Relating Radar Backscatter to Biophysical Properties of Temperate Perennial Grassland

Hill, Michael J.; Donald, G. E.; Vickery, P. J.

doi:10.1016/s0034-4257(98)00063-7

Cited by 39 publications

(21 citation statements)

References 19 publications

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“…Before that, in 1999, Hill et al [10] conducted a very detailed experiment on grassland biophysical properties using SAR backscatter calculated from multi-frequency (C, L and P band) and multi-polarized (HH, HV and VV) airborne (JPL/NASA airborne imaging system) SAR data. Significant relationships were formulated between the measurement of grass height and the SAR backscatter, demonstrating the potential that might be offered with repeatpass satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis

Ali

Barrett

Cawkwell

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-This paper describes the potential and limitations of repeat-pass synthetic aperture radar interferometry (InSAR) to retrieve the biophysical parameters of intensively managed pastures. We used a time series of 8 acquisitions from the TerraSAR-X Staring Spotlight (TSX-ST) mode. The ST mode is different from conventional Stripmap mode therefore we adjusted the Doppler phase correction for interferometric processing. We analysed the three interferometric pairs with an 11-day temporal baseline, and among these three pairs found only one gives a high coherence. The results show that the high coherence in different paddocks is due to cutting of the grass in the month of June, however the temporal decorrelation in other paddocks is mainly due to the grass growth and high sensitivity of the X-band SAR signals to the vegetation cover. The InSAR coherence (over coherent paddocks) shows a good correlation with SAR backscatter (R 2 dB = 0.65, p < 0.05) and grassland biophysical parameters (R 2 Height = 0.55, p < 0.05, R 2 Biomass = 0.75, p < 0.05). It is thus possible to detect different management practices (e.g., grazing, mowing/cutting) using SAR backscatter (dB) and coherence information from high spatial, short baseline X-band imagery, however the rate of decorrelation over vegetated areas is high.

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Section: Introductionmentioning

confidence: 99%

Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis

Ali

Barrett

Cawkwell

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Using multispectral optical images, the seasonal pattern of Normalized Differential Vegetation Index (NDVI) has been used for classification of grassland in North America [2] and Eastern Australia [3]. NDVI has been widely used for estimation of grass biomass [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The other studies involving grass focused on classification, where grass was one of the several ground cover classes. For example, the potential of airborne SAR has been explored for grassland classification, using relationships between mean herbage height and C-and L-backscatter in grassland in New South Wales, Australia [3]. The results suggest that combined imagery from Cand L-band satellite-borne SAR sensors have potential for current application in grassland monitoring.…”

Section: Introductionmentioning

confidence: 99%

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

Wang

Liu

2013

Remote Sensing

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Abstract:Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR), and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) radar signals at horizontally emitted and received polarization (HH) for pasture monitoring at the paddock scale in order to guide farmers for better management. The pasture site is selected, in Otway, Victoria, Australia. The biomass, water content of grass, and soil moisture over this site were analyzed with these three bands of SAR images, through linear relationship between SAR backscattering coefficient, and vegetation indices Normalized Differential Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI)), together with soil moisture index (MI). NDVI, NDWI, and MI are considered as proxy of pasture biomass, plant water content, and soil moisture, respectively, and computed from optical images and climate data. SAR backscattering coefficient and vegetation indices are computed within a grass zone, defined by classification with MODIS data. The grass condition and grazing activities for specific paddocks are detectable, based on SAR backscatter, with all three wavelengths datasets. Both temporal and spatial analysis results show that the X-band SAR has the highest correlation to the vegetation indices. However, its accuracy can be affected by wet weather due to its sensitivity to the OPEN ACCESSRemote Sens. 2013, 5 3612 water on leaves. The C-band HH backscattering coefficient showed moderate reliability to evaluate biomass and water content of grass, with limited influence from rainfall in the dry season. The L-band SAR is the less accurate one for grass biomass measurement due to stronger penetration.

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“…The use of 3 cm wavelength radar imagery is novel, and might reasonably be expected to be particularly appropriate for pasture biomass, since the scale of pasture and the wavelength is appropriate. Other studies using imaging radar for the pasture or grassland environment [5,6] have used longer wavelengths, and the results from these studies have not shown consistent trends.…”

Section: Introductionmentioning

confidence: 96%

Estimation of pasture biomass using dual-polarisation radar imagery; a preliminary study

McNeill

Pairman

Belliss

et al. 2008

2008 23rd International Conference Image and Vision Computing New Zealand

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The efficient management of pasture is a key driver of profitability for New Zealands pastoral dairy and meat industries. Regular monitoring of available pasture feed and feed quality are required regularly for dairy operations. In this paper, we report preliminary results from a study that tests the viability of using short-wavelength imaging radar to estimate pasture biomass. We use six (out of 36) images from the TerraSAR-X satellite, comparing the HH & HV and VV&HV dual-polarisation combinations. Several linear models have been tested for field-measured pasture biomass. The best model is a linear regression using backscatter from the HH & HV combination, resulting in a residual standard error of 191.4 (kg/ha). Most important, this polarisation combination showed no evidence of a date effect, which suggests that this polarisation combination can provide estimation of pasture biomass independent of the surface environmental conditions.

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Relating Radar Backscatter to Biophysical Properties of Temperate Perennial Grassland

Cited by 39 publications

References 19 publications

Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis

Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

Estimation of pasture biomass using dual-polarisation radar imagery; a preliminary study

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