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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #335382

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape

Author
item RAN, YOUHUA - Chinese Academy Of Sciences
item LI, XIN - Chinese Academy Of Sciences
item JIN, RUI - Chinese Academy Of Sciences
item KANG, JIAN - Chinese Academy Of Sciences
item Cosh, Michael

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2016
Publication Date: 12/1/2016
Citation: Ran, Y., Li, X., Jin, R., Kang, J., Cosh, M.H. 2016. Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape. Water Resources Research. 53(1):283-301. doi:10.1002/2015/RWR018182.

Interpretive Summary: This study introduces a new stratified temporal stability analysis which compensates for irrigation events in agricultural fields and determines appropriate criteria for application of the technique to field management. Temporal stability is useful for the quality control of in situ soil moisture networks and allows for efficiencies to be identified in network management. Anintensive study was conducted in northwestern China to analyze the length of time necessary to determine representativeness of a sampling point and it was determined to be just one irrigation cycle. This will greatly impact the ability of field operators and irrigation managers to monitor the progress of their water management decisions.

Technical Abstract: Monitoring and estimating field-mean soil moisture is very important but is limited to high-intensity irrigated agricultural landscapes due to data availability and method constraints. In this paper, the temporal stability analysis, a valuable tool for designing a soil moisture monitoring network and estimating field-mean soil moisture, was evaluated and improved based on the high-quality surface soil moisture data acquired using a wireless sensor network (WSN) in a high-intensity irrigated agricultural landscape in northwestern China. The results show that the performance of the temporal stability analysis is limited in areas where the representative error is dominated by random events, which occurred due to irrigation events in this study. This shortcoming can be effectively compensated for by using the stratified temporal stability analysis proposed in this paper. In addition, some skills and precautions are proposed when identifying the representative points used to monitor and estimate mean field soil moisture when using temporal stability analysis. The first risk is that only one representative point is identified to estimate field mean soil moisture, especially when the number of presampling points is small, because the representativeness of a certain sampling point is sensitive to the number of sampling points in some cases. Second, although the use of instantaneous sampling to identify the representativeness of a point looks tempting, it is risky. Finally, the calibration periods needed are dependent on the scale. One irrigation cycle (as a calibration phase) is sufficient for identifying representative points at both scales .because it dominate the soil moisture cycle in growing season in test field in this study.