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

A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the soil moisture active passive (SMAP) enhanced product (SPL2SMP$\_$E) from 9 to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from moderate resolution imaging spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid-this MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP core validation sites (CVS), the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network (CRN). Results were also compared with the baseline SPL2SMP$\_$E and the SMAP/Sentinel-1 (SPL2SMAP$\_$S) 1 km product. Overall, the unbiased root-mean-square error (ubRMSE) of the disaggregated SM at the CVS using the TI approach is approximately 0.04 $\text{m}^3/\text{m}^3$, which is the SMAP mission requirement for the baseline products. The TI approach outperforms the SMAP/Sentinel SL2SMAP$\_$S 1 km product by approximately 0.02 $\text{m}^3/\text{m}^3$. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMP$\_$E and SPL2SMAP$\_$S by about 0.01 and 0.02 $\text{m}^3/\text{m}^3$, indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.

Assessing Disaggregated SMAP Soil Moisture Products in the United States