A pre-operational algorithm for the retrieval of snow depth and soil moisture from AMSR-E data

Santi, E.; Pettinato, S.; Brogioni, Marco; Macelloni, Giovanni; Montomoli, Francesco; Paloscia, S.; Pampaloni, P.

doi:10.1109/igarss.2010.5652009

Cited by 2 publications

(2 citation statements)

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“…• Masking of deserts, dense vegetation, snow cover and open water, where SMC cannot be reliably estimated. This process is basically performed by using PI X for dense vegetation (PI X < 0.05, corresponding roughly to a vegetation water content of about 3-4 Kg/m 2 ) and for deserts (PI X > 0.1), FI > 4 for snow cover (Paloscia and Pampaloni, 1988;Macelloni et al, 2003;Santi et al, 2010Santi et al, , 2012. • Generation of output SMC maps and report files containing the output SMC estimates for the desired test areas.…”

Section: Description Of the Algorithms Hydroalgomentioning

confidence: 99%

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

et al. 2015

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A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Global Change Observation Mission-Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) ≤0.06 m 3 /m 3 and Bias <0.02 m 3 /m 3 . These results expand on previous investigations using different algorithms and sites. The novelty of the paper consists of the fact that it is the first intercomparison of the HydroAlgo algorithm with a more traditional retrieval algorithm, which offers an approach to higher spatial resolution products.

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Section: Description Of the Algorithms Hydroalgomentioning

confidence: 99%

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

et al. 2015

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“…Passive microwave remote sensing has been used to estimate snow depth and snow water equivalent because it can penetrate cloud cover and interact with the snowpack at good temporal (daily) and moderate spatial (~25km) resolution (Derksen et al 2010). At present, there are three daily passive microwave (PM) snow depth or snow water equivalent products covering China: the global SWE product from the National Snow and Ice Data Center (NSIDC) (Santi et al 2010;Tedesco et al 2010), the Northern Hemisphere SWE product from the European Space Agency (ESA) (Pulliainen 2006), and the snow depth product for China from the West Data Center in China . However, there is a problem associated with both the brightness temperature difference algorithms and the assimilation algorithm is that it is difficult to determine their accuracy (Liu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Validation of the Daily Passive Microwave Snow Depth Products Over Northern China

Qiao

Wang

et al. 2018

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

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ABSTRACT:Passive microwave sensors have the capability to provide information on snow depth (SD), which is critically important for hydrological modeling and water resource management. However, the different algorithms used to produce SD products lead to discrepancies in the data. To determine which products might be most suitable for Northern China, this paper assesses the accuracy of the existing snow depth products in the period of 2002-2011. By comparing three daily snow depth products, including NSIDC, WESTDC and ESA Globsnow, with snow cover product and meteorological stations data, the accuracies of the different SD products are analyzed for different snow class and forest cover fraction. The results show that comparison between snow cover derived from snow depth of NSIDC, ESA GlobSnow and WESTDC with snow cover product shows that accuracy of WESTDC and ESA GlobSnow in snow cover detecting can reach 0.70. Compared to meteorological stations data below 20cm, NSIDC consistently overestimate, WESTDC and ESA Globsnow underestimate, furthermore the product from WESTDC is superior to the others. The three products have the same tendency of significant undervaluation over 20cm. The WESTDC is superior to the ESA Globsnow and NSIDC in non-forest regions, whereas the ESA GlobSnow estimate is superior to the WESTDC and NSIDC in forest regions. As for the prairie and alpine snow, WESTDC has smaller bias and RMSE, meanwhile Globsnow has advantages in the snow depth retrieval in tundra and taiga snow. Therefore, we should choose the more suitable snow depth products according to different needs.

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A pre-operational algorithm for the retrieval of snow depth and soil moisture from AMSR-E data

Cited by 2 publications

References 12 publications

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

Validation of the Daily Passive Microwave Snow Depth Products Over Northern China

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