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

Du, Jinyang; Kimball, J. S.; Shi, Jiancheng; Jones, L. A.; Wu, Shengli; Sun, Ruijing; Yang, Haiguang

doi:10.3390/rs6098594

Cited by 81 publications

(59 citation statements)

References 34 publications

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“…The altitude correction to the AMSR2 L1R data considers the actual surface of the Earth instead of an ideal Earth ellipsoid (T. , which helps to ensure reliable analysis of AMSR-E/2 land surface retrievals over high elevation areas, including the Qinghai-Tibetan Plateau; (2) a similar gridding process was performed on the AMSR2 L1R swath data. (3) The AMSR2 multifrequency (X-to W-band) T b retrievals were empirically calibrated against the same AMSR-E channels using similar overlapping T b observations from the Microwave Radiation Imager (MWRI) onboard the Chinese FY3B satellite (Du et al, 2014). However, in contrast to Du et al (2014) in which the T b calibration was conducted on a per grid cell basis for each frequency, polarization, and orbit, the approach used for this investigation involved calibrating within 5 × 5 grid cell windows to minimize the impact of the different sensor footprints.…”

Section: Amsr-e and Amsr2 T B Records Used For Land Parameter Retrievalsmentioning

confidence: 99%

“…The LPDR v1 has also served as a baseline for evaluating other AMSR-E algorithm retrievals (Mladenova et al, 2014) and refinements (Jang et al, 2014;Du et al, 2014). The LPDR v1 encompasses the AMSR-E record (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011), while similar observations from AMSR2 enable potential LPDR continuity (Du et al, 2014).…”

mentioning

confidence: 99%

“…The LPDR v1 database has been applied for a variety of environmental studies, including quantifying surface water inundation impacts on tundra methane emissions (Watts et al, 2014), boreal wildfire disturbance and recovery assessments (Jones et al, 2013), evaluating hydroclimatic controls on vegetation phenology (Alemu and Henebry, 2013;Guan et al, 2014), biodiversity modeling and prediction (Waltari et al, 2014), and vector-borne disease risk assessments (Chuang et al, 2012). The LPDR v1 has also served as a baseline for evaluating other AMSR-E algorithm retrievals (Mladenova et al, 2014) and refinements (Jang et al, 2014;Du et al, 2014). The LPDR v1 encompasses the AMSR-E record (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011), while similar observations from AMSR2 enable potential LPDR continuity (Du et al, 2014).…”

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confidence: 99%

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A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

Kimball

Jones

et al. 2017

Earth Syst. Sci. Data

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141

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Abstract. Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (T b ) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002-December 2015 global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (T mx and T mn ), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003) and AMSR2 (2013 observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE ≤ 0.06), PWV (R ≥ 0.91; RMSE ≤ 4.94 mm), T mx and T mn (R ≥ 0.90; RMSE ≤ 3.48 • C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE ≤ 0.06 cm 3 cm −3 ). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at

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Section: Amsr-e and Amsr2 T B Records Used For Land Parameter Retrievalsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

Kimball

Jones

et al. 2017

Earth Syst. Sci. Data

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141

142

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“…Meanwhile the instrument configurations of AMSR-E and MWRI are similar and they also have nearly simultaneous satellite overpass and data acquisition times. As shown in Table 1, except for the calibration system, the channel setting and view geometry of MWRI are almost identical to the AMSR-E (Kawanishi et al, 2003;Yang et al, 2012;Du et al, 2014). …”

Section: Methodsmentioning

confidence: 83%

Study on the Retrieval of Snow Depth From Fy3b/Mwri in the Atctic

Li¹,

Chen²,

Guan³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Snow on sea ice is a sensitive indicator of climate change, it plays an essential role in regulating surface and near surface air temperatures. Given the high albedo and low thermal conductivity, snow is regarded as one of the key reasons for the amplification of the warming in polar regions. The distributions of sea ice and snow depth are essential to the whole thermal conduction in the Arctic. This study focused on the retrieval of snow depth on sea ice from brightness temperatures of the MicroWave Radiometer Imager (MWRI) onboard the FengYun (FY)-3B satellite during the period from December 1, 2010 to April 30, 2011. After cross calibrated to the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Level 2A data, the MWRI brightness temperatures were applied to calculate the sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm. According to the proportional relationship between the snow depth and the surface scattering in 18.7 and 36.5 GHz, the snow depths were derived. In order to eliminate the influence of uncertainties in grain sizes of snow as well as sporadic weather effects, the seven-day averaged snow depths were calculated. Then the results were compared with the snow depths from the AMSR-E Level 3 Sea Ice products. The bias of differences between the MWRI and the AMSR-E Level 3 products are ranged between -1.09 and -0.32 cm，while the standard deviations and the correlation coefficients are ranged from 2.47 to 2.88 cm and from 0.78 to 0.90 for different months. As a result, it could be summarized that FY3B/MWRI showed a promising prospect in retrieving snow depth on sea ice.

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“…The method was used to generate a global soil moisture product for the period 2002-2013 based on observations from AMSR-E (June 2002-September 2011) and similar sensors, including MWRI (October 2011-June 2012) and AMSR2 (July 2012-December 2013). To ensure consistent observations over different sensors, an inter-sensor calibration technique proposed in [44] was used to calibrate AMSR2 and MWRI brightness temperature datasets. The soil moisture product was provided in a geographic latitude/longitude projection at a spatial resolution of 0.25˝(about 25 km at the Equator) and a daily temporal resolution.…”

Section: Soil Moisture Datamentioning

confidence: 99%

Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method

et al. 2016

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Abstract:Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits.

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

Cited by 81 publications

References 34 publications

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

Study on the Retrieval of Snow Depth From Fy3b/Mwri in the Atctic

Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method

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