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A practical model and online tool to predict cover crop nitrogen content and carbon-to-nitrogen ratio via remote sensing

Navdeep Kaur·Charlie White·Andrew Lefever·Alison Thieme·Jyoti Jennewein·W. Dean Hively·Daniella Carrijo
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Abstract

Purpose: Estimations of cover crop (CC), nitrogen (N) content and carbon-to-nitrogen (C:N) ratio are crucial for guiding N fertilizer recommendations for subsequent cash crops, yet prediction tools that can be easily adopted by practitioners are lacking. This study aims to (1) develop models to predict CC N content and C:N ratio using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) from Uncrewed Aerial Vehicles (UAV) and satellite imagery, combined with growing degree days (GDD, base 5 °C) and CC species; and (2) integrate the models developed into an online application. Methods: Over 260 CC biomass samples were collected from 10 Pennsylvania counties in the Chesapeake Bay Watershed representing diverse species (triticale, cereal rye, wheat, brassicas, legumes, and mixtures). Four models were evaluated: (1) UAV NDVI + GDD + species, (2) UAV NDRE + GDD + species, (3) satellite NDVI + GDD + species, and (4) satellite NDRE + GDD + species. Results: Random forest models (R² > 0.74 for N content and R² > 0.86 for C:N ratio) generally outperformed generalized additive models (R² > 0.70 for N content and R² > 0.85 for C:N ratio) based on 10-fold cross validation. GDD and vegetation indices were the strongest predictors for N content, while GDD and species group influenced C:N ratio the most. In the independent site-years validation, UAV-NDRE performed best for predicting CC N content (R² = 0.82), and both UAV-based models (R² = 0.52) outperformed the satellite models for C:N ratio prediction. Conclusion: Random forest models accurately predicted CC N content and C:N ratio and were integrated into an online application to support precision agriculture practices.

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Precision AgricultureArticle
90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 39.74624096024223° to 40.78220894336633° latitude; -78.92555858547613° to -75.6962362223381° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Navdeep Kaur, Charlie White, Andrew Lefever, Alison Thieme, Jyoti Jennewein, W. Dean Hively, Daniella Carrijo. 2026-09-25. A practical model and online tool to predict cover crop nitrogen content and carbon-to-nitrogen ratio via remote sensing. https://doi.org/10.1007/s11119-026-10434-6

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USGS2024

Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

Purpose Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy. Methods The objective of this study was to quantify cash and cover crop residue nitrogen and carbon traits using partial least squares regression models and a combination of 1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected ASD spectra (n = 296) of 11 cover crop species and three cash crop species, and 2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches. Results Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 – 0.98; RMSE = 0.24 – 4.25%) and results suggest that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 – 0.75; RMSE = 2.71 – 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38 – 1.85%). PRISMA imagery models were also impacted by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation. Conclusion As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.

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