English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. The prevalent soil moisture probe algorithms are based ona polynomial function that does not account for the variability in soilorganic matter. Users are expected to choose a model before application:either a model for mineral soil or a model for organic soil. Both approachesinevitably suffer from limitations with respect to estimating the volumetricsoil water content in soils with a wide range of organic matter content. In this study, we propose a new algorithm based on the idea that the amountof soil organic matter (SOM) is related to major uncertainties in thein situ soil moisture data obtained using soil probe instruments. To test this theory, we derived a multiphase inversion algorithm from aphysically based dielectric mixing model capable of using the SOM amount, performed a selection process from the multiphase model outcomes, and testedwhether this new approach improves the accuracy of soil moisture (SM) dataprobes. The validation of the proposed new soil probe algorithm wasperformed using both gravimetric and dielectric data from the Soil MoistureActive Passive Validation Experiment in 2012 (SMAPVEX12). The new algorithmis more accurate than the previous soil-probe algorithm, resulting in aslightly improved correlation (0.824 to 0.848), 12 % lower root mean square error (RMSE; 0.0824 to 0.0727 cm3 cm−3), and 95 % less bias (−0.0042 to 0.0001 cm3 cm−3). Theseresults suggest that applying the new dielectric mixing model together withglobal SOM estimates will result in more reliable soil moisture referencedata for weather and climate models and satellite validation.

An inverse dielectric mixing model at 50 MHz that considers soil organic carbon