Groeneveld et al. (2026) Surface Reflectance: An Image Standard to Upgrade Precision Agriculture

Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-03-30
• Authors: David P. Groeneveld, Tim Ruggles
• DOI: 10.3390/rs18071037

Research Groups

• Resolv, Inc., Hartford, SD, USA

Short Summary

This study evaluates atmospheric correction software for converting satellite imagery to surface reflectance for precision agriculture, finding that the CMAC software provides the necessary accuracy and precision for automated, error-free agricultural analytics, unlike established methods.

Objective

• To determine if existing atmospheric correction methods provide the precision and accuracy required for surface reflectance in precision agriculture, and to evaluate CMAC software against established methods (Sen2Cor and FORCE) using Sentinel-2 imagery.

Study Configuration

• Spatial Scale: Farmland near Burley, Idaho, USA, focusing on three irrigated corn fields.
• Temporal Scale: Analysis of 43 Sentinel-2 images over a crop growing season, evaluated as reflectance time series.

Methodology and Data

• Models used: CMAC (closed-form method for atmospheric correction), Sen2Cor, FORCE.
• Data sources: Sentinel-2 satellite imagery; smallsat data (mentioned for Tier 2 infill applications).

Main Results

• CMAC software consistently produced smooth, expected logistic growth patterns in NDVI time series for irrigated corn, indicating high precision and accuracy in surface reflectance retrieval.
• Sen2Cor and FORCE exhibited systematic divergences in NDVI and spectral band reflectance time series, over-correcting in clear conditions and under-correcting in hazy conditions.
• Only CMAC provided surface reflectance with the accuracy required for precision agriculture applications, applicable for both Sentinel-2 (Tier 1) and haze/cloud-affected smallsat data (Tier 2 infill).
• CMAC-corrected data enabled accurate indexing of crop start dates and automated identification and removal of uncorrectable data (cloud, cloud shadow, extreme haze).
• Broadband NIR NDVI showed minimal errors compared to narrowband NIR NDVI, despite its sensitivity to atmospheric water vapor.

Contributions

• Establishes surface reflectance as a critical image standard for upgrading precision agriculture applications.
• Validates CMAC as a superior atmospheric correction method for precision agriculture, demonstrating its ability to deliver accurate and precise surface reflectance from Sentinel-2 and smallsat data.
• Highlights the limitations of established atmospheric correction software (Sen2Cor, FORCE) for precision agriculture due to systematic errors related to atmospheric effects.
• Provides a framework for fully automated, error-free delivery of agricultural analytics, crucial for building farmer trust and enabling applications like crop start date indexing.

Funding

• Not specified in the provided text.

Citation

@article{Groeneveld2026Surface,
  author = {Groeneveld, David P. and Ruggles, Tim},
  title = {Surface Reflectance: An Image Standard to Upgrade Precision Agriculture},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18071037},
  url = {https://doi.org/10.3390/rs18071037}
}