σ&lt;sup&gt;°&lt;/sup&gt; Signature of the Amazon Rain Forest Obtained from the Seasat Scatterometer

Birrer, I. J.; Bracalente, E. M.; Dome, G. J.; Sweet, J.; Berthold, G.

doi:10.1109/tgrs.1982.4307513

Cited by 65 publications

(36 citation statements)

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“…for all measurements taken at the same incidence angles [Birer et al, 1982]. This method provides excellent relative normalization that is used to eliminate differences in o • caused by absolute gain biases among antenna beams.…”

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confidence: 99%

“…An analysis of SeaSat A Scatterometer System (SASS) o •) data by Kennet and Li [1989] showed Amazon and Congo tropical rain forests to be homogenous over a large area. Detailed analysis of the SASS measured o • over the Amazon showed some temporal and spatial variability that must be taken into account [Birer et al, 1982]. To remove the effects of spatial variability, o •) data sets used for beam balancing can be filtered using a "mask" created by an enhancing scatterometer resolution algorithm developed by Long et al [1993] NSCAT transmits pulses at 14 GHz using six antennas that NSCAT sequences through all eight beams during 3.74 s to achieve along-track beam sampling of 25 km, and after approximately 2 min, the fore and aft antenna samples tully overlap.…”

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confidence: 99%

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NSCAT normalized radar backscattering coefficient biases using homogenous land targets

Zec¹,

Long²,

Jones³

1999

J. Geophys. Res.

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confidence: 99%

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confidence: 99%

NSCAT normalized radar backscattering coefficient biases using homogenous land targets

Zec¹,

Long²,

Jones³

1999

J. Geophys. Res.

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“…2) Beam Balancing Using Distributed Land Targets: Distributed land targets have been used for scatterometer beam balancing during SASS [18], [19] and ERS-1 [19] missions. This calibration method relies on large area homogeneous land targets with uniform azimuthal responses.…”

Section: ) Beam Balancing Using Open Ocean Backscatter Measurementsmentioning

confidence: 99%

“…Its uniform and dense canopy makes the radar cross section independent of azimuth. The random orientation of individual scatterers in the vegetation canopy makes sigma-0 essentially polarization insensitive [18], while the equatorial location minimizes seasonal effects. The observed diurnal sigma-0 variation is within 0.5 dB, with the highest sigma-0 in the morning [18], [19].…”

Section: ) Beam Balancing Using Open Ocean Backscatter Measurementsmentioning

confidence: 99%

“…The random orientation of individual scatterers in the vegetation canopy makes sigma-0 essentially polarization insensitive [18], while the equatorial location minimizes seasonal effects. The observed diurnal sigma-0 variation is within 0.5 dB, with the highest sigma-0 in the morning [18], [19]. In addition to the Amazon basin, another large area in central Russia was identified, during the calibration and validation (cal/val) activities, as being sufficiently homogeneous and isotropic.…”

Section: ) Beam Balancing Using Open Ocean Backscatter Measurementsmentioning

confidence: 99%

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Postlaunch sensor verification and calibration of the NASA Scatterometer

Tsai

Graf

Winn

et al. 1999

IEEE Trans. Geosci. Remote Sensing

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Abstract-Scatterometer instruments are active microwave sensors that transmit a series of microwave pulses and measure the returned echo power to determine the normalized radar backscattering cross section (sigma-0) of the ocean surface from which the speed and direction of near-surface ocean winds are derived. The NASA Scatterometer (NSCAT) was launched on board the ADEOS spacecraft in August 1996 and returned ten months of high-quality data before the failure of the ADEOS spacecraft terminated the data stream in June 1997.The purpose of this paper is to provide an overview of the NSCAT instrument and sigma-0 computation and to describe the process and the results of an intensive postlaunch verification, calibration, and validation effort. This process encompassed the functional and performance verification of the flight instrument, the sigma-0 computation algorithms, the science data processing system, and the analysis of the sigma-0 and wind products. The calibration process included the radiometric calibration of NSCAT using both engineering telemetry and science data and the radiometric beam balance of all eight antenna beams using both open ocean and uniform land targets. Finally, brief summaries of the construction of the NSCAT geophysical model function and the verification and validation of the wind products will be presented.The key results of this paper are as follows: The NSCAT instrument was shown to function properly and all functional parameters were within their predicted ranges. The instrument electronics subsystems were very stable and all of the key parameters, such as transmit power, receiver gain, and bandpass filter responses, were shown to be stable to within 0.1 dB. The science data processing system was thoroughly verified and the sigma-0 computation error was shown to be less than 0.1 dB. All eight antenna beams were radiometrically balanced, using natural targets, to an estimated accuracy of about 0.3 dB. Finally, a new model function, called NSCAT-1, was constructed and used to produce wind products. The wind products were statistically verified using ECMWF wind fields and were validated using NDBC buoy measurements. Overall, we believe that NSCAT generated high-quality wind products with wind speed and direction accuracies that met the science requirements.

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Water stress detection in the Amazon using radar

Emmerik

Steele‐Dunne

Paget

et al. 2017

Geophysical Research Letters

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The Amazon rainforest plays an important role in the global water and carbon cycle, and though it is predicted to continue drying in the future, the effect of drought remains uncertain. Developments in remote sensing missions now facilitate large‐scale observations. The RapidScat scatterometer (Ku band) mounted on the International Space Station observes the Earth in a non‐Sun‐synchronous orbit, which allows for studying changes in the diurnal cycle of radar backscatter over the Amazon. Diurnal cycles in backscatter are significantly affected by the state of the canopy, especially during periods of increased water stress. We use RapidScat backscatter time series and water deficit measurements from dendrometers in 20 trees during a 9 month period to relate variations in backscatter to increased tree water deficit. Morning radar bacskcatter dropped significantly with increased tree water deficit measured with dendrometers. This provides unique observational evidence that demonstrates the sensitivity of radar backscatter to vegetation water stress, highlighting the potential of drought detection and monitoring using radar.

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σ<sup>°</sup> Signature of the Amazon Rain Forest Obtained from the Seasat Scatterometer

Cited by 65 publications

References 1 publication

NSCAT normalized radar backscattering coefficient biases using homogenous land targets

NSCAT normalized radar backscattering coefficient biases using homogenous land targets

Postlaunch sensor verification and calibration of the NASA Scatterometer

Water stress detection in the Amazon using radar

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