Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

Kasischke, Eric S.; Bourgeau-Chavez, L. L.; Rober, Allison R.; Wyatt, Kevin H.; Waddington, J. M.; Turetsky, M. R.

doi:10.1016/j.rse.2009.04.006

Cited by 66 publications

(47 citation statements)

References 25 publications

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“…Baghdadi et al [57] applied ENVISAT ASAR data for soil moisture retrieval over bare soils. The research on application of radar data for soil moisture assessment has been published for inland wetlands at High Latitudes applying C-band SAR (Synthetic Aperture Radar) data-Reschke et al in [58], Bartsch et al in [59], and Kasischke et al in [60]. They stated that C-band backscatter may be enhanced by inundation or high levels of soil moisture, what has been presented in our study.…”

Section: Soil Moisturesupporting

confidence: 62%

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

Dąbrowska-Zielińska¹,

Budzynska²,

Tomaszewska³

et al. 2016

Preprint

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Abstract:The objectives of the study were to determine the spatial rate of CO2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive-cross polarization) and VV (Vertical Transmit/Vertical Receive-like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R 2 = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R 2 = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO2 to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring.

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Section: Soil Moisturesupporting

confidence: 62%

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

Dąbrowska-Zielińska¹,

Budzynska²,

Tomaszewska³

et al. 2016

Preprint

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“…For each sameday strip of the SAR data a channel was also created with values representing the Julian Day on which each scene was acquired ( Figure 2). We theorized that model outputs could be affected by the scene acquisition date, since changes in moisture conditions (soil and vegetation), as well as plant phenology have been shown to affect backscattering behaviour of wetlands and other land cover types [60][61][62][63].…”

Section: Processing Chain Predictor Variablesmentioning

confidence: 99%

“…These are all likely to impact the consistency of SAR and optical image values in space and in time [60][61][62][63], which would make it more difficult to classify a given land cover type, especially if the full range of values exhibited throughout the study area are not well represented in the training dataset. This could explain why better accuracies were achieved when training data from all regions were included in the model, even if the sample size was relatively small (e.g., 13 to 25 points per-class, as was the case for test (1)).…”

Section: Potential For Remote Predictive Mappingmentioning

confidence: 99%

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

et al. 2015

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Abstract:The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km 2 in size, and representing the entirety of Dease Strait, Coronation Gulf and Bathurst Inlet, Nunavut. The focus of this research was to assess the potential to operationalize shoreline sensitivity mapping to inform oil spill response and contingency planning. The impact of varying the training sample size and reducing model data load were evaluated. Results showed that acceptable accuracies could be achieved with relatively few training samples, but that higher accuracies and greater probabilities of correct class assignment were observed with larger sample sizes. Additionally, the number of inputs to the model could be greatly reduced without impacting overall performance. Optimized models reached independent accuracies of 91% for seven land cover types, and classification probabilities between 0.77 and 0.98 (values for latter represent per-class averages generated from independent validation sites). Mixed OPEN ACCESSRemote Sens. 2015, 7 13529 results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data.

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“…In particular, the research on microwave remote sensing focused on soil moisture retrieval, forest and crop biomass estimation, and ice and snow pack parameter investigation [e.g. Kasischke et al, 2009;Balenzano et al, 2011;Paloscia et al, 2013;Pettinato et al, 2013a]. Soil moisture and its temporal and spatial variations are influential parameters in both climatic and hydrologic models.…”

Section: Introductionmentioning

confidence: 99%

HydroCosmo: The Monitoring of Hydrological Parameters on Agricultural Areas by using Cosmo-SkyMed Images

Fontanelli¹,

Paloscia²,

Pampaloni³

et al. 2013

European Journal of Remote Sensing

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In this paper, the results of an experiment carried out for exploiting the capabilities of X-band Cosmo-SkyMed data in the monitoring of soil and vegetation characteristics are summarized. SAR data have been collected in two agricultural areas in Italy and compared with ground truth measurements of soil and vegetation parameters. A rather good sensitivity to vegetation features, biomass and the moisture of bare or slightly vegetated soils has been confirmed. On bare surfaces the effect of surface roughness was significant, as expected, due to the high observation frequency. Model simulations have also been performed for better explaining the backscattering response to soil moisture at this frequency. The different backscattering response according to vegetation types, which has already been observed at C-band, has been confirmed at X-band, too. The absorption due to thin vertical stems was observed on wheat crops, whereas sunflower showed a prevalent scattering behavior due to the large circular leaves.

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Effects of soil moisture and water depth on ERS SAR backscatter measurements from an Alaskan wetland complex

Cited by 66 publications

References 25 publications

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

HydroCosmo: The Monitoring of Hydrological Parameters on Agricultural Areas by using Cosmo-SkyMed Images

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