Vegetation and surface roughness effects on AMSR-E land observations

Njoku, E. G.; Chan, S.

doi:10.1016/j.rse.2005.10.017

Cited by 260 publications

(167 citation statements)

References 20 publications

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“…Since 2000 several satellite missions measuring soil moisture have been launched: e.g., the Advanced Microwave Sounding Radiometer for EOS (AMSR-E) (Njoku and Chan, 2006), the Advanced SCATterometer (ASCAT) (Bartalis et al, 2007b), and the Soil Moisture Ocean Salinity (SMOS) . The AMSR-2 mission (Imaoka et al, 2012) is continuing the soil moisture measurements from AMSR-E, which failed in late 2011.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of satellite soil moisture products over Norway using ground-based observations

Griesfeller

Lahoz

Dorigo

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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Abstract.In this study we evaluate satellite soil moisture products from the Advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) over Norway using ground-based observations from the Norwegian Water Resources and Energy Directorate. The ASCAT data are produced using the change detection approach of Wagner et al. (1999), and the AMSR-E data are produced using the VUA-NASA algorithm (Owe et al., 2001(Owe et al., , 2008. Although satellite and ground-based soil moisture data for Norway have been available for several years, hitherto, such an evaluation has not been performed. This is partly because satellite measurements of soil moisture over Norway are complicated owing to the presence of snow, ice, water bodies, orography, rocks, and a very high coastline-to-area ratio. This work extends the European areas over which satellite soil moisture is validated to the Nordic regions. Owing to the challenging conditions for soil moisture measurements over Norway, the work described in this paper provides a stringent test of the capabilities of satellite sensors to measure soil moisture remotely. We show that the satellite and in situ data agree well, with averaged correlation (R) values of 0.72 and 0.68 for 2 ASCAT descending and ascending data vs in situ data, and 0.64 and 0.52 for AMSR-E descending and ascending data vs in situ data for the summer/autumn season (1 June -15 October), over a period of 3 years (2009)(2010)(2011). This level of agreement indicates that, generally, the ASCAT and AMSR-E soil moisture products over Norway have high quality, and would be useful for various applications, including land surface monitoring, weather forecasting, hydrological modelling, and climate studies. The increasing emphasis on coupled approaches to study the Earth System, including the interactions between the land surface and the atmosphere, will benefit from the availability of validated and improved soil moisture satellite datasets, including those over the Nordic regions.

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Section: Introductionmentioning

confidence: 99%

Evaluation of satellite soil moisture products over Norway using ground-based observations

Griesfeller

Lahoz

Dorigo

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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“…For clarification, the soil moisture product derived by the two-channel retrieval algorithm is denoted by TCRM. Jiang et al [45] and Liu et al [43] report that the accuracy of the TCRM soil moisture product is much better than that of other existing soil moisture products from AMSR-E, including the NASA standard soil moisture product [46], the Japan Aerospace Exploration Agency (JAXA) soil moisture product [4], and both the C-band and X-band soil moisture products developed using the Land Parameter Retrieval Model [47]. In this study, the TCRM soil moisture product for 2012 over the same area as the meteorological data (0˝-60˝N, 70˝E-150˝E) was used for testing the algorithm.…”

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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“…As a result, wetter soils have a diminished emissivity relative to drier soils (Njoku and Kong, 1977). Passive microwave observations are most sensitive to subsurface soil moisture at low ( 3 GHz) frequencies, and at these low frequencies, the influences of vegetation and surface roughness is also limited (Njoku and Kong, 1977;Njoku and Chan, 2005).…”

Section: A3 Ntsg Soil Moisture (Growing Season)mentioning

confidence: 99%

Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation

et al. 2013

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Abstract. Arctic field studies have indicated that the air temperature, soil moisture and vegetation at a site influence the quantity of snow accumulated, and that snow accumulation can alter growing-season soil moisture and vegetation. Climate change is predicted to bring about warmer air temperatures, greater snow accumulation and northward movements of the shrub and tree lines. Understanding the responses of northern environments to changes in snow and growingseason land surface characteristics requires: (1) insights into the present-day linkages between snow and growing-season land surface characteristics; and (2) the ability to continue to monitor these associations over time across the vast panArctic. The objective of this study was therefore to examine the pan-Arctic (north of 60 • N) linkages between two temporally distinct data products created from AMSR-E satellite passive microwave observations: GlobSnow snow water equivalent (SWE), and NTSG growing-season AMSR-E Land Parameters (air temperature, soil moisture and vegetation transmissivity). Due to the complex and interconnected nature of processes determining snow and growingseason land surface characteristics, these associations were analyzed using the modern nonparametric technique of alternating conditional expectations (ACE), as this approach does not impose a predefined analytic form. Findings indicate that regions with lower vegetation transmissivity (more biomass) at the start and end of the growing season tend to accumulate less snow at the start and end of the snow season, possibly due to interception and sublimation. Warmer air temperatures at the start and end of the growing season were associated with diminished snow accumulation at the start and end of the snow season. High latitude sites with warmer mean annual growing-season temperatures tended to accumulate more snow, probably due to the greater availability of water vapor for snow season precipitation at warmer locations. Regions with drier soils preceding snow onset tended to accumulate greater quantities of snow, likely because drier soils freeze faster and more thoroughly than wetter soils. Understanding and continuing to monitor these linkages at the regional scale using the ACE approach can allow insights to be gained into the complex response of Arctic ecosystems to climate-driven shifts in air temperature, vegetation, soil moisture and snow accumulation.

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Vegetation and surface roughness effects on AMSR-E land observations

Cited by 260 publications

References 20 publications

Evaluation of satellite soil moisture products over Norway using ground-based observations

Evaluation of satellite soil moisture products over Norway using ground-based observations

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

Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation

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