Abstract
SWCalibrateR is a user-friendly web application. We designed SWCalibrateR to interactively estimate linear regression relationships of any couple of field data series. We specifically developed this toolbox to calibrate soil moisture sensors based on gravimetric soil moisture samples. The application has been implemented using R-shiny ( https://shiny.rstudio.com/ ). As a user you can upload your own dataset and dynamically filter it by categories like soil type, land use, soil depth and others. With SWCalibrateR you can visualise the filtered data scatter and the estimated linear model. You can diagnose your model estimate and thus easily remove outliers influencing your model estimate. SWCalibrateR handles robust estimates of linear models besides ordinary least square estimation. Additional features are an interactive data table view and mapping of the data points. Funding statement: This work was supported by the farming consulting centre for fruticulture and viticulture “Sudtiroler Beratungsring” and the research grant “MONALISA” of the Provincia Autonoma di Bolzano, Alto Adige, Ripartizione Diritto allo studio, Universita e ricerca scientifica.
Highlights
We designed SWCalibrateR to interactively estimate linear regression relationships of any couple of field data series. We developed this toolbox to calibrate soil moisture sensors based on gravimetric soil moisture samples
With SWCalibrateR we provide a user-friendly web application to facilitate this task for calibration of soil moisture sensors
Field calibration is conducted by gravimetric calibration, which is a precise but destructive method that involves taking a soil sample, weighting, oven drying, and reweighting it
Summary
The application’s current features allow the user to retrieve calibration equations interactively by filtering the data and deciding whether to use ordinary least square (OLS) or the “maximum likelihood type” MM-type estimator This means, when several soil moisture sensors are installed in heterogeneous soils and locations, SWCalibrateR permits one to find the best calibration equation by grouping the data according to user defined criteria. For our example in the drier range of the fitted soil moisture values (points #11 and #18) and for one point (#2) in wet conditions we see higher error variances and non-linearity of the model residuals (Figure 3). This plot already advertises the use of a robust model estimator. Point #2 can be clearly identified as outlier and leverage point
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