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

For understanding water and solute transport processes, knowledge about therespective hydraulic properties is necessary. Commonly, hydraulic parametersare estimated via pedo-transfer functions using soil texture data to avoidcost-intensive measurements of hydraulic parameters in the laboratory.Therefore, current soil texture information is only available at a coarsespatial resolution of 250 to 1000 m.  Here, a method is presented to derive high-resolution (15 m) spatial topsoiltexture patterns for the meso-scale Attert catchment (Luxembourg, 288 km<sup>2</sup>)from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. Aprinciple component analysis of the images reveals the most dominant thermalpatterns (principle components, PCs) that are related to 212 fractional soiltexture samples. Within a multiple linear regression framework, distributedsoil texture information is estimated and related uncertainties areassessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp)lies well within and even below the range of recent studies on soil textureestimation, while requiring sparser sample setups and a less diverse set ofbasic spatial input.  This approach will improve the generation of spatially distributed topsoilmaps, particularly for hydrologic modeling purposes, and will expand theusage of thermal remote sensing products

Estimating spatially distributed soil texture using time series of thermal remote sensing – a case study in central Europe