Vesterdal et al. (2025) Current remote sensing applications for sustainable agricultural transitions and nature-based solutions

Identification

• Journal: Journal of Environmental Management
• Year: 2025
• Date: 2025-12-09
• Authors: Maria S. Vesterdal, René Gislum, Tommy Dalgaard
• DOI: 10.1016/j.jenvman.2025.128263

Research Groups

• Department of Agroecology, Aarhus University, Viborg, Denmark
• Department of Agroecology, Aarhus University, Slagelse, Denmark
• Center for Landscape Research in Sustainable Agricultural Futures and Center for Sustainable Landscapes, Department of Agroecology, Aarhus University, Viborg, Denmark

Short Summary

This review systematically synthesizes current satellite remote sensing applications in agriculture, focusing on their potential to facilitate a transition towards sustainable management practices in nutrient management, environmental impact, and food security. It highlights that while remote sensing offers valuable insights, challenges in data integration, accuracy, and scalability persist.

Objective

• To present a selection of current satellite remote sensing applications in agriculture, specifically examining their potential to facilitate a transition towards more sustainable management practices.
• To highlight representative examples of identified remote sensing applications and analyze their sustainability potential across three key areas: nutrient management, environmental impacts of production, and food security.

Study Configuration

• Spatial Scale: Field-level to regional and global scales, with a specific focus on conventional, large-scale agricultural systems. Resolutions discussed range from 3 meters (PlanetScope) to 1 kilometer (MODIS).
• Temporal Scale: Multi-annual and intra-annual time series, covering growing seasons and long-term trends (e.g., crop rotation over multiple years, phenological shifts).

Methodology and Data

• Models used: Systematic review and synthesis of literature following PRISMA 2020 framework. The reviewed studies utilized:
  • Crop classification models
  • Phenology models
  • Machine learning models (e.g., Random Forest, Deep Learning like DeepYield)
  • AI-driven systems (in precision agriculture context)
  • Denitrification–decomposition (DNDC) model
• Data sources:
  • Literature Databases: Web of Science (WoS), Scopus.
  • Satellite Remote Sensing Data:
    • Landsat (5 TM, 7 ETM+, 8)
    • Moderate Resolution Imaging Spectroradiometer (MODIS)
    • Sentinel (1, 2, 3)
    • Medium Resolution Imaging Spectrometer (MERIS)
    • Agency Environment Satellite (ENVISAT)
    • PlanetScope
    • Synthetic Aperture Radar (SAR) data
  • Derived Data: Vegetation Indices (e.g., NDVI), spectral indices, texture features, spectral reflectance, surface albedo, Forel-Ule Index, phycocyanin pigmentation.
  • Ancillary Data: Farm economy data, switchgrass potential, environmental factors (surface temperature, soil characteristics, weather conditions), historical yield maps, ground truth observations, survey photographs.

Main Results

• Remote sensing (RS) data is increasingly relevant for agroecological studies and sustainable agricultural development, with advancements offering higher spatial resolutions, diverse sensors, and improved temporal frequencies.
• RS applications can effectively monitor and support sustainable agricultural transitions in three key areas:
  • Nutrient Management: Monitoring crop rotation patterns, tillage events (conventional vs. conservation), soil organic carbon (SOC) status, bare fallowed areas, nitrogen dynamics, and water pollution pathways (e.g., liquid manure application, river plumes, cyanobacteria blooms, irrigation, tile drainage).
  • Environmental Impacts: Supporting climate change mitigation by assessing land surface albedo effects (e.g., converting bare fallow to cropped areas, no-till practices) and leveraging phenological events for adaptive management and improved crop classification. RS also aids in mapping land use and land cover (LULC) changes to address deforestation and biodiversity loss.
  • Food Security: Identifying underperforming cropland for sustainable intensification, mapping LULC changes over time, accurate crop classification (including rare crops and complex systems like intercropping), and yield prediction using vegetation indices or machine learning models.
• Despite significant potential, challenges remain in RS applications, including data integration, accuracy, scalability, model transparency (black box issues), the need for advanced technical expertise, limited availability of high-quality ground truth data, and trade-offs between resolution and revisit frequency.
• Future research should focus on refining and automating RS tasks to develop standardized, user-friendly tools for data-driven decision-making at farm, regional, and policy levels.

Contributions

• Provides a systematic review specifically focused on how current satellite remote sensing applications can be repurposed and utilized to facilitate a transition towards more sustainable agricultural management strategies, rather than solely optimizing production efficiency.
• Synthesizes the potential of satellite RS across three critical dimensions of agricultural sustainability: nutrient management, environmental impacts (climate change mitigation, biodiversity), and food security.
• Highlights the original value of adapting existing RS tools for monitoring sustainable practices and Nature-based Solutions (NbS) at various scales, offering insights beyond traditional precision agriculture applications.
• Identifies key challenges and future research directions for enhancing the accessibility, accuracy, and practical implementation of RS-driven solutions in sustainable agriculture.

Funding

• European Union’s (EU) Horizon Europe programme of Food, Bioeconomy Natural Resources, Agriculture and Environment (Grant Agreement No. 101081847) for the project ‘Transformation for Sustainable Nutrient Supply and Management’ (trans4num.eu).
• Sustainscapes.org research center.
• Land-CRAFT.dk research center.

Citation

@article{Vesterdal2025Current,
  author = {Vesterdal, Maria S. and Gislum, René and Dalgaard, Tommy},
  title = {Current remote sensing applications for sustainable agricultural transitions and nature-based solutions},
  journal = {Journal of Environmental Management},
  year = {2025},
  doi = {10.1016/j.jenvman.2025.128263},
  url = {https://doi.org/10.1016/j.jenvman.2025.128263}
}