A new open-access paper from Resolv, Inc. argues that the long-standing challenges of precision agriculture—unreliable satellite data and prohibitive costs—can be addressed by making accurate surface reflectance the standard output of satellite imagery. The paper, “Surface Reflectance: An Image Standard to Upgrade Precision Agriculture,” published March 30 in Remote Sensing by Dr. David Groeneveld and Tim Ruggles, benchmarks three atmospheric correction methods on Sentinel-2 imagery and outlines how a reliable correction standard can unlock low-cost, fully automated crop intelligence.
Atmospheric correction is critical because light traveling through the atmosphere distorts the signal received by satellites. Correcting this distortion yields surface reflectance, the measurement needed for accurate crop analytics. Without precise correction, small clouds and shadows can be mistaken for crop problems, triggering false alarms that waste time and money. Automated analysis has been unable to distinguish bad data from real trouble, stalling the adoption of precision agriculture.
The Resolv team compared two mainstream tools, Sen2Cor and FORCE, against CMAC, the closed-form method for atmospheric correction developed by Resolv and now being readied for commercial release. Across a wide range of atmospheric conditions, CMAC produced precise and accurate surface reflectance estimates, while the mainstream methods showed systematic error, over-correcting clear images and under-correcting hazy ones. This bias had gone undetected until this paper surfaced it.
Reliable surface reflectance enables several proof-of-concept applications, including automated removal of clouds and cloud shadows, an automated crop start-date index that could replace growing-degree-day scheduling, stable NDVI readings even with varying atmospheric water vapor, soil capability classification directly from imagery, and accurate remote crop irrigation based on crop greenness and reference evapotranspiration. Together, these applications provide a path for precision agriculture to pay for itself.
To address high image costs, the paper proposes a tiered model. Tier 1 uses free, high-quality Sentinel-2 imagery corrected to surface reflectance. Tier 2 fills gaps with commercial smallsat data when clouds block Sentinel-2. The smallsat data can be resampled to match Sentinel-2, verified, and billed automatically, with no human in the loop. This creates a turnkey pipeline that orders, corrects, analyzes, tracks, and bills imagery across vast regions without manual touchpoints, reducing service costs while increasing image sales volume. Crop insurance could serve as a natural channel.
Resolv, Inc. develops atmospheric correction technology for satellite imagery, with a focus on making precision agriculture analytics trustworthy and affordable at scale. Initial development of CMAC was funded by National Science Foundation SBIR. The paper is available online at the Remote Sensing journal website. For more information, visit Resolv's website.
