Rationale Behind an Optimal Field Experiment to Assess the Suitability of Soil Moisture Retrieval Algorithms for SAR Data

Barber, M.; Grings, F.; Bruscantini, Cintia A.; Karszenbaum, H.

doi:10.1109/lgrs.2014.2362758

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…We make an initial guess for the calibration parameters by assuming < (ε x ) 2 > = < (ε y ) 2 > = < (ε z ) 2 > and subsequently solve the calibration and error equations until convergence is achieved [9,35,49].…”

Section: The Triple Collocation Error Modelmentioning

confidence: 99%

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Zhang

Ping

et al. 2017

Remote Sensing

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Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they can be routinely used at a global scale. In this paper, we evaluated the Soil Moisture Active/Passive (SMAP) and the Advanced Microwave Scanning Radiometer (AMSR2) radiometer soil moisture products against in situ measurements collected from American networks with four statistics, including the mean difference (MD), the root mean squared difference (RMSD), the unbiased root mean square error (ubRMSE) and the correlation coefficient (R). The evaluation results of SMAP and AMSR2 soil moisture products were compared. Moreover, the triple collocation (TC) error model was used to assess the error among AMSR2, SMAP and in situ data. The monthly average and daily AMSR2 and SMAP soil moisture data were analyzed. Different spatial series, temporal series and combined spatial-temporal analysis were carried out. The results reveal that SMAP soil moisture retrievals are generally better than AMSR2 soil moisture data. The remotely sensed retrievals show the best agreement with in situ measurements over the central Great Plains and cultivated crops throughout the year. In particular, SMAP soil moisture data shows a stable pattern for capturing the spatial distribution of surface soil moisture. Further studies are required for better understanding the SMAP soil moisture product.

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Section: The Triple Collocation Error Modelmentioning

confidence: 99%

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Zhang

Ping

et al. 2017

Remote Sensing

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“…Portable impedance probes measure the dielectric properties of the soil-water-air mixture from which the volumetric soil moisture can be inferred. The instrument error after performing a field-specific calibration is [15] e inst = e 2 bias +…”

Section: E Total Error In Field-based In Situ Soil Moisturementioning

confidence: 99%

“…Expression (12) defines a 100α% confidence region for the true mean value m v . In (12), the standard deviation of soil moisture at field size S is computed as [14], [15]…”

Section: E Total Error In Field-based In Situ Soil Moisturementioning

confidence: 99%

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Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm

Barber

Bruscantini

Grings

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper focused on exploiting remotely sensed active and passive observations over agricultural fields for soil moisture retrieval purposes. Co-polarized backscattering coefficients HH and VV and V-polarized brightness temperature T bV measurements were merged onto a Bayesian algorithm to enhance field-based retrieval estimates. The Bayesian algorithm relies on the use of active SAR to constrain passive information. It is assumed that observations are representative of an extent involving field sizes of about 800 m by 800 m, disregarding the scaling issues between the high resolution SAR pixel and the coarse resolution passive pixel. The integral equation model with multiple scattering at second order (IEM2M) and the ωτ model were used as forward models for the backscattering coefficients and for the V-polarized brightness temperature, respectively. The Bayesian algorithm was assessed using datasets from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEx12). Such datasets are representative of contrasting soil conditions since soil moisture spanned almost its whole feasible range from 0.10 to 0.40 cm 3 /cm 3 , at different observation geometries with incidence angles ranging from 35°to 55°. Also, the fairly large amount of measurements (97) made the dataset complete for assessment purposes. Soil moisture variability at field scale and dielectric probe error were accounted for in the comparison between retrieved estimates and in situ measurements. Performance metrics were used to quantify the agreement of the retrieval methodology to in situ information, and to assess the improvement in the combined methodology with respect to the single ones (active or passive). Overall, the root mean squared error (RMSE) showed an improvement from 0.08 to 0.11 cm 3 /cm 3 (only active) or 0.03-0.12 cm 3 /cm 3 (only passive, after bias correction) to 0.06-0.10 cm 3 /cm 3 (combined), thus, demonstrating the potential of such combined soil moisture estimates. When analyzed each field separately, RMSE is less than 0.07 cm 3 /cm 3 and correlation coefficient r is greater than 0.6 for most of the fields.

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“…With the rapid development of maritime economy and the increasing number of ships on the sea, the safety requirements of maritime transportation are becoming higher and higher. erefore, it is an urgent problem to accurately detect the position of naval vessels in coastal areas [1][2][3][4][5]. erefore, the ship detection and recognition technology is very important, and the need to use information means to improve the supervision ability and confrontation ability is increasing.…”

Section: Introductionmentioning

confidence: 99%

Achievement of Small Target Detection for Sea Ship Based on CFAR-DBN

Yuan

Chi

Hui

2022

Wireless Communications and Mobile Computing

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With the rapid development of marine exploration and marine transportation, the activities of marine ships are becoming more and more frequent. Accurate and rapid detection of the position of marine ships has very important practical and strategic significance. SAR has the characteristics of all-weather detection. It is an important means of ship detection. Aiming at the problem of fuzzy interference in sea surface ship SAR image detection, a small target image detection algorithm based on constant false alarm rate (CFAR) and depth belief network (DBN) is proposed. Firstly, according to the traditional CFAR detection principle, the whole image to be detected is detected by CFAR globally, and the index matrix is obtained, so as to improve the ship detection speed. Secondly, the output data of the hidden layer of the last layer of DBN are used as the input data of SVM, and the trained DBN model is applied to local detection, so as to improve the accuracy and robustness of ship detection. Finally, the algorithm combining CFAR and DBN is trained and applied in ship detection. Experimental results show that the accuracy of the proposed algorithm under fuzzy interference is better than that of traditional CFAR, BPNN, Fast R-CNN, and SSD512 algorithms, which proves that the robustness of the combined algorithm is significantly improved.

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Rationale Behind an Optimal Field Experiment to Assess the Suitability of Soil Moisture Retrieval Algorithms for SAR Data

Cited by 5 publications

References 8 publications

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Bayesian Combined Active/Passive (B-CAP) Soil Moisture Retrieval Algorithm

Achievement of Small Target Detection for Sea Ship Based on CFAR-DBN

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