Development of an Advanced Microwave Scanning Radiometer (Amsr-E) Algorithm for Soil Moisture and Vegetation Water Content

Koike, Takao; Nakamura, Y.; Kaihotsu, Ichirow; Davaa, Gombo; Matsuura, Naoto; Tamagawa, Katsunori; Fujii, Hideyuki

doi:10.2208/prohe.48.217

Cited by 135 publications

(81 citation statements)

References 4 publications

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“…e ptq " et p ptqˆw ptq w max (4) where et p ptq is the potential evapotranspiration (mm¨day´1) calculated using the Turc method [35]:…”

Section: Soil Moisture Modelmentioning

confidence: 99%

“…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%

“…Currently, multiple soil moisture products have been generated from data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) onboard the Aqua satellite [3], the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite [4], the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite [1], and the Advanced Scatterometer (ASCAT) onboard the MetOP-A satellite [2]. A number of studies have assessed satellite soil moisture products through inter-comparisons with model simulations or other satellite soil moisture product [5][6][7][8][9], while other evaluation activities were performed against ground-based measurements [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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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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“…e ptq " et p ptqˆw ptq w max (4) where et p ptq is the potential evapotranspiration (mm¨day´1) calculated using the Turc method [35]:…”

Section: Soil Moisture Modelmentioning

confidence: 99%

Section: Soil Moisture Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Soil moisture is highly variable in space and time, controlled by precipitation, redistribution and evapotranspiration. Attempts to address this spatio-temporal variability have led to the development of remote sensing techniques employing surface soil moisture (SSM) retrieval algorithms (e.g., [8][9][10][11]). The availability and applicability of satellite data in the Arctic is in addition crucial because of the remote nature of this region.…”

Section: Introductionmentioning

confidence: 99%

Assessing Seasonal Backscatter Variations with Respect to Uncertainties in Soil Moisture Retrieval in Siberian Tundra Regions

et al. 2014

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Knowledge of surface hydrology is essential for many applications, including studies that aim to understand permafrost response to changing climate and the associated feedback mechanisms. Advanced remote sensing techniques make it possible to retrieve a range of land-surface variables, including radar retrieved soil moisture (SSM). It has been pointed out before that soil moisture retrieval from satellite data can be challenging at high latitudes, which correspond to remote areas where ground data are scarce and the applicability of satellite data of this type is essential. This study investigates backscatter variability other than associated with changing soil moisture in order to examine the possible impact on soil moisture retrieval. It focuses on issues specific to SSM retrieval in the Arctic, Remote Sens. 2014, 6 8719 notably variations related to tundra lakes. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS, 120 m) data are used to understand and quantify impacts on Metop (AAdvanced Scatterometer (ASCAT, 25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to ASAR WS availability, high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. Backscatter variations are analyzed with respect to the ASCAT footprint area. It can be shown that the low model agreement is related to water fraction in most cases. No difference could be detected between periods with floating ice (in snow off situation) and ice free periods at the chosen sites. The mean footprint backscatter is however impacted by partial short term surface roughness change. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91−0.97). Backscatter deviations of up to 5 dB can occur in areas with less than 50% water fraction and an assumed soil moisture related range (sensitivity) of 7 dB in the ASCAT data. The sensitivity is also positively correlated with water fraction in regions with low land-surface model agreement (R = 0.68). A precise quantification of the impact on soil moisture retrieval would, however, need to consider actual soil moisture changes and sensor differences. The study demonstrates that the usage of higher spatial resolution data than currently available for SSM is required in lowland permafrost environments.

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“…Different algorithms have been developed to derive soil moisture products from C-and X-band Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) data (Njoku et al, 2003;Koike et al, 2004;Owe et al, 2008) and several global soil moisture products are made available to the scientific community. Additionally, development and validation of soil moisture products from the L-band Soil Moisture and Ocean Salinity (SMOS) mission are currently ongoing (Kerr et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

doninck

Peters

Lievens

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract.A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing surface properties mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

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Development of an Advanced Microwave Scanning Radiometer (Amsr-E) Algorithm for Soil Moisture and Vegetation Water Content

Cited by 135 publications

References 4 publications

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

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

Assessing Seasonal Backscatter Variations with Respect to Uncertainties in Soil Moisture Retrieval in Siberian Tundra Regions

Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

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