.envsoft.2025.106689}
}