LDAS-Monde sequential assimilation of satellite-derived vegetation and soil moisture products

Abstract: Within a global Land Data Assimilation System (LDAS-Monde), satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) products are jointly assimilated with

withFRAC i the geometric fraction at the node corresponding to the local surface cover of site i, Mean_WEF(ρ) i the mean footprint value at distance ρ from the node

[ 85 ] To specifically assess the impacts of the seasonal and interannual dynamics of soil moisture and seasonal vegetation on Sahelian dust emissions, the simulated area was

Open-loop (OL) water table depth simulations using the RT (top panel) and reanalyzed (bottom panel) satellite precipitation prod- ucts as forcing compared to in situ measurements

In the dashed green rectangles, secondary extrema of TWS: 1 – Mouth of the Orinoco, 2 – Sources of Parana (La Plata) and São Francisco rivers, 3 – Patagonia Icefield and

(2010) used the best linear unbiased estimate (BLUE) method to as- similate streamflow observations into the MODCOU hydro- geological model over France, which they used to update

For soil moisture in near- surface layers (4–20 cm depth), analysis increments are sim- ilar for both approaches, but EnSRF estimates tend to be wetter especially for arid places due

For example, we considered products based on several major satellite missions used for global soil moisture mapping (AMSR2, ASCAT, SMAP, and SMOS), models of various type and