Vegetation canopy anisotropy at 1.4 GHz

Hornbuckle, B. K.; England, Anthony W.; Roo, R.D. De; Fischman, M.A.; Boprie, D.

doi:10.1109/tgrs.2003.817192

Cited by 55 publications

(16 citation statements)

References 42 publications

(58 reference statements)

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“…Vegetation canopy volume scattering is significant [35]; non-zero values of ω increase dry bias, lower values of ω result in lower ubRMSE and higher R 2 (this work).…”

Section: Single-scattering Albedo Of Vegetation Canopymentioning

confidence: 67%

“…While ω is often assumed to be unpolarized [41], several tower-based experiments have found that ω h = ω v in agriculture. This would occur if scattering plant structures appeared different when observed at hand v-pol; for example, ω h > ω v for the REBEX-8x experiment in corn [35]. Table 5 indicates that lower values of ω result in the best soil moisture retrievals as quantified by ubRMSE and R 2 .…”

Section: Single Scattering Albedomentioning

confidence: 98%

“…Scatter darkening, where T B p is reduced by radiation scattering within the canopy, occurs when the size of plant components (e.g., stems, leaves, ears) is similar to the wavelength (SMAP: λ = 21 cm). This effect must be considered in corn [35,36]. The components of soybean plants are much smaller and as such there is relatively little scattering [36].…”

Section: Single Scattering Albedomentioning

confidence: 99%

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Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt

et al. 2019

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NASA's Soil Moisture Active Passive (SMAP) Level 2 soil moisture products are not meeting mission goals in the U.S. Corn Belt according to our seasonal evaluation conducted at a SMAP Core Validation Site in central Iowa. The single-channel algorithm (SCA) soil moisture products are too dry in early spring and late fall before and after crops are present, and too noisy in late spring and early summer when crops begin to grow. We investigated likely contributing factors. The climatology of vegetation's effect on soil moisture retrieval in the SCA can differ by more than 14 days from what is retrieved by SMAP's dual-channel algorithm (DCA). Soil and vegetation temperatures, assumed to be equal by all retrieval algorithms, are not: vegetation is about 2 K colder at 6:00 a.m. and about 2 K warmer at 6:00 p.m.. The effective temperature in version 2 products is too warm as compared to in situ soil temperatures. We propose a new effective temperature model that is consistent with observations, decreases the unbiased root-mean-square-error (ubRMSE) overall, and increases the coefficient of determination (R 2 ) of the DCA in every month. However, some monthly dry biases increase to more than 0.10 m 3 m −3 . The single-scattering albedo, ω, has a significant impact on soil moisture retrieval. While the DCA has its lowest ubRMSE and highest R 2 when ω is non-zero, the SCA have their lowest ubRMSE and highest R 2 when ω = 0, and the dry bias of all algorithms increases as ω increases. Errors in soil texture are not significant, but soil surface roughness should not be static and have a higher overall value. Our findings make it clear that a new retrieval algorithm that can account for changing soil roughness and vegetation conditions is needed.

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“…Vegetation canopy volume scattering is significant [35]; non-zero values of ω increase dry bias, lower values of ω result in lower ubRMSE and higher R 2 (this work).…”

Section: Single-scattering Albedo Of Vegetation Canopymentioning

confidence: 67%

Section: Single Scattering Albedomentioning

confidence: 98%

Section: Single Scattering Albedomentioning

confidence: 99%

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Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt

et al. 2019

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“…The stronger anisotropy observed in this study for the wheat vegetation was also shown by the retrieved tt V parameter with a mean value of 3.82 in comparison to 2.54 for the mustard (see Figure 7d,h). Additionally, several studies have shown (e.g., [9,11,14,45]) that neglecting the polarization dependency of the ω parameter, as in this study, could also introduce potential errors in the τ retrieval, and therefore influence the estimation of the angle dependency especially at V polarization. Finally, the angular dependency may also be related to the amount of VWC and LAI.…”

Section: Vegetation Optical Depthmentioning

confidence: 93%

“…The TDR measurement interval was set to 30 min. Air temperature within the vegetation canopy was measured at different heights (15,30,45, 60, 75 and 90 cm above ground) for the non-gridded and gridded plot using twelve Pt100 sensors (six for each plot) with an accuracy of about 0.1 • C. The measuring interval was set to 5 min and the data were logged with a DT85 logger (COSINUS Messtechnik GmbH, Taufkirchen, Germany).…”

Section: (A)mentioning

confidence: 99%

Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat

et al. 2018

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L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (T B ) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from T B observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured T B .

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Sensor Principles and Remote Sensing Techniques

England

2005

Encyclopedia of Hydrological Sciences

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Model‐derived estimates of water stored in the upper few meters of soil, in vegetation, and in snow packs are the objectives of a new satellite microwave remote sensing technology. The most mature of the alternatives, this technology will use a combination of longer wavelength satellite microwave brightness observations and Soil Vegetation Atmosphere Transfer (SVAT) models, forced by solar and thermal radiation and by weather, to produce a near‐daily global field of stored water. While the near‐daily temporal resolution is a unique strength for hydrometeorology, its likely spatial resolution of tens of kilometers is a serious weakness for land‐surface hydrology. This article is intended as a review of the concept and the investigations that have contributed to it. It also includes brief surveys of some technologies, like aperture synthesis radiometry and combined active passive sensing, that are being explored to improve spatial resolution.

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Vegetation canopy anisotropy at 1.4 GHz

Cited by 55 publications

References 42 publications

Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt

Seasonal Evaluation of SMAP Soil Moisture in the U.S. Corn Belt

Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat

Sensor Principles and Remote Sensing Techniques

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