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You searched for subject:(SMAP). Showing records 1 – 7 of 7 total matches.

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University of Manitoba

1. Miller, Brian. Soil Moisture, vegetation and surface roughness impacts on high resolution L-band microwave emissivity from cropped land during SMAPVEX12.

Degree: Soil Science, 2016, University of Manitoba

 The SMAPVEX12 (Soil Moisture Active/Passive Validation Experiment 2012) was carried out over the summer of 2012 in Manitoba, Canada. The goal of the project was… (more)

Subjects/Keywords: Passive microwave; Soil moisture; SMAP; Radiometer; L-band microwave; Agriculture

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Miller, B. (2016). Soil Moisture, vegetation and surface roughness impacts on high resolution L-band microwave emissivity from cropped land during SMAPVEX12. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/31210

Chicago Manual of Style (16th Edition):

Miller, Brian. “Soil Moisture, vegetation and surface roughness impacts on high resolution L-band microwave emissivity from cropped land during SMAPVEX12.” 2016. Masters Thesis, University of Manitoba. Accessed March 21, 2019. http://hdl.handle.net/1993/31210.

MLA Handbook (7th Edition):

Miller, Brian. “Soil Moisture, vegetation and surface roughness impacts on high resolution L-band microwave emissivity from cropped land during SMAPVEX12.” 2016. Web. 21 Mar 2019.

Vancouver:

Miller B. Soil Moisture, vegetation and surface roughness impacts on high resolution L-band microwave emissivity from cropped land during SMAPVEX12. [Internet] [Masters thesis]. University of Manitoba; 2016. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1993/31210.

Council of Science Editors:

Miller B. Soil Moisture, vegetation and surface roughness impacts on high resolution L-band microwave emissivity from cropped land during SMAPVEX12. [Masters Thesis]. University of Manitoba; 2016. Available from: http://hdl.handle.net/1993/31210


Delft University of Technology

2. De Koning, D. Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands:.

Degree: 2015, Delft University of Technology

 This thesis discusses the applicability of assimilation of artificial SMAP data into a quasi steady state hydrological model to improve soil moisture estimates. The model… (more)

Subjects/Keywords: data assimilation; Ensemble Kalman filter; SMAP; soil moisture

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APA (6th Edition):

De Koning, D. (2015). Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:392abee2-abb3-4402-80a2-c1939d1e5a66

Chicago Manual of Style (16th Edition):

De Koning, D. “Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands:.” 2015. Masters Thesis, Delft University of Technology. Accessed March 21, 2019. http://resolver.tudelft.nl/uuid:392abee2-abb3-4402-80a2-c1939d1e5a66.

MLA Handbook (7th Edition):

De Koning, D. “Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands:.” 2015. Web. 21 Mar 2019.

Vancouver:

De Koning D. Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2019 Mar 21]. Available from: http://resolver.tudelft.nl/uuid:392abee2-abb3-4402-80a2-c1939d1e5a66.

Council of Science Editors:

De Koning D. Assimilation of soil moisture data with the ensemble Kalman filter for the intermediate scale soil moisture predictions in the Netherlands:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:392abee2-abb3-4402-80a2-c1939d1e5a66


Iowa State University

3. Patton, Jason Carl. Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm.

Degree: 2014, Iowa State University

 Soil moisture is important to agriculture, weather, and climate. Current soil moisture networks measure at single points, while large spatial averages are needed for some… (more)

Subjects/Keywords: microwave remote sensing; SMAP; SMOS; soil moisture; vegetation optical thickness; Remote Sensing

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APA (6th Edition):

Patton, J. C. (2014). Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/13943

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Patton, Jason Carl. “Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm.” 2014. Thesis, Iowa State University. Accessed March 21, 2019. https://lib.dr.iastate.edu/etd/13943.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Patton, Jason Carl. “Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm.” 2014. Web. 21 Mar 2019.

Vancouver:

Patton JC. Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm. [Internet] [Thesis]. Iowa State University; 2014. [cited 2019 Mar 21]. Available from: https://lib.dr.iastate.edu/etd/13943.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Patton JC. Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm. [Thesis]. Iowa State University; 2014. Available from: https://lib.dr.iastate.edu/etd/13943

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Washington

4. Mao, Yixin. Extracting hydrologic information from the Soil Moisture Active Passive (SMAP) satellite data for improved hydrologic modeling.

Degree: PhD, 2019, University of Washington

 Soil moisture is a key component of the water cycle. The launch of the new-generation satellite mission, the NASA Soil Moisture Active/Passive (SMAP) mission, in… (more)

Subjects/Keywords: data assimilation; data-driven; hydrology; modeling; SMAP; soil moisture; Hydrologic sciences; Civil engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mao, Y. (2019). Extracting hydrologic information from the Soil Moisture Active Passive (SMAP) satellite data for improved hydrologic modeling. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43340

Chicago Manual of Style (16th Edition):

Mao, Yixin. “Extracting hydrologic information from the Soil Moisture Active Passive (SMAP) satellite data for improved hydrologic modeling.” 2019. Doctoral Dissertation, University of Washington. Accessed March 21, 2019. http://hdl.handle.net/1773/43340.

MLA Handbook (7th Edition):

Mao, Yixin. “Extracting hydrologic information from the Soil Moisture Active Passive (SMAP) satellite data for improved hydrologic modeling.” 2019. Web. 21 Mar 2019.

Vancouver:

Mao Y. Extracting hydrologic information from the Soil Moisture Active Passive (SMAP) satellite data for improved hydrologic modeling. [Internet] [Doctoral dissertation]. University of Washington; 2019. [cited 2019 Mar 21]. Available from: http://hdl.handle.net/1773/43340.

Council of Science Editors:

Mao Y. Extracting hydrologic information from the Soil Moisture Active Passive (SMAP) satellite data for improved hydrologic modeling. [Doctoral Dissertation]. University of Washington; 2019. Available from: http://hdl.handle.net/1773/43340


Louisiana State University

5. Tu, Le. Downscaling SMAP Soil Moisture Data Using MODIS Data.

Degree: MS, Remote Sensing, Louisiana State University

  Soil moisture level is an important index in studying environmental changes. High resolution soil moisture data is in high demand for agricultural and weather… (more)

Subjects/Keywords: SMAP; Downscaling; MODIS; Multivariate statistical regression; DEM; TWI

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tu, L. (n.d.). Downscaling SMAP Soil Moisture Data Using MODIS Data. (Masters Thesis). Louisiana State University. Retrieved from https://digitalcommons.lsu.edu/gradschool_theses/4847

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16th Edition):

Tu, Le. “Downscaling SMAP Soil Moisture Data Using MODIS Data.” Masters Thesis, Louisiana State University. Accessed March 21, 2019. https://digitalcommons.lsu.edu/gradschool_theses/4847.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7th Edition):

Tu, Le. “Downscaling SMAP Soil Moisture Data Using MODIS Data.” Web. 21 Mar 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Tu L. Downscaling SMAP Soil Moisture Data Using MODIS Data. [Internet] [Masters thesis]. Louisiana State University; [cited 2019 Mar 21]. Available from: https://digitalcommons.lsu.edu/gradschool_theses/4847.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

Tu L. Downscaling SMAP Soil Moisture Data Using MODIS Data. [Masters Thesis]. Louisiana State University; Available from: https://digitalcommons.lsu.edu/gradschool_theses/4847

Note: this citation may be lacking information needed for this citation format:
No year of publication.


University of Colorado

6. Shellito, Peter Judd. Surface Soil Moisture Dynamics from Remote Sensing, Modeling, and in Situ Observations.

Degree: PhD, 2017, University of Colorado

  Soil moisture content plays a central role in the coupled water and energy exchange between the land surface and the atmosphere. It also controls… (more)

Subjects/Keywords: land surface modeling; remote sensing; smap; soil moisture; soil moisture monitoring; Hydrology; Remote Sensing; Soil Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shellito, P. J. (2017). Surface Soil Moisture Dynamics from Remote Sensing, Modeling, and in Situ Observations. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/geol_gradetds/149

Chicago Manual of Style (16th Edition):

Shellito, Peter Judd. “Surface Soil Moisture Dynamics from Remote Sensing, Modeling, and in Situ Observations.” 2017. Doctoral Dissertation, University of Colorado. Accessed March 21, 2019. https://scholar.colorado.edu/geol_gradetds/149.

MLA Handbook (7th Edition):

Shellito, Peter Judd. “Surface Soil Moisture Dynamics from Remote Sensing, Modeling, and in Situ Observations.” 2017. Web. 21 Mar 2019.

Vancouver:

Shellito PJ. Surface Soil Moisture Dynamics from Remote Sensing, Modeling, and in Situ Observations. [Internet] [Doctoral dissertation]. University of Colorado; 2017. [cited 2019 Mar 21]. Available from: https://scholar.colorado.edu/geol_gradetds/149.

Council of Science Editors:

Shellito PJ. Surface Soil Moisture Dynamics from Remote Sensing, Modeling, and in Situ Observations. [Doctoral Dissertation]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/geol_gradetds/149


University of New South Wales

7. Huang, Chee Cheun. Automatic phonetic-unit selection and modelling techniques for forensic voice comparison.

Degree: Electrical Engineering & Telecommunications, 2013, University of New South Wales

 Acoustic phonetic approaches to Forensic Voice Comparison (FVC) have traditionally involved a labor-intensive process of identifying speaker-discriminative phonetic units in speech for speaker characterization in… (more)

Subjects/Keywords: Forensic Voice Comparison (FVC); Automatic phonetic-unit selection; Hidden-Markov-Model (HMM); Gaussian Mixture Model – Universal Background Model (GMM-UBM) modelling; Separate MAP (SMAP) adaptation; Supervector regression methods; Support Vector Machine (SVM)

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APA (6th Edition):

Huang, C. C. (2013). Automatic phonetic-unit selection and modelling techniques for forensic voice comparison. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53105 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11791/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Huang, Chee Cheun. “Automatic phonetic-unit selection and modelling techniques for forensic voice comparison.” 2013. Doctoral Dissertation, University of New South Wales. Accessed March 21, 2019. http://handle.unsw.edu.au/1959.4/53105 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11791/SOURCE01?view=true.

MLA Handbook (7th Edition):

Huang, Chee Cheun. “Automatic phonetic-unit selection and modelling techniques for forensic voice comparison.” 2013. Web. 21 Mar 2019.

Vancouver:

Huang CC. Automatic phonetic-unit selection and modelling techniques for forensic voice comparison. [Internet] [Doctoral dissertation]. University of New South Wales; 2013. [cited 2019 Mar 21]. Available from: http://handle.unsw.edu.au/1959.4/53105 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11791/SOURCE01?view=true.

Council of Science Editors:

Huang CC. Automatic phonetic-unit selection and modelling techniques for forensic voice comparison. [Doctoral Dissertation]. University of New South Wales; 2013. Available from: http://handle.unsw.edu.au/1959.4/53105 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11791/SOURCE01?view=true

.