Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Sheng Wang, Kaiyu Guan, Chenhui Zhang, Chongya Jiang, Qu Zhou, Kaiyuan Li, Ziqi Qin, Elizabeth A. Ainsworth, Jingrui He, Jun Wu, Dan Schaefer, Lowell E. Gentry, Andrew J. Margenot, Leo Herzberger

Research output: Contribution to journalArticlepeer-review


Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. We also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

Original languageEnglish (US)
Article number113386
JournalRemote Sensing of Environment
StatePublished - Feb 1 2023


  • Aboveground biomass
  • Cover crop
  • Imaging spectroscopy
  • Nitrogen
  • Process-guided machine learning
  • Radiative transfer modeling

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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