A soil moisture‐based framework for guiding the number and location of soil moisture sensors in agricultural fields

Abstract

Soil moisture information is a key variable for guiding in-season management decisions in rainfed and irrigated agricultural systems. However, methods for deciding the number and location of soil moisture sensors (SMS) per field still remain poorly explored in the scientific literature. The goal of this study was to evaluate a quantitative framework based on soil moisture-based management zones (MZs) to determine the minimum number and tentative deployment location of SMS. Multiple spatially intensive (n > 100 observations) surveys of near-surface (0-12 cm) soil moisture were conducted during the fallow periods and early growing seasons of 2017, 2018, and 2019 on three agricultural fields using a calibrated handheld soil water reflectometer. The fuzzy C-means (FCM) clustering method was used to delineate MZs based on the soil moisture surveys, and the silhouette clustering evaluation method was used to identify the optimal number of MZs per field. Then, a sensor location index that considered the distance to the MZ boundaries and the FCM membership grade was developed to identify the tentative optimal deployment location of SMS. The proposed method effectively identified field areas with distinct soil moisture regimes and revealed the complex soil moisture spatial patterns that were not captured with elevation or soil texture alone. Dividing the fields using soil moisture-based MZ reduced the intrazone soil moisture spatial variability by about 50% compared with that of the entire field. In the three studied fields, a total of two SMS were sufficient to capture the salient soil moisture spatial regimes.

Differentiating between crop and soil effects on soil moisture dynamics

“…Effects of dense living root networks on soil hydraulic conductivity have been reported, e.g., by Scholl et al, (2014), Zhang et al (2021) and Lange et al (2013). Further soilvegetation interactions might play a role, such as soil organic matter from cover crops and plant residues (Manns et al, 2014;Rossini et al, 2021). Although this effect constituted only a minor share of soil moisture variance (Table 4), it was clearly discernible as a separate principal component.…”

Calibration and validation of soil water reflectometers

“…The Jaccard index provides an objective metric to compare differences in spatial patterns. The Jaccard index computes the size of the intersection divided by the size of the union of two finite sets and has been used in previous studies to compare the similarity of soil‐moisture‐based field management zones (Rossini et al., 2021). Since the Jaccard index is obtained for each individual field zone, a global Jaccard index was obtained by weighting the index of each zone per survey by the area of each zone.…”

“…In recent years, soil water reflectometers from the CS65x series have been adopted in core calibration–validation sites of the NASA Soil Moisture Active Passive satellite mission (Caldwell et al., 2018), in core calibration–validation sites of a new near‐surface soil moisture product for northern boreal forests developed by the European Space Agency's (ESA) Climate Change Initiative (Ikonen et al., 2018), in small catchment‐scale networks aimed at better understanding the role of soil moisture in herbaceous fuel moisture and curing rates in a tallgrass prairie in the U.S. Great Plains (Sharma et al., 2020), and have been adopted for long‐term monitoring of rootzone soil water storage by mesoscale environmental monitoring networks like the Kansas Mesonet (Patrignani et al., 2020). Similarly, portable soil water reflectometers have been used to delineate soil moisture‐based field management zones in agricultural fields (Rossini et al., 2021), explore the hydrologic connection between snowmelt runoff and streamflow (Kampf et al., 2015), and to calibrate and validate proximal soil moisture sensors like impulse radar sensors (Tan et al., 2014) and cosmic‐ray neutron detectors (Patrignani et al., 2021; Vather et al., 2019).…”

Soil moisture heterogeneity and sensor deployment in uniformly managed field with unitextural soil

“…Quantifying the number of monitoring locations can aid producers, irrigators, and practitioners to use the proper number of sensors and carry out effective irrigation management. Quantifying adequate sampling schemes to capture soil moisture variability over larger scales is a wellresearched topic (Brocca et al, 2007(Brocca et al, , 2010Famiglietti et al, 2008;Jacobs et al, 2004;Mujumdar et al, 2021;Rossini et al, 2021;Wang et al, 2008). However, methods that are operational, transferrable, easy to understand, and stakeholder centric are rarely discussed, especially in the context of public soil databases (e.g., SSURGO, a commonly used and inexpensive soil moisture technology).…”