Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits

Area of Science:

• Agricultural remote sensing
• Soil science
• Environmental monitoring

Background:

• Winter cover crops enhance soil health and reduce environmental impacts.
• Accurate biophysical trait estimation (biomass, groundcover) is vital for assessing cover crop benefits.
• Remote sensing offers a scalable approach to monitor cover crops.

Purpose of the Study:

• To compare ground-based and satellite sensor measurements for estimating winter cover crop biophysical traits.
• To evaluate different satellite data processing levels (surface vs. top-of-atmosphere reflectance).
• To assess the interchangeability of various remote sensing platforms.

Main Methods:

• Paired satellite imagery (SPOT 5, Landsat 7, WorldView-2) and handheld multispectral sensors were used.
• Normalized Difference Vegetation Index (NDVI) was calculated and compared between sensors and processing levels.
• Fractional green cover was compared to in-situ photographs, and biomass was estimated using NDVI calibration equations.

Main Results:

• Surface reflectance satellite data showed stronger correlations with proximal sensors than top-of-atmosphere data.
• Satellite-derived NDVI strongly agreed with passive handheld sensor estimates for fractional green cover and biomass.
• Active handheld sensors showed high accuracy but required intercept correction for data integration.

Conclusions:

• Passive multispectral remote sensing platforms can be used interchangeably for cover crop trait assessment, with minor adjustments for SPOT 5 NDVI.
• Active sensors may need separate calibration before combining with passive sensor data.
• Surface reflectance products are valuable, but cloud shadow detection needs improvement in satellite data processing.