Progress 02/15/23 to 02/14/24
Outputs
Target Audience:We presented research at the National Soil Moisture Workshop and at the annual meetings of the American Meteorological Society and American Geophysical Union during the reporting period. These organizations include scientists, researchers from universities and federal and state agencies. Changes/Problems:The Co-PI on this project (Vahid Rahmani, Kansas State) left his faculty position at Kansas State in June 2022. He is taking a non-research related job and so he is no longer involved in this project. He was going to lead the ET-related research tasks. Therefore, we have shifted the work that was originally going to be completed at Kansas State to Ohio State. Dr. Iliyana Dobreva was leading the ET-related research tasks. Given this unexpected change in project personnel, we are behind schedule on the ET tasks. Dr. Dobreva accepted a new position as a senior data scientist at StormImpact in 2024. The has led to additional delays in completion of the ET-related project tasks. What opportunities for training and professional development has the project provided?This project supports a PhD student (Eshita Eva) in the Department of Geography at The Ohio State University. Eva has completed 3 years of coursework at The Ohio State University including Statistical Consultant Services. Therefore, she has gained experience in statistical methods. This will help her to apply the statistical and ML methods in this project. Further, she has completed five courses at OSU that are part of the Certificate of Practical Data Analytics (CPDA). This has provided her with hands-on experience in data mining and ML and DL models. During the last year, Eva presented research from this project at the AGU Fall Meeting. She has also had the opportunity to gain teaching experience through giving guest lectures at Ohio State University in a weather and climate class. This project also supports a Post-Doctoral Scholar (Iliyana Dobreva) in the Department of Geography at The Ohio State University. Dr. Dobreva has received training and mentoring from Dr. Quiring and his research group. We have established a postdoctoral mentoring plan that guides her professional development. During the last year, Dr. Dobreva presented research from this project at the National Soil Moisture Workshop and the AMS Annual Meeting. Dr. Dobreva successfully completed her postdoc in 2024 and accepted a position as a senior data scientist at StormImpact. How have the results been disseminated to communities of interest?The results of this project have been disseminated to communities of interest through conference presentations (as listed in Products) and through our website: nationalsoilmoisture.com. Our research team gave a total of 4 presentations related to this project at conferences and workshops during the last year. Eva is working on writing two journal articles that will be submitted later this year. What do you plan to do during the next reporting period to accomplish the goals?Our focus during the next reporting period will be on: (1) Finalizing the two publications on applying machine learning methods to integrate and downscale SM data to 400 m resolution over the contiguous United States. The first paper will identify which features are most important for accurately downscaling SM to field scale. The second paper will identify with machine learning methods produce the most accurate results. (2) Apply the methods developed in (1) for ET at field scale in CONUS. (3) Developing a web portal and API to serve these datasets to the farm-level decision-makers.
Impacts
What was accomplished under these goals?
To date, we completed the first goal by developing a pipeline to collect the different sources (in-situ, satellite-derived, and model-derived products) of soil moisture and other ancillary variables to downscale the soil moisture such as precipitation, mean temperature, maximum temperature, minimum temperature, dew point temperature, leaf area index (LAI), normalized difference vegetation index (NDVI), elevation, slope, aspect, national land cover dataset. We have also made substantial progress on the second goal. We developed and tested a variety of machine learning methods to integrate and downscale soil moisture data to field scale over the United States. Our preliminary results of this work were presented the 2023 AGU meeting, 2024 AMS meeting and we also will be presenting new results at the National Soil Moisture Workshop. We have drafted a paper that summarizes the results of the second major goal. We anticipate that this paper will be published in 2025.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Quiring, S. M., Dobreva, I., Leasor, Z., Eva, E., and T. Ford (2023) Relevance of forest soil moisture in the context of the NCSMMN strategy. Invited paper presented at the National Soil Moisture Workshop, Beltsville, MD, August 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Quiring, S. M., Dobreva, I. and J. Eyerman (2023) Exploring Soil Moisture Datasets from NationalSoilMoisture.com. Contributed presentation at Colorado River Climate and Hydrology Work Group, Salt Lake City, Utah, November 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Eva, E., Leasor, Z., Dobreva, I., and S. M. Quiring (2023) Evaluating the Variable Importance of 1-km Downscaled Soil Moisture Active Passive (SMAP) Satellite Product for CONUS. Contributed poster presented at the Fall Meeting of the American Geophysical Union, San Francisco, CA, December 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Quiring, S. M. (2024) Soil Moisture Monitoring. Panelist at AMS Town Hall at the 104th AMS Annual Meeting, Baltimore, MD.
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Progress 02/15/22 to 02/14/23
Outputs
Target Audience:We presented research at the National Soil Moisture Workshop and at the annual meetings of the American Meteorological Society and American Geophysical Unionduring the reporting period. These organizations include scientists, researchersfrom universities and federal and state agencies. Changes/Problems:The Co-PI on this project (Vahid Rahmani, Kansas State) left his faculty position at Kansas State in June 2022. He is taking a non-research related job and so he is no longer involved in this project. He was going to lead the ET-related research tasks. Therefore, we have shifted the work that was originally going to be completed at Kansas State to Ohio State. Dr. Iliyana Dobreva is leading the ET-related research tasks. Given this unexpected change in project personnel, we are behind schedule on the ET tasks. It is likely that we will need an extra 6 to 9 months (no cost extension) to successfully wrap up all of the project tasks. What opportunities for training and professional development has the project provided?This project supports a PhD student (Eshita Eva) in the Department of Geography at The Ohio State University. Eva has completed 2 years of coursework at The Ohio State University including Statistical Consultant Services. Therefore, she has gained experience in statistical methods. This will help her to apply the statistical and ML methods in this project. Further, she has completed five courses at OSU that are part of the Certificate of Practical Data Analytics (CPDA). This has provided her with hands-on experience in data mining and ML and DL models. During the last year, Eva presented research from this project at the AGU Fall Meeting (December 2022), AAG Annual Meeting (April 2023) and at the National Soil Moisture Workshop (August 2022). She will be presenting her research from this project at these meetings again in the coming year. This project also supports a Post-Doctoral Scholar (Iliyana Dobreva) in the Department of Geography at The Ohio State University. Dr. Dobreva has received training and mentoring from Dr. Quiring and his research group. We have established a postdoctoral mentoring plan that guides her professional development. During the last year, Dr. Dobreva presented research from this project at the National Soil Moisture Workshop (August 2022) and the AMS Annual Meeting (January 2023). How have the results been disseminated to communities of interest?The results of this project have been disseminated to communities of interest through conference presentations (as listed in Products) and through our website: nationalsoilmoisture.com. Our research team gave a total of 9 presentations related to this project at conferences and workshops during the last year. Eva is working on writing a journal article that will be submitted later this summer. We anticipate two additional manuscripts will be developed during the next year. What do you plan to do during the next reporting period to accomplish the goals?Our focus during the next reporting period will be on: (1) applying machine learning methods to integrate and downscale SM and ET data to 400 m resolution over the contiguous United States. In particular, we will be carrying out validation studies to test various ML methods for downscaling SM and ET. These tests will provide us with data-driven guidance on the most accurate methods (and how the best method varies by region, season and land cover). The results of our analysis will be presented at upcoming meetings (AGU, AMS and AAG). (2) Developing a web portal and API to serve these datasets to the farm-level decision-makers.
Impacts
What was accomplished under these goals?
In year 2 we completed the first goal by developing a pipeline to collect the different sources (in-situ, satellite-derived, and model-derived products) of soil moisture and other ancillary variables to downscale the soil moisture such as precipitation, mean temperature, maximum temperature, minimum temperature, dew point temperature, leaf area index (LAI), normalized difference vegetation index (NDVI), elevation, slope, aspect, national land cover dataset. We have also made progress on the second goal. We are developing and testing a variety of machine learning methods to integrate and downscale soil moisture data to field scale over the United States. Our preliminary results of this work were presented the 2022 AGU meeting, 2023 AMS meeting and 2023 AAG meeting. We also will be presenting new results at the National Soil Moisture Workshop (August 2023).
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Dobreva, I. Leasor, Z., Eva, E., Sunderhaft, R., Ford, T., Quiring, S. (2023) Comparison between Regression Kriging and Bayesian Deep Learning for Creating Spatial Interpolation Maps of Soil Moisture in Forests. American Meteorological Society, Denver, CO
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Eva, E.A. and S. M. Quiring (2023) Generating High-Resolution Daily Soil Moisture by Applying Machine and Deep Learning Techniques [Poster presentation]. The Fall Meeting of the American Association of Geographers, Denver, CO, March 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Eva, E.A. and S. M. Quiring (2022) Generating High-Resolution Daily Soil Moisture by Applying Downscaling Techniques [Poster presentation]. The Fall Meeting of the American Geophysical Union, Chicago, IL, December 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Eva, E.A. and S.M. Quiring (2022) Generating High-Resolution Daily Soil Moisture by Applying Machine and Deep Learning Techniques [Poster presentation]. Midwest Student Conference on Atmospheric Research, September 01-02. 2021, online.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Eva, E.A. and S. M. Quiring (2022) Applying machine and deep learning algorithm to generate fine resolution soil moisture products [Poster presentation]. The National Soil Moisture Workshop, Columbus, OH, August 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Quiring, S. M., Dobreva, I. and E. Eshita (2022) Developing high resolution national soil moisture maps. Contributed paper presented at the National Soil Moisture Workshop, Columbus, OH, August 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Quiring, S. M. (2023) Soil Moisture Data and You: When, Where, and How. Contributed panel presentation at the Annual Meeting of the American Meteorological Society, Denver, January 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Dobreva, I., Leasor, Z., Eva, E.A., Ford, T. and Quiring, S. M. (2022) Ancillary information to improve forest soil moisture mapping. Contributed paper presented at the National Soil Moisture Workshop, Columbus, OH, August 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Quiring, S. M. (2022) Exploring soil moisture datasets. Colorado River Climate and Hydrology Work Group, Salt Lake City, UT, November 2022.
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Progress 02/15/21 to 02/14/22
Outputs
Target Audience:We presented research atthe National Soil Moisture Workshop in August 2021 and to theSoil Ontology and Informatics Working Group (Earth Science Information Partners) in December 2021. These organizations include scientists, researchers from universities and federal and state agencies. Changes/Problems:The Co-PI on thisproject (Vahid Rahmani, Kansas State) will be leaving his facultyposition at Kansas State in June 2022. He istaking a non-research related job and so he will not be able to continue to be involved in this project. He was going to lead the ET-related research tasks. Therefore, we have shifted the work that was originally going to be completed at Kansas State to Ohio State. Dr. Iliyana Dobreva,a postdoc in my research group, has the necessary knowledge and skills to undertake the tasks that were orginally assigned to Dr. Rahmani. Therefore, we are well-positioned to successfully complete the research. We are a bit behind schedule on the ET tasks, but we expect to make substantial progress this year. These changes to the project have been approved by Dr. Stapleton and NIFA. Overall, there will not be change in the scope of the work or in the participant support budget category. What opportunities for training and professional development has the project provided?This project supports a PhD student (Eshita Eva) in the Department of Geography at The Ohio State University. Eva has received formal training through her graduate course work at OSU. She is also complete a graduate certificate in Data Analytics to enhance her knowledge of ML methods. Eva is also receiving informal training and mentoring from Dr. Quiring and his research group. Eva will be presenting her research from this project at the National Soil Moisture Workshop (August 2022), AGU Fall Meeting (December 2022) and AAG Annual Meeting (April 2023). This project also supports a Post-Doctoral Scholar (Iliyana Dobreva)in the Department of Geography at The Ohio State University. Dr. Dobreva receivestraining and mentoring from Dr. Quiring and his research group. We have established apostdoctoral mentoring plan that guides her professional development. Dr. Dobreva will be presenting her research from this project at the National Soil Moisture Workshop (August 2022) and the AGU Fall Meeting (December 2022). How have the results been disseminated to communities of interest?The results of this project have been dissemintated to communities of interest through conference presentations (as listed in Products) and through our website: nationalsoilmoisture.com. We have a series of papers in development and have submitted abstracts to present the research at meetings in fall 2022 and spring 2023. What do you plan to do during the next reporting period to accomplish the goals?Our focus during the next reporting period will be on: (1) completing the development of thedata pipeline to collect, quality-control and integrate SM and ET data. In particular, we will focus on ET data since the work that was originally to be completed at KSU will now be completed at OSU. These data provide the foundation for the research in this project. (2) applying machine learning methods to integrate and downscale SM and ET data to 400 m resolution over the contiguous United States. In particular, we will be carrying out validation studies to test various ML methods for downscaling SM and ET. These tests will provide us with data-driven guidance on the most accurate methods (and how the best method varies by region, season and land cover). The results of our analysis will be presented at upcoming meetings (AGU, AMS and AAG).
Impacts
What was accomplished under these goals?
Our focus in year 1 was on developing a data pipeline to collect, quality-control SMdata. To date, we have developed Python software to automatically collect, integrate, quality-control and generate national soil moisture maps using data from nearly 2000 in situ stations in the United States. We have also collected satellite-dervied soil moisture data from NASA SMAP (2016 to present) and model-derived soil moisture from NLDAS-2. Work ondeveloping a data pipeline to collect, quality-control ET data was delayed by the departure of Co-PI Rahmani from Kansas State. A post-doc has been hired at OSU to takeover the ET-related work. Over the coming months we will complete the ET data pipeline. Work also has begun on developing ML methods for downscaling the SM to field scale over CONUS. Our preliminary results of this work will be reported at the 2022 AGU meeting.
Publications
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2021
Citation:
Quiring, S. M. (2021) Building a National Soil Moisture Dataset. Soil Ontology and Informatics Working Group, Earth Science Information Partners, December 2021. Invited presentation.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Leasor, Z., Zhao, C., Liu, L. and S. M. Quiring (2021) Exploring the Benefits of Downscaled Remote Sensing Soil Moisture for Drought Monitoring. Contributed paper presented at the Annual Meeting of the American Association of Geographers, April 2021.
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