A microwave scattering model for layered vegetation

Karam, M.A.; Fung, A.K.; Lang, Roger H.; Chauhan, N.S.

doi:10.1109/36.158872

Cited by 275 publications

(108 citation statements)

References 27 publications

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“…As for HV, the double-volume scattering is the dominant scattering source at four winter wheat growth stages. The RVI not only included HH, HV, VV, backscattering difference information and then was sensitive to crops structure, but also reduced the environmental and incidence angle effects [48,58]. Therefore, the RVI showed higher correlations with LAI and biomass.…”

Section: Discussionmentioning

confidence: 99%

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Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

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Section: Discussionmentioning

confidence: 99%

“…However, SAR has some advantages for estimating LAI and biomass at medium to high LAI and biomass [58], and therefore, RPPs were introduced in our study in combination with multispectral data. Previous studies have combined OSVIs and RPPs to estimate biomass and LAI in crops or forests by simply multiplying them [12,24,35,36].…”

Section: Discussionmentioning

confidence: 99%

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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“…The first implication is that, in order to infer AGB from radar backscatter, different models must be applied to each forest type and the number of models depend on the extent the structural variability of forest types introduce significantly different radar backscatter. The physics underlying the differences in backscatter model is explained by the understanding of the electromagnetic wave propagation and scattering in the forest canopies [16,48,49]. In a two layer forest canopy model consisting of distinct crown and trunk regions, the distorted Born approximation can be used to decompose the backscattering coefficient into three dominant terms rising from first order multiple scattering contributions: Volume scattering, volume-surface double bounce scattering, and surface scattering [16].…”

Section: Radar Backscatter Modelmentioning

confidence: 99%

Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests

Saatchi

2016

Remote Sensing

129

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Synthetic Aperture Radar (SAR) backscatter measurements are sensitive to forest aboveground biomass (AGB), and the observations from space can be used for mapping AGB globally. However, the radar sensitivity saturates at higher AGB values depending on the wavelength and geometry of radar measurements, and is influenced by the structure of the forest and environmental conditions. Here, we examine the sensitivity of SAR at the L-band frequency (~25 cm wavelength) to AGB in order to examine the performance of future joint National Aeronautics and Space Administration, Indian Space Research Organisation NASA-ISRO SAR mission in mapping the AGB of global forests. For SAR data, we use the Phased Array L-Band SAR (PALSAR) backscatter from the Advanced Land Observing Satellite (ALOS) aggregated at a 100-m spatial resolution; and for AGB data, we use more than three million AGB values derived from the Geoscience Laser Altimeter System (GLAS) LiDAR height metrics at about 0.16-0.25 ha footprints across eleven different forest types globally. The results from statistical analysis show that, over all eleven forest types, saturation level of L-band radar at HV polarization on average remains ě100 Mg¨ha´1. Fresh water swamp forests have the lowest saturation with AGB at~80 Mg¨ha´1, while needleleaf forests have the highest saturation at~250 Mg¨ha´1. Swamp forests show a strong backscatter from the vegetation-surface specular reflection due to inundation that requires to be treated separately from those on terra firme. Our results demonstrate that L-Band backscatter relations to AGB can be significantly different depending on forest types and environmental effects, requiring multiple algorithms to map AGB from time series of satellite radar observations globally.

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“…There are several approaches which can be used ( Figure 5). These include the semi-empirical Water Cloud Model (WCM) [22], Radiative Transfer Model (RTM) [37] and 3D parameterizations [38]. In this work the (WCM) was used.…”

Section: Vegetation Moisture Datamentioning

confidence: 99%