Abstract. For an assessment of the roles of soil and vegetation in the climate system, a further understanding of the flux components of H2O and CO2 (e.g., transpiration, soil respiration) and their interaction with physical conditions and physiological functioning of plants and ecosystems is necessary. To obtain magnitudes of these flux components, we applied source partitioning approaches after Scanlon and Kustas (2010; SK10) and after Thomas et al. (2008; TH08) to high-frequency eddy covariance measurements of 12 study sites covering different ecosystems (croplands, grasslands, and forests) in different climatic regions. Both partitioning methods are based on higher-order statistics of the H2O and CO2 fluctuations, but proceed differently to estimate transpiration, evaporation, net primary production, and soil respiration. We compared and evaluated the partitioning results obtained with SK10 and TH08, including slight modifications of both approaches. Further, we analyzed the interrelations among the performance of the partitioning methods, turbulence characteristics, and site characteristics (such as plant cover type, canopy height, canopy density, and measurement height). We were able to identify characteristics of a data set that are prerequisites for adequate performance of the partitioning methods. SK10 had the tendency to overestimate and TH08 to underestimate soil flux components. For both methods, the partitioning of CO2 fluxes was less robust than for H2O fluxes. Results derived with SK10 showed relatively large dependencies on estimated water use efficiency (WUE) at the leaf level, which is a required input. Measurements of outgoing longwave radiation used for the estimation of foliage temperature (used in WUE) could slightly increase the quality of the partitioning results. A modification of the TH08 approach, by applying a cluster analysis for the conditional sampling of respiration–evaporation events, performed satisfactorily, but did not result in significant advantages compared to the original method versions developed by Thomas et al. (2008). The performance of each partitioning approach was dependent on meteorological conditions, plant development, canopy height, canopy density, and measurement height. Foremost, the performance of SK10 correlated negatively with the ratio between measurement height and canopy height. The performance of TH08 was more dependent on canopy height and leaf area index. In general, all site characteristics that increase dissimilarities between scalars appeared to enhance partitioning performance for SK10 and TH08.

Agricultural management decision-making in salinization-prone environments requires efficient soil salinity monitoring methods. This is the case in the B-XII irrigation district in SW Spain, a heavy clay reclaimed marsh area where a shallow saline water table and intensively irrigated agriculture create a fragile balance between salt accumulation and leaching in the root zone, which might be disrupted by the introduction of new crops and increasing climate variability. We evaluated the potential of electromagnetic induction (EMI) tomography for field-scale soil salinity assessment in this hyper-conductive environment, using EMI and limited analytical soil data measured in 2017 and 2020 under a processing tomato–cotton–sugar beet crop rotation. Salinity effects on crop development were assessed by comparing Sentinel 2 NDVI imagery with inverted depth-specific electrical conductivity (EC). Average apparent electrical conductivity (ECa) for the 1-m depth signal was 20% smaller in 2020 than in 2017, although the spatial ECa pattern was similar for both years. Inverted depth-specific EC showed a strong correlation (R ≈ 0.90) with saturated paste extract EC (ECe), [Na+] and sodium absorption ratio (SAR), resulting in linear calibration equations with R2 ≈ 0.8 for both years and leave-one-out cross validation Nash–Sutcliffe Efficiency Coefficient, ranging from 0.57 to 0.74. Overall, the chemical parameter estimation improved with depth and soil wetness (2017), yielding 0.83 < R <0.98 at 0.9 m. The observed spatial EC distributions showed a steadily increasing inverse correlation with NDVI during the growing season, particularly for processing tomato and cotton, reaching R values of −0.71 and −0.85, respectively. These results confirm the potential of EMI tomography for mapping and monitoring soil salinity in the B-XII irrigation district, while it allows, in combination with NDVI imagery, a detailed spatial assessment of soil salinity impacts on crop development throughout the growing season. Contrary to the popular belief among farmers in the area, and despite non-saline topsoil conditions, spatial EC and subsoil salinity patterns were found to affect crop development negatively in the studied field.

Many current precision agriculture applications involve on-the-go field measurements of soil and plant properties that require accurate georeferencing. Specific equipment configuration characteristics or data transmission, reception, or logging delays may cause a mismatch between the logged data and the GPS coordinates because of time and position lags that occur during data acquisition. We propose a simple coordinate translation along the measurement tracks to correct for such positional inaccuracies, based on the local travel speed and time lag, which is estimated by minimizing the average ln-transformed absolute difference with the nearest neighbors. The correction method is evaluated using electromagnetic induction soil-sensor data for different spatial measurement layouts and densities and by comparing variograms for raw and modified coordinates. Time lags of 1 s are shown to propagate into the spatial correlation structure up to lag distances of 10 m. The correction method performs best when repeated measurements in opposite driving directions are used and worst when measurements along parallel driving tracks are only repeated at the headland turns. In the latter case, the performance of the method is further improved by limiting the search neighborhood to adjacent measurement tracks. The proposed coordinate correction method is useful for improving the positional accuracy in a wide range of soil- and plant-sensing applications, without the need to grid the data first.

&lt;p&gt;On-the-go field measurements of soil and plant characteristics, including yield, are commonplace in current Precision Agriculture applications. Yet, such measurements can be affected by positional inaccuracies that result from equipment configuration or operation characteristics (e.g. GPS antenna position with respect to sensor position) and delays in the data transmission, reception or logging. The resulting time and position lags cause a misfit between the measurements and their attributed GPS position.&lt;/p&gt;&lt;p&gt;In order to compensate for this effect a simple coordinate translation along the measurement direction is proposed, depending on the local velocity and a field- and measurement configuration-specific time lag, which is estimated by minimizing the average absolute difference between the nearest neighbors. The correction procedure is demonstrated using electromagnetic induction data with different spatial configurations and by comparing&lt;br&gt;variograms for corrected and non-corrected data.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Best results are obtained when overlapping measurements are available, obtained in opposite driving directions, while the worst results are found when no overlapping measurements exist or only those corresponding to headland turns. Further improvements in the nearest neighbor search algorithm, e.g. by imposing the search in adjacent measurement swaths are discussed. The results are valid beyond motorized soil sensing applications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgement&lt;/strong&gt;&lt;br&gt;This work is funded by the Spanish State Agency for Research through grant PID2019-104136RR-C21 and by IFAPA/FEDER through grant AVA2019.018.&lt;/p&gt;