Estimating biomass on CRP pastureland: A comparison of remote sensing techniques

• Published in: Biomass & bioenergy Vol. 66; pp. 268 - 274
• Main Authors: Porter, Tucker F., Chen, Chengci, Long, John A., Lawrence, Rick L., Sowell, Bok F.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2014; Elsevier
• Subjects: Agronomy. Soil science and plant productions; Bandwise regression; Biological and medical sciences; Biomass estimation model; Crop circle sensor; Fundamental and applied biological sciences. Psychology; General agroecology; General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping; General agronomy. Plant production; Generalities. Agricultural and farming systems. Agricultural development; Landsat imagery; NDVI; United States; North America; America; USA, Montana; Biomass estimation model; CRP; Landsat imagery; Bandwise regression; NDVI; Crop circle sensor; Vegetation index; Space remote sensing; Measurement sensor; Biomass; Imagery; Modeling; Landsat satellite; Soil conservation; Near infrared radiation; Detector; Regression model; Pasture; Optical properties; Cultivated plant; Comparative study
• ISSN: 0961-9534; 1873-2909
• DOI: 10.1016/j.biombioe.2014.01.036

Biomass from land enrolled in the Conservation Reserve Program (CRP) is being considered as a biofuel feedstock source. A quick, accurate and nondestructive method to estimate biomass yield would be valuable for land managers to ensure sustainable production. The purpose of this study was to compare the ability of regression models to estimate biomass yields using data from satellite and ground based remote sensing platforms. Biomass yields and plant spectral responses were obtained at different phenological stages over two growing seasons (2011–2012) on an 8.1 ha CRP pasture in central Montana. Regression models were constructed using the normalized difference vegetation index (NDVI) and various band combinations from a hand held Crop Circle sensor and from Landsat satellite images. All models showed reasonable accuracy in estimating biomass, with a difference of <276 kg ha−1 or 8% of measured values. None of the models showed statistically significant differences (p > 0.05) between actual and estimated biomass. Results suggest that the usefulness of the spectral regions is a function of phenological growth stage. Red, red edge, and the near-infrared bands are more responsive at boot and peak growth stages while bands in the short-wave infrared increased the accuracy for the dormant stage biomass estimations. Land managers may construct spectral models to more effectively manage biomass resources. •Remote sensing techniques for biomass yield estimation of grass/legume mixed stands.•All biomass estimates were within 276 kg ha−1, or 8%, of the actual measured value.•The Landsat-NDVI model produced estimates within 1% of the actual measured value.•Red, red edge, and NIR bands increased accuracy at boot and peak growth stages.•Short-wave infrared bands increased biomass estimation success at dormancy.