The FengYun-3 Microwave Radiation Imager On-Orbit Verification

Yang, Haiguang; Weng, Fuzhong; Lv, Liqing; Lu, Naimeng; Gao-feng, Liu; Bai, Ming; Qiaoyuan, Qian; He, Jiakai; Xu, Honggang

doi:10.1109/tgrs.2011.2148200

Cited by 107 publications

(54 citation statements)

References 10 publications

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“…The calibration process utilized the DD method and a third microwave radiometer record with collocated observations to both AMSR-E and AMSR2 to calibrate AMSR2 T b observations to the earlier AMSR-E baseline record. The reference sensor used for calibration is the MWRI (Microwave Radiation Imager) on-board the Chinese FengYun 3B (FY3B) satellite, which was launched in November 2010 [30]. Similar overlapping MWRI observations to both AMSR-E and AMSR2 provided a unique opportunity for comparing and calibrating the sensor records.…”

Section: Introductionmentioning

confidence: 99%

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

Kimball

Shi

et al. 2014

Remote Sensing

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Abstract:The development and continuity of consistent long-term data records from similar overlapping satellite observations is critical for global monitoring and environmental change assessments. We developed an empirical approach for inter-calibration of satellite microwave brightness temperature (T b ) records over land from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Microwave Scanning Radiometer 2 (AMSR2) using overlapping T b observations from the Microwave Radiation Imager (MWRI). Double Differencing (DD) calculations revealed significant AMSR2 and MWRI biases relative to AMSR-E. Pixel-wise linear relationships were established from overlapping T b records and used for calibrating MWRI and AMSR2 records to the AMSR-E baseline. The integrated multi-sensor T b record was largely consistent over the major global vegetation and climate zones; sensor biases were generally well calibrated, though OPEN ACCESSRemote Sens. 2014, 6 8595 residual T b differences inherent to different sensor configurations were still present. Daily surface air temperature estimates from the calibrated AMSR2 T b inputs also showed favorable accuracy against independent measurements from 142 global weather stations (R 2 ≥ 0.75, RMSE ≤ 3.64 °C), but with slightly lower accuracy than the AMSR-E baseline (R 2 ≥ 0.78, RMSE ≤ 3.46 °C). The proposed method is promising for generating consistent, uninterrupted global land parameter records spanning the AMSR-E and continuing AMSR2 missions.

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Section: Introductionmentioning

confidence: 99%

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

Kimball

Shi

et al. 2014

Remote Sensing

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“…The Earth view sampling interval is 2.08 ms, and the integration times are 15, 10.0, 7.5, 5.0, and 2.5 ms from 10.65 to 89 GHz, respectively. Since the integration time is longer than the sampling time, some overlap exists between the antenna fields of view for all the channels, except for 89 GHz where both times are nearly equal [35]. The scan geometry of the MWRI is shown in Figure 1.…”

Section: Mwri Instrument and Satellite Scanmentioning

confidence: 99%

“…Since the integration time is longer than the sampling time, some overlap exists between the antenna fields of view for all the channels, except for 89 GHz where both times are nearly equal [35]. In one scan, the MWRI can capture 254 observed brightness temperature data from the Earth [35]. An image file of 1600 × 254 pixels can be obtained with 1600 scans.…”

Section: Mwri Instrument and Satellite Scanmentioning

confidence: 99%

A Deconvolution Technology of Microwave Radiometer Data Using Convolutional Neural Networks

Zhang

Shi

et al. 2018

Remote Sensing

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Abstract:Microwave radiometer data is affected by many factors during the imaging process, including the antenna pattern, system noise, and the curvature of the Earth. Existing deconvolution methods such as Wiener filtering handle this degradation problem in the Fourier domain. However, under complex degradation conditions, the Wiener filtering results are not accurate. In this paper, a convolutional neural network (CNN) model is proposed to solve the degradation problem. The deconvolution procedure is defined as a regression problem in the spatial domain that can be solved with deep learning. For the real inverse process of microwave radiometer data, the CNN model has a more powerful reconstruction ability than Wiener filtering due to the multi-layer structure of the CNN, which enables the multiple feature transform of the data. Additionally, the complex degradation factor during the imaging process of a microwave radiometer can be solved with general framework-based learning. Experimental results demonstrated that the CNN model gains about 5 dB at the peak signal-to-noise ratio compared to the Wiener filtering deconvolution method, and can better distinguish the measured data.

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“…(1). In this paper, we only consider the emissivity towards normal direction (θ i = 0 • and φ i = 0 • ), as most calibration targets are applied to provide reference bright temperature radiation at this direction.…”

Section: Emissivity Reflectivity and Scatteringmentioning

confidence: 99%

“…The microwave calibration target is a vital instrument mounted on satellite to calibrate the space-borne radiometers, by providing referencing brightness temperature radiation [1][2][3]. For such a purpose, the calibration target is required to have a high emissivity and low surface temperature gradient [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Floquet Scattering Lobes From Microwave Calibration Targets

Gao¹,

Yuan²,

Jing³

et al. 2017

PIER B

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Abstract-The calibration target is a vital instrument for calibrating space-borne microwave radiometers, and its emissivity performance must be accurately determined before usage. Based on the Kirchhoff's law of thermal equilibrium, the emissivity of a calibration target can be determined from its electromagnetic reflectivity, which is defined as space integration of scattering. However, due to the general shape of periodic coated sharp pyramids, the scattering from calibration targets shows Floquet mode properties with scattering lobes in upper space. That phenomenon must be considered in the reflectivity measurement of calibration target, especially in the mono-static backscattering configuration. To support such backscattering-based reflectivity measurement, the Floquet mode and scattering patterns from periodic unit and finite-sized array are investigated by numerical simulations, more specifically, by the finite-difference time domain (FDTD) algorithm. The investigations include the scattering power distributions among scattering lobes from coated and bare pyramid arrays, and the ratio of total reflection to backscattering in cases of typical parameters. It is found in the millimeter wave region that the scattering power from bare pyramids is still concentrated in the backscattering lobe in the mono-static configuration, while for the coated pyramids the scattering power is distributed around Floquet modes. For the considered geometry and coating parameters, the power ratio of total scattering to backscattering can be more than 10 dB at the cared frequencies. After all, the numerical results provide referencing correction factor for actual measurement studies. It is also validated by numerical results and suggested in practice, to use periodic simulations of low computational burden to evaluate the compensation factor for the mono-static reflectivity measurement.

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The FengYun-3 Microwave Radiation Imager On-Orbit Verification

Cited by 107 publications

References 10 publications

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

A Deconvolution Technology of Microwave Radiometer Data Using Convolutional Neural Networks

Analysis on the Floquet Scattering Lobes From Microwave Calibration Targets

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