Nonlinear hierarchical models for predicting cover crop biomass using Normalized Difference Vegetation Index

• Published in: Remote sensing of environment Vol. 114; no. 12; pp. 2833 - 2840
• Main Authors: Muñoz, Juan David, Finley, Andrew O., Gehl, Ron, Kravchenko, Sasha
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.12.2010; Elsevier
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Bayesian; Biological and medical sciences; Cover crop; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Heteroscedasticity; Internal geophysics; MCMC; NDVI; Red clover; Teledetection and vegetation maps; United States; Michigan; MCMC; Red clover; NDVI; Cover crop; Bayesian; Heteroscedasticity; Agricultural chemical product; Spatial variability; Vegetation index; springs; Agricultural system; Available nutrient; focus; accuracy; Information; North America; Modeling; Profit; soils; Ability; biomass; Weed; Prediction; Property of soil; Soil structure; Increase; utilization; non-linear models; Southwest; erosion; Nonlinearity
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2010.06.011

Incorporating cover crops into agricultural systems can improve soil structural properties, increase nutrient availability, reduce erosion and loss of agrochemicals, and suppress weeds. These benefits are a function of the amount of cover crop biomass that enters the soil. The ability to easily and inexpensively quantify the spatial variability of cover crop biomass is needed to better understand and predict its potential as an input to agricultural systems. Here, we explore the use of Normalized Difference Vegetation Index (NDVI) as a source of information for improving accuracy and precision of cover crop biomass prediction. We focus on developing models that account for biomass variability within and among fields. These models are used to produce digital data layers of predicted biomass and associated uncertainty. We propose hierarchical nonlinear models with field random effects and a residual variance function to accommodate strong heteroscedasticity. These models are motivated using aboveground biomass of red clover ( Trifolium pratense L.) measured on three different dates in five fields in southwest Michigan. Model adequacy was assessed using the Deviance Information Criterion. Given this criterion, the “best” fitting model included field effects and a polynomial function to account for non-constant residual variance. Importantly, we demonstrate that accounting for heteroscedasticity in the model fitting is critical for capturing uncertainty in subsequent biomass prediction.