Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and Preliminary Results

Magagi, Ramata; Berg, Aaron; Goı̈ta, Kalifa; Bélair, Stéphane; Jackson, Thomas J.; Toth, Brenda; Walker, Anne; McNairn, Heather; O'Neill, Peggy E.; Moghaddam, Mahta; Gherboudj, Imen; Colliander, Andreas; Cosh, Michael H.; Burgin, Mariko; Fisher, Joshua B.; Kim, Seungbum; Mladenova, I. E.; Djamai, Najib; Rousseau, Louis-Philippe; Belanger, J.; Shang, Jiali; Merzouki, A.

doi:10.1109/tgrs.2012.2198920

Cited by 78 publications

(70 citation statements)

References 30 publications

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“…As for the profile length, for instance, experimental and numerical studies [6][7][8] suggested the need for long profiles (400l or ∼25 m) whereas other studies, focused on soil moisture retrieval rather than on soil roughness measurements [9][10][11][12][13][14], employed 1-m-long profiles, where no clear relationship between the reliability of the retrieved estimates and the profile length could be found. As for the sampling interval ∆x, a rule of thumb recommended in [3] (Volume II) stated that ∆x < λ 10 provided that the corresponding change in height z is ∆z λ.…”

Section: Introductionmentioning

confidence: 99%

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

et al. 2016

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Abstract:The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm) to small ones (1 mm), with several intermediate values.Large-and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C-or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band.

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Section: Introductionmentioning

confidence: 99%

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

et al. 2016

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“…SMOS provides multi-angular global microwave TB observations, from 2010 until the present, and its SM products have the high accuracy of 0.04 m 3 /m 3 . A great deal of research has evaluated SMOS SM products and demonstrated its high accuracy [45][46][47][48][49][50][51][52][53][54][55][56]. For the consideration of long time series, we choose AMSR-E/AMSR2 data as the training dataset because it has the same configuration and constitutes a continuous time series, for AMSR-E, lasting from 2003 to 2011, and AMSR2, lasting from 2012 to the present.…”

Section: Introductionmentioning

confidence: 99%

Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index

et al. 2017

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This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 brightness temperature (TB) data and L3 SM products. The BPNNs on each grid were trained over July 2010-June 2011, and the entire year of 2013, with SMOSL3sm as a training target, and taking the reflectivities (Rs) of the C/X/Ku/Ka/Q bands, and the MVI from AMSR-E/AMSR2 TB data, as input, in which the MVI is used to correct for vegetation effects. The training accuracy of networks was evaluated by comparing soil moisture products produced using BPNNs (NNsm hereafter) with SMOSL3sm during the BPNN training period, in terms of correlation coefficient (CC), bias (Bias), and the root mean square error (RMSE). Good global results were obtained with CC = 0.67, RMSE = 0.055 m 3 /m 3 and Bias = −0.0005 m 3 /m 3 , particularly over Australia, Central USA, and Central Asia. With these trained networks over each pixel, a global and long-term soil moisture time series, i.e., 2003-2015, was built using AMSR-E TB from 2003 to 2011 and AMSR2 TB from 2012 to 2015. Then, NNsm products were evaluated against in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (SCANsm). The results show that NNsm has a good agreement with in situ data, and can capture the temporal dynamics of in situ SM, with CC = 0.52, RMSE = 0.084 m 3 /m 3 and Bias = −0.002 m 3 /m 3 . We also evaluate the accuracy of NNsm by comparing with AMSR-E/AMSR2 SM products, with results of a regression method. As a conclusion, this study provides a promising BPNN method adopting MVI to rebuild a long-term SM time series, and this could provide useful insights for the future Water Cycle Observation Mission (WCOM).

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“…The available data were acquired within the Canadian Experiment for Soil Moisture in 2010 [32] and cover the time period from 2-14 June 2010. The test site near Kenaston, Saskatchewan, Canada (51˝30 .…”

Section: Study Sitementioning

confidence: 99%

“…This percentage is likely to have been even larger due to the wet conditions before and during the campaign [33]. The majority of the agricultural fields had been tilled prior to the measurements and several were covered with crop residues to varying degrees [32].…”

Section: Study Sitementioning

confidence: 99%

A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence

2015

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Changes in soil moisture between two radar acquisitions can impact the observed coherence in differential interferometry: both coherence magnitude |γ| and phase φ are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and sign, as well as dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves-upon calibration with airborne L band data-median correlations ρ at HH polarization of 0.77 for the phase φ, of 0.50 for |γ|, and for the phase triplets Ξ of 0.56. The predictions are sensitive to the choice of dielectric mixing model, in particular the absorptive properties; the differences between the mixing models are found to be partially compensatable by varying the relative importance of surface and volume scattering. However, for half of the agricultural fields the Hallikainen mixing model cannot reproduce the observed sensitivities of the phase to soil moisture. In addition, the first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels HH and VV. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of Remote Sens. 2015, 7 7572 soil moisture changes on the HH and VV DInSAR signals. Hence it may prove useful in separating the deformations from the moisture signals, thus yielding improved displacement estimates or new ways for inferring soil moisture.

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Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10): Overview and Preliminary Results

Abstract: Abstract-

Cited by 78 publications

References 30 publications

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index

A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence

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