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Assessing Seasonal Backscatter Variations with Respect to Uncertainties in Soil Moisture Retrieval in Siberian Tundra Regions  [PDF]
Elin H?gstr?m,Anna Maria Trofaier,Isabelle Gouttevin,Annett Bartsch
Remote Sensing , 2014, DOI: 10.3390/rs6098718
Abstract: 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, 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
Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data  [PDF]
Maurizio Santoro,Oliver Cartus,Johan E.S. Fransson,Anatoly Shvidenko,Ian McCallum,Ronald J. Hall,André Beaudoin,Christian Beer,Christiane Schmullius
Remote Sensing , 2013, DOI: 10.3390/rs5094503
Abstract: A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Québec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.3×10 6 km 2 were mapped with a 0.01° pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1° and 0.5° was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5° was consistently within a magnitude of 20–30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available?information.
Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia  [PDF]
Xin Wang,Linlin Ge,Xiaojing Li
Remote Sensing , 2013, DOI: 10.3390/rs5073611
Abstract: Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR), and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) radar signals at horizontally emitted and received polarization (HH) for pasture monitoring at the paddock scale in order to guide farmers for better management. The pasture site is selected, in Otway, Victoria, Australia. The biomass, water content of grass, and soil moisture over this site were analyzed with these three bands of SAR images, through linear relationship between SAR backscattering coefficient, and vegetation indices Normalized Differential Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI)), together with soil moisture index (MI). NDVI, NDWI, and MI are considered as proxy of pasture biomass, plant water content, and soil moisture, respectively, and computed from optical images and climate data. SAR backscattering coefficient and vegetation indices are computed within a grass zone, defined by classification with MODIS data. The grass condition and grazing activities for specific paddocks are detectable, based on SAR backscatter, with all three wavelengths datasets. Both temporal and spatial analysis results show that the X-band SAR has the highest correlation to the vegetation indices. However, its accuracy can be affected by wet weather due to its sensitivity to the water on leaves. The C-band HH backscattering coefficient showed moderate reliability to evaluate biomass and water content of grass, with limited influence from rainfall in the dry season. The L-band SAR is the less accurate one for grass biomass measurement due to stronger penetration.
On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar  [PDF]
Niko E.C Verhoest,Hans Lievens,Wolfgang Wagner,Jesús álvarez-Mozos,M. Susan Moran,Francesco Mattia
Sensors , 2008, DOI: 10.3390/s8074213
Abstract: Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.
On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar
Niko E.C Verhoest,Hans Lievens,Wolfgang Wagner,Jes???os ????lvarez-Mozos
Sensors , 2008,
Abstract: Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.
Multi-scale estimation of surface moisture in a semi-arid region using ENVISAT ASAR radar data  [PDF]
M. Zribi,A. Chahbi,M. Shabou,Z. Lili-Chabaane
Hydrology and Earth System Sciences Discussions , 2010, DOI: 10.5194/hessd-7-8045-2010
Abstract: In this paper, we propose an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images. Our approach is based on soil moisture mapping over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events. We chose to implement this analysis over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This study is based on the reduction of a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness), during the 2008–2009 vegetation cycle.
Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation
M. Zribi, A. Chahbi, M. Shabou, Z. Lili-Chabaane, B. Duchemin, N. Baghdadi, R. Amri,A. Chehbouni
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2011,
Abstract: The present paper proposes a method for the evaluation of soil evaporation, using soil moisture estimations based on radar satellite measurements. We present firstly an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images, over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events, over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This analysis is based on a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness), during the 2008–2009 vegetation cycle. Finally, a semi-empirical approach is proposed in order to relate surface moisture to the difference between soil evaporation and the climate demand, as defined by the potential evaporation. Mapping of the soil evaporation is proposed.
Operational Mapping of Soil Moisture Using Synthetic Aperture Radar Data: Application to the Touch Basin (France)  [PDF]
Nicolas Baghdadi,Maelle Aubert,Olivier Cerdan,Laurent Franchistéguy,Christian Viel,Martin Eric,Mehrez Zribi,Jean Fran?ois Desprats
Sensors , 2007, DOI: 10.3390/s7102458
Abstract: Soil moisture is a key parameter in different environmental applications, suchas hydrology and natural risk assessment. In this paper, surface soil moisture mappingwas carried out over a basin in France using satellite synthetic aperture radar (SAR)images acquired in 2006 and 2007 by C-band (5.3 GHz) sensors. The comparisonbetween soil moisture estimated from SAR data and in situ measurements shows goodagreement, with a mapping accuracy better than 3%. This result shows that themonitoring of soil moisture from SAR images is possible in operational phase. Moreover,moistures simulated by the operational Météo-France ISBA soil-vegetation-atmospheretransfer model in the SIM-Safran-ISBA-Modcou chain were compared to radar moistureestimates to validate its pertinence. The difference between ISBA simulations and radarestimates fluctuates between 0.4 and 10% (RMSE). The comparison between ISBA andgravimetric measurements of the 12 March 2007 shows a RMSE of about 6%. Generally,these results are very encouraging. Results show also that the soil moisture estimatedfrom SAR images is not correlated with the textural units defined in the European Soil Geographical Database (SGDBE) at 1:1000000 scale. However, dependence was observed between texture maps and ISBA moisture. This dependence is induced by the use of the texture map as an input parameter in the ISBA model. Even if this parameter is very important for soil moisture estimations, radar results shown that the textural map scale at 1:1000000 is not appropriate to differentiate moistures zones.
Relating surface backscatter response from TRMM precipitation radar to soil moisture: results over a semi-arid region
H. Stephen, S. Ahmad, T. C. Piechota,C. Tang
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2010,
Abstract: The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in the Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using the Variable Infiltration Capacity (VIC) model and measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with a correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.
Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region
H. Stephen,S. Ahmad,T. C. Piechota,C. Tang
Hydrology and Earth System Sciences Discussions , 2009,
Abstract: The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.
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