Sentinel-2 data are the choice for many analyses, especially for agricultural analytics. The data are free and produced at relatively high cadence – an image every five days, or less. At 40.5 pixels per acre for the visible and the broader NIR band, Sentinel-2 data are perfect for most agricultural applications: higher resolution is often not needed.
Precision Agriculture Needs Accurate Surface Reflectance
Satellite image data analytics are inaccurate if the images are not corrected to surface reflectance. For comparing uncalibrated crop growth zones across a field, top-of-atmosphere reflectance (TOAR) data are fine. However, comparing results across more than a mile distance can be problematic. For scaling treatments, for comparing yield potential among regional fields, or the same field from past years, the appropriate analytics can only be generated by accurate surface reflectance. A good example why is NDVI (Normalized Difference Vegetation Index), calculated from a ratio with red and NIR bands and the number one index applied in agriculture. NDVI from surface reflectance can be used to scale against reference evapotranspiration to estimate crop water use, identify plugged center pivot emitters, find an infestation of spider mites in corn, or to scale the application of nutrients or amendments across a field. Unless reliably corrected to surface reflectance, NDVI’s utility for these applications is questionable and potentially perilous.
Use the Most Accurate Surface Reflectance Data
Since they affect the farm’s bottom line, agriculture analytics must be made reliable and precise. NDVI generated from TOAR, or poorly corrected surface-reflectance estimates, provide good examples why. Atmospheric effect causes the red band values for growing crops to increase and the NIR band to decrease. This results in a false drop in NDVI values that confuses analytical results and falsely raises concerns for the farmer. Analyses in our Sentinel-2 NDVI Application topic paper clearly demonstrate that top-of-atmosphere (TOAR) analytics can’t be used for automated analyses. The data also clearly demonstrate that Sen2Cor software to correct Sentinel-2 data performs well for clean images with little haze but fails at levels of atmospheric effect now common over much of the Midwest during the growing season. RESOLV is a better solution.
RESOLV was developed specifically for smallsats with an eye on agriculture: smallsat surface reflectance conversion can now be more accurate than corrected Landsat and Sentinel-2 data. If you’re interested in agriculture and you know the value that high quality Sentinel-2 data can provide, harness RESOLV for the precision you need to achieve the outcomes you want. A quick read of the NDVI topic paper also teaches how to apply RESOLV-corrected data for indexing crop start dates to schedule treatments on thousands of fields for the entire season
Sentinel-2 views of the farmland around Basalt and Firth, Idaho on July 9,2021 before and after RESOLV.
About the Author
Hello,
I’m Dr. David Groeneveld, founder and leader of RESOLV™. Our software atmospherically corrects smallsat data conveniently, accurately and reliably and does so in near real time. The benefits of RESOLV™ go beyond its technical capabilities. Better accuracy helps researchers, scientists, and others make smarter choices to monitor and manage our planet.
Curious to learn more about RESOLV™, the science behind it and its potential for correcting smallsat images? Fill out this short form and I’ll be in touch.