passive microwave
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2022 ◽  
Author(s):  
Peilin Song ◽  
Yongqiang Zhang ◽  
Jianping Guo ◽  
Jiancheng Shi ◽  
Tianjie Zhao ◽  
...  

Abstract. Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellite, while the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, we developed one such SSM product in China with all these characteristics. The product was generated through downscaling the AMSR-E/AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003–2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that had been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, so that the “all-weather” quality was achieved for the 1-km SSM. Daily images from this developed SSM product have quasi-complete coverage over the country during April–September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations, through a specifically developed sub-model for filling the gap between seams of neighboring PM swaths during the downscaling procedure. The product is well compared against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased RMSD ranging from 0.052 vol/vol to 0.059 vol/vol. Moreover, the evaluation results also show that the developed product outperforms the SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km, with a correlation coefficient of 0.55 achieved against that of 0.40 for the latter product. This indicates the new product has great potential to be used for hydrological community, agricultural industry, water resource and environment management.


2022 ◽  
Vol 14 (2) ◽  
pp. 276
Author(s):  
Qiurui He ◽  
Zhenzhan Wang ◽  
Jiaoyang Li

Both the Microwave Humidity and Temperature Sounder (MWHTS) and the Microwave Temperature Sounder-II (MWTS-II) operate on the Fengyun-3 (FY-3) satellite platform, which provides an opportunity to retrieve the sea surface barometric pressure (SSP) with high accuracy by fusing the observations from the 60 GHz, 118.75 GHz, and 183.31 GHz channels. The theory of retrieving SSP using passive microwave observations is analyzed, and the sensitivity test experiments of MWHTS and MWTS-II to SSP as well as the test experiments of the contributions of MWHTS and MWTS-II to SSP retrieval are carried out. The theoretical channel combination is established based on the theoretical analysis, and the SSP retrieval experiment is carried out based on the Deep Neural Network (DNN) for the theoretical channel combination. The experimental results show that the retrieval accuracy of SSP using the theoretical channel combination is higher than that of MWHTS or MWTS-II. In addition, based on the test results of the contributions of MWHTS and MWTS-II to the retrieval SSP, the optimal theoretical channel combination can be built, and can further improve the retrieval accuracy of SSP from the theoretical channel combination.


2021 ◽  
Author(s):  
Jayson Eppler ◽  
Bernhard T. Rabus ◽  
Peter Morse

Abstract. Area-based measurements of snow water equivalent (SWE) are important for understanding earth system processes such as glacier mass balance, winter hydrological storage in drainage basins and ground thermal regimes. Remote sensing techniques are ideally suited for wide-scale area-based mapping with the most commonly used technique to measure SWE being passive-microwave, which is limited to coarse spatial resolutions of 25 km or greater, and to areas without significant topographic variation. Passive-microwave also has a negative bias for large SWE. Repeat-pass synthetic aperture radar interferometry (InSAR) as an alternate technique allows measurement of SWE change at much higher spatial resolution. However, it has not been widely adopted because: (1) the phase unwrapping problem has not been robustly addressed, especially for interferograms with poor coherence and; (2) SWE change maps scaled directly from repeat-pass interferograms are not an absolute measurement but contain unknown offsets for each contiguous coherent area. We develop and test a novel method for repeat-pass InSAR based dry-snow SWE estimation that exploits the sensitivity of the dry-snow refraction-induced InSAR phase to topographic variations. The method robustly estimates absolute SWE change at spatial resolutions of < 1 km, without the need for phase unwrapping. We derive a quantitative signal model for this new SWE change estimator and identify the relevant sources of bias. The method is demonstrated using both simulated SWE distributions and a 9-year RADARSAT-2 spotlight-mode dataset near Inuvik, NWT, Canada. SWE results are compared to in situ snow survey measurements and estimates from ERA5 reanalysis. Our method performs well in high-relief areas and in areas with high SWE (> 150 mm), thus providing complementary coverage to other passive- and active-microwave based SWE estimation methods. Further, our method has the advantage of requiring only a single wavelength band and thus can utilize existing spaceborne synthetic aperture radar systems. In application, a first order analysis of SWE trends within three drainage basins suggests that differences between basin-level accumulations are a function of major landcover types, and that re-vegetation following a forest-tundra fire that occurred over 50 years ago continues to affect the spatial distribution of SWE accumulation in the study area.


2021 ◽  
pp. 1-19
Author(s):  
Xingxing Wang ◽  
Yubao Qiu ◽  
Yixiao Zhang ◽  
Juha Lemmetyinen ◽  
Bin Cheng ◽  
...  

Author(s):  
Junjun Huo ◽  
Xing Qu ◽  
Dejun Zhu ◽  
Zhe Yuan ◽  
Ziyue Zeng

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