Within-field soil moisture variability and time-invariant spatial structures of agricultural fields in the US Midwest

Authors: YANG, Y - University Of Illinois; PENG, B - University Of Illinois; GUAN, K - Department Of Natural Resources; PAN, M - University Of California, San Diego; FRANZ, T - University Of Nebraska; Cosh, Michael; Bernacchi, Carl

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/26/2024
Publication Date: 4/26/2024
Citation: Yang, Y., Peng, B., Guan, K., Pan, M., Franz, T., Cosh, M.H., Bernacchi, C.J. 2024. Within-field soil moisture variability and time-invariant spatial structures of agricultural fields in the US Midwest. Vadose Zone Journal. Article e20337. https://doi.org/10.1002/vzj2.20337.
DOI: https://doi.org/10.1002/vzj2.20337

Interpretive Summary: Farmers rely on understanding how water is distributed in the soil across their fields. This information helps them make better decisions about when and where to water, which can lead to better crop growth. Up until now, it's been hard to get a detailed picture of soil moisture at a small scale across large fields, and the methods for measuring this moisture from space using satellites were not well understood. Researchers conducted an in-depth study in the U.S. Midwest, across three typical commercial agricultural fields. By taking frequent soil moisture measurements in the fields and comparing them with satellite data, they were able to better understand how soil moisture varies across the fields over time. Specifically, they found that moisture levels remained stable in the soil, and they discovered that certain satellite sensors, such as SWIR1 from Sentinel-2, can accurately reflect soil moisture patterns at small scales. This study's findings open doors for more advanced ways to estimate soil moisture levels using remote sensing data from satellites. It could help create more accurate and timely soil moisture maps, which would be valuable for farmers. With this knowledge, farmers can manage their fields more efficiently, providing the right amount of water where and when it's needed, leading to healthier crops and potentially saving both water and money. In a broader sense, this research contributes to the ongoing efforts to increase agricultural sustainability and improve food production techniques.

Technical Abstract: Understanding soil moisture variability and estimating high-resolution soil moisture at sub-field to field scales is critical for agricultural research and applications. However, systematic investigation of sub-field scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within-field soil moisture distribution over typical cropland in the U.S. Midwest, and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at sub-field scale. Specifically, we conducted soil moisture field experiments and measurements in three typical commercial agricultural fields (~85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest. In each field, dense soil moisture samples (spaced at 50-60 m) were obtained for two dry down events in May and July 2021, and multiple long-term soil moisture stations were installed. We found prominent temporal stability of soil moisture at within-field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found surface reflectance of shortwave infrared bands, such as SWIR1 from Sentinel-2, can capture relative surface soil moisture patterns at within-field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within-field soil moisture dynamics could potentially help future research on high resolution soil moisture estimation with multi-source remote sensing data.