The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

McNairn, Heather; Jackson, Thomas J.; Wiseman, Grant; Bélair, Stéphane; Berg, Aaron; Bullock, Paul; Colliander, Andreas; Cosh, Michael H.; Kim, Seungbum; Magagi, Ramata; Moghaddam, Mahta; Njoku, E. G.; Adams, Justin; Homayouni, Saeid; Ojo, E. RoTimi; Rowlandson, Tracy; Shang, Jiali; Goı̈ta, Kalifa; Hosseini, Mehdi

doi:10.1109/tgrs.2014.2364913

Cited by 210 publications

(120 citation statements)

References 52 publications

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“…Moreover, the backscattering signals were found to be more sensitive to ks than to m v and kl. Those results are highly consistent with those by other methods, such as EFAST methods [15,16]. The consistency between the different models indicates again the feasibility of the proposed entropy-based method in SA.…”

Section: Sa Of Surface Conditionsupporting

confidence: 87%

“…Moreover, the backscattering signals were found to be more sensitive to ks than to and . Those results are highly consistent with those by other methods, such as EFAST methods [15,16]. The consistency between the different (2) SA by Information Entropy Figure 6 is a volumetric slice of SE with three key surface parameters, namely, ks, kl, and m v in the x, y, z directions, respectively.…”

Section: Sa Of Surface Conditionsupporting

confidence: 81%

“…Recently, global-based SA methods, such as extended Fourier amplitude sensitivity test (EFAST) [14][15][16][17][18][19][20][21] and Sobol's method [22][23][24], have begun to consider the probability distribution functions of parameters as prior knowledge in examining changes in the model response. However, these methods often suffer difficulty in illustrating SA variations for specific surface conditions or observational configurations, which is essential in establishing surface sensing algorithms.…”

Section: Introductionmentioning

confidence: 99%

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An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface

Liu¹,

Chen²

2018

Remote Sensing

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Abstract:We apply Shannon entropy, an information content measure, in sensitivity analysis (SA), stemming from the fact that the essence of SA is to preserve the maximum information content of the parameters of interest that are inverted from the radar response. Then, the sensitivity to the observation configuration and surface parameters is subsequently analyzed. Attempts are also made to explore advantages, by maximizing the information content, of dual-polarization and multi-angle in improving the parameter retrieval from radar sensing of rough surface. Simulation results show that the entropy is a good indicator of the sensitivity of the radar response to the surface parameter, as it contains information on not only the probability distribution of the scattering coefficient but also on its deviation. By information entropy, richer details, to large extent, on the scattering behavior are offered through quantitatively predicting the scattering signal saturation, evaluating the effect of using multi-polarization and multi-angle observation configuration, and identifying non-significant variables. It is found that Shannon entropy, compared to Renyi entropy, appears to better represent the sensitivity with respect to monotonic variation and narrower parameter ranges. The proposed entropy-based SA method helps to deepen our understanding of the microwave scattering behavior in response to surface parameters.

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Section: Sa Of Surface Conditionsupporting

confidence: 87%

Section: Sa Of Surface Conditionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface

Liu¹,

Chen²

2018

Remote Sensing

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“…Related calibration and validation activities for SMAP have been ongoing, such as the conduction of SMAPEx [6] and SMAPVEX12 [7] experiments. The validation of the SMAP SM products is of crucial significance, not only in view of possible applications of these data, but also to provide useful feedback for the further refinement of the retrieval algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data

Long

et al. 2017

Remote Sensing

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Abstract:The Soil Moisture Active Passive (SMAP) mission was designed to provide global mapping of soil moisture (SM) on nested 3, 9, and 36 km earth grids measured by L-band passive and active microwave sensors. The validation of SMAP SM products is crucial for the application of the products and improvement of the retrieval algorithm. Since the SMAP SM products were released, much effort has been invested in the evaluation of the SMAP radiometer SM product (SMAP_P). However, there has been little validation of SMAP radar (SMAP_A) and active/passive combined (SMAP_AP) SM products. This paper presents an evaluation of SMAP_P, SMAP_A and SMAP_AP SM products by using distributed ground observations networks in different landscapes in the Heihe River Basin of northwestern China. The standard error metrics of SMAP products and relative error are applied to measure the products' performances. The results show that the SMAP SM products exhibit consistent spatial-temporal variation with the ground measurements and typical precipitation events. Three products show various types of performance capability (e.g., active, passive and combined), surface coverage (e.g., bare, vegetated) and climatic region (e.g., cold, arid). Relatively, the SMAP_P shows the best performance, while the SMAP_A performs the worst. The best performances are observed over bare soils but with overestimation and the largest relative error, and unsatisfactory accuracies are observed over cold regions and woody vegetated surfaces with underestimation. The vegetation effect and the freezing-thawing cycle may be major factors that led to an unsatisfactory performance. Efforts on resolving the influence of these factors are expected to improve the accuracy and to promote the application of SMAP SM products over these regions. Overall, this evaluation provides an understanding of SMAP SM products over cold and arid regions, and suggestions for the further refinement of the SMAP SM retrieval algorithms.

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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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The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

Cited by 210 publications

References 52 publications

An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface

An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface

Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data

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

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