Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Dissertation_Ning Zhang_2020_03_18.pdf (6.65 MB)

ETD Abstract Container

Abstract Header

Soil Moisture Mapping in South Central United States by Blending In-situ, Modeled and Remote Sensing Data

Zhang, Ning
http://rave.ohiolink.edu/etdc/view?acc_num=osu1584465849013608

Abstract Details

2020, Doctor of Philosophy, Ohio State University, Geography.
Soil moisture is a critical component in the hydrologic cycle and has an important influence on weather and climate processes. Microwave remote sensing, land surface models and soil moisture networks are three sources of soil moisture information. However, none of them, at least by themselves, is adequate for providing accurate and spatially continuous soil moisture through the entire soil column. The goal of this research is to generate an accurate and spatially continuous soil moisture product in the South Central United States and to demonstrate the value of soil moisture in crop yield predictions. To achieve this goal, three main objectives are accomplished: (1) develop a general model for estimating in-situ root-zone soil moisture (RZSM) using surface measurements, (2) propose an operational method for large-scale soil moisture mapping by blending in-situ, modeled and remote sensing data, and (3) demonstrate the value of the blended soil moisture data for predicting maize yield. Results from objective 1 revealed that exponential filter performs better than the linear regression and artificial neural network methods for the estimation of RZSM. Adding meteorological variables did not improve the accuracy of RZSM estimation. Therefore, these methods can be applied at locations where only surface soil moisture data are available. The methodology can be adopted by various soil moisture monitoring networks to fill the gaps in root-zone soil moisture measurements and provide a unified and serially complete record of soil moisture measurements at multiple depths. Results from objective 2 showed that the remote sensing NASA SMAP L4 data had a large negative bias in comparison to in-situ measurements and did not improve the accuracy of the hybrid soil moisture product. Regression Kriging (RK) was a reliable approach for generating soil moisture maps based on in-situ soil moisture and gridded precipitation. The RK method performed better when the density of in situ stations was higher. When station density was low, or measurement sites were less representative, the most accurate soil moisture maps were based on a combination of the RK soil moisture and NLDAS modeled soil moisture. Lastly, Objective 3 applied soil moisture for predicting maize yield in Illinois. The results showed soil moisture products were helpful and had comparable performance to precipitation in maize yield prediction. In addition, the time-series model was found more stable and performed better with long-term data, while the spatial regression model made better usage of the spatial information and short-term data. This research demonstrates the value of the soil moisture products in yield prediction. Soil moisture can also be applied to improve irrigation efficiency, weather forecasting, drought and flood prediction, and in soil erosion and landslide studies. Overall, this doctoral research advances physical understanding of climate processes and environmental systems. The new soil moisture products generated by this study will be made freely available for the scientific community and state and federal agencies. The new soil moisture products will facilitate the validation of other soil moisture products, and they will contribute to the agricultural, climatological, hydrological, and biological communities.
Steven Quiring (Advisor)
Desheng Liu (Committee Member)
Alvaro Montenegro (Committee Member)
Darla Munroe (Committee Member)
171 p.
Geography

Recommended Citations

Citations

  • Zhang, N. (2020). Soil Moisture Mapping in South Central United States by Blending In-situ, Modeled and Remote Sensing Data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1584465849013608

    APA Style (7th edition)

  • Zhang, Ning. Soil Moisture Mapping in South Central United States by Blending In-situ, Modeled and Remote Sensing Data. 2020. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1584465849013608.

    MLA Style (8th edition)

  • Zhang, Ning. "Soil Moisture Mapping in South Central United States by Blending In-situ, Modeled and Remote Sensing Data." Doctoral dissertation, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1584465849013608

    Chicago Manual of Style (17th edition)

osu1584465849013608
258
© 2020, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.