Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

Liu, Qian; Wang, Mingyu; Zhao, Yannan

doi:10.1007/s11769-010-0218-6

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The Watershed Allied Telemetry Experimental Research (WATER) is a simultaneous airborne, satellite-borne, and ground-based remote-sensing experiment aiming to improve the observability, understanding, and predictability of hydrological and related ecological processes at a catchment scale Li et al 2011;Liu, Wang, and Zhao 2010;Wang et al 2011). WATER consists of hydrological experiments in a cold region, a forest region, and an arid region, and the experiments took place in the Heihe River Basin, a typical inland river basin in the northwest region of China.…”

Section: The Linze Observations For Semi-arid Regionmentioning

confidence: 99%

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Zhang

Tang

et al. 2015

International Journal of Remote Sensing

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2015): Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions, International Journal of Remote Sensing,Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a SoilVegetation-Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/ Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m 3 m −3 with a bias of 0.05 m 3 m −3 for the humid region and 0.08 m 3 m −3 with a bias of 0.03 m 3 m −3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.

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Section: The Linze Observations For Semi-arid Regionmentioning

confidence: 99%

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Zhang

Tang

et al. 2015

International Journal of Remote Sensing

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“…Remote sensing has played an increasingly important role in this process in recent years. The establishment of parameters in hydrological models have called upon, for example, leaf area index calculations or on surface water extents using data from optical sensors such as Landsat Thermal Mapper (Chen et al, 2004;Stisen et al, 2008;Milzow et al, 2009a) and have increasingly incorporated soil moisture values gained from Envisat ASAR in such modelling (Decharme et al, 2009;Liu et al, 2010;Saux-Picart et al, 2009). Radar-derived DEM's have been assessed and used in hydrological modelling widely (e.g.…”

Section: Natural Disaster Responsementioning

confidence: 99%

Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

O’Grady¹,

Leblanc²,

Gillieson³

2011

Preprint

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Envisat ASAR Global Monitoring Mode (GM) data are used to produce maps of the extent of the flooding in Pakistan which are made available to the rapid response effort within 24 h of acquisition. The high temporal frequency and independence of the data from cloud-free skies makes GM data a viable tool for mapping flood waters during those periods where optical satellite data is unavailable, which may be crucial to rapid response disaster planning, where thousands of lives are affected. Image differencing techniques are used, with pre-flood baseline image backscatter values being deducted from target values to eliminate regions with a permanent flood-like radar response due to volume scattering and attenuation, and to highlight the low response caused by specular reflection by open flood water. The effect of local incidence angle on the received signal is mitigated by ensuring that the deducted image is acquired from the same orbit track as the target image. Poor separability of the water class with land in areas beyond the river channels is tackled using a region-growing algorithm which seeks threshold-conformance from seed pixels at the center of the river channels. The resultant mapped extents are tested against MODIS SWIR data where available, with encouraging results

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“…Remote sensing has played an increasingly important role in this process in recent years. The establishment of parameters in hydrological models have called upon, for example, leaf area index calculations or on surface water extents using data from optical sensors such as Landsat Thermal Mapper (Chen et al, 2004;Stisen et al, 2008;Milzow et al, 2009a) and have increasingly incorporated soil moisture values gained from Envisat ASAR in such modelling Liu et al, 2010;Saux-Picart et al, 2009). Radar-derived DEM's have been assessed and used in hydrological modelling widely (e.g.…”

Section: Natural Disaster Responsementioning

confidence: 99%

Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

O’Grady

Leblanc

Gillieson

2011

Hydrol. Earth Syst. Sci.

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Abstract. Envisat ASAR Global Monitoring Mode (GM) data are used to produce maps of the extent of the flooding in Pakistan which are made available to the rapid response effort within 24 h of acquisition. The high temporal frequency and independence of the data from cloud-free skies makes GM data a viable tool for mapping flood waters during those periods where optical satellite data are unavailable, which may be crucial to rapid response disaster planning, where thousands of lives are affected. Image differencing techniques are used, with pre-flood baseline image backscatter values being deducted from target values to eliminate regions with a permanent flood-like radar response due to volume scattering and attenuation, and to highlight the low response caused by specular reflection by open flood water. The effect of local incidence angle on the received signal is mitigated by ensuring that the deducted image is acquired from the same orbit track as the target image. Poor separability of the water class with land in areas beyond the river channels is tackled using a region-growing algorithm which seeks threshold-conformance from seed pixels at the center of the river channels. The resultant mapped extents are tested against MODIS SWIR data where available, with encouraging results.

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Assimilation of ASAR data with a hydrologic and semi-empirical backscattering coupled model to estimate soil moisture

Cited by 3 publications

References 17 publications

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

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