Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/28336
Title: A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape
Contributor(s): Crabbe, Richard Azu (author); Lamb, David William  (author)orcid ; Edwards, Clare (author); Andersson, Karl  (author); Schneider, Derek  (author)orcid 
Publication Date: 2019-04-10
Open Access: Yes
DOI: 10.3390/rs11070872
Handle Link: https://hdl.handle.net/1959.11/28336
Open Access Link: https://doi.org/10.3390/rs11070872
Abstract: Knowledge of the aboveground biomass (AGB) of large pasture fields is invaluable as it assists graziers to set stocking rate. In this preliminary evaluation, we investigated the response of Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to biophysical variables (leaf area index, height and AGB) for native pasture grasses on a hilly, pastoral farm. The S1 polarimetric parameters such as backscattering coefficients, scattering entropy, scattering anisotropy, and mean scattering angle were regressed against the widely used morphological parameters of leaf area index (LAI) and height, as well as AGB of pasture grasses. We found S1 data to be more responsive to the pasture parameters when using a 1 m digital elevation model (DEM) to orthorectify the SAR image than when we employed the often-used Shuttle Radar Topography 30 m and 90 m Missions. With the 1m DEM analysis, a significant quadratic relationship was observed between AGB and VH cross-polarisation (R2 = 0.71), and significant exponential relationships between polarimetric entropy and LAI and AGB (R2 = 0.53 and 0.45, respectively). Similarly, the mean scattering angle showed a significant exponential relationship with LAI and AGB (R2 = 0.58 and R2 = 0.83, respectively). The study also found a significant quadratic relationship between the mean scattering angle and pasture height (R2 = 0.72). Despite a relatively small dataset and single season, the mean scattering angle in conjunction with a generalised additive model (GAM) explained 73% of variance in the AGB estimates. The GAM model estimated AGB with a root mean square error of 392 kg/ha over a range in pasture AGB of 443 kg/ha to 2642 kg/ha with pasture LAI ranging from 0.27 to 1.87 and height 3.25 cm to 13.75 cm. These performance metrics, while indicative at best owing to the limited datasets used, are nonetheless encouraging in terms of the application of S1 data to evaluating pasture parameters under conditions which may preclude use of traditional optical remote sensing systems.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 11(7), p. 1-19
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Field of Research (FOR): 070104 Agricultural Spatial Analysis and Modelling
090905 Photogrammetry and Remote Sensing
Socio-Economic Objective (SEO): 830403 Native and Residual Pastures
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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