Here is a breakdown of what soil moisture products SERVIR is currently providing, where and what format they are in. There are two domains available for current model simulations, in East Africa and Southwest Asia (Data provided by FEWSNET), however, forecasted data are only available in East Africa. 
 
We are sharing the data in two different ways:
TIFF file download. This is served through the URL:
 https://gis1.servirglobal.net/data/locusts 
 
WMS/OpenDAP access: This is served through :
https://thredds.servirglobal.net/thredds/catalog/Locusts/catalog.html 
 
or a quick preview of the latest data, this link displays the latest 15 day forecast (using the THREDDS server above):
https://terria.servirglobal.net/#share=g-15e754f6fa9c54b05540a158c3511b37
 
 
The data we are providing is volumetric soil moisture in the top 10 cm of the ground surface (i.e. percent of the soil that has water, so the max will usually be no greater that 50% (0.5)). The data is produced from the NOAH land surface model within  NASA’s Land Information System (LIS) framework that uses satellite based inputs (see references below). From the desert locusts experts that we have talked with, locust have 2 main requirements for successful breeding: that the volumetric soil moisture be in a specific range (~0.15 – 0.25) and that soil type be pretty sandy (we’ve found sand percentages between ~55-75% for locust observations). 

Data citations:

“Soil moisture modeled data provided by SERVIR and SPoRT, FEWSNET and GSFC using NASA Land Information System (Kumar et al., 2006; McNally et al., 2017, Case et al., 2016)”
 
Kumar, S.V., C.D. Peters-Lidard, Y. Tian, P.R. Houser, J. Geiger, S. Olden, L. Lighty, J.L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E.F. Wood, and J. Sheffield, 2006: Land Information System - An interoperable framework for high resolution land surface modeling. Environ. Modeling & Software, 21, 1402-1415, doi:10.1016/j.envsoft.2005.07.004
McNally, A., K. Arsenault, S.V. Kumar, S. Shukla, P. Peterson, S. Wang, C. Funk, C.D. Peters-Lidard, and J.P. Verdin, 2017: A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific Data, 4, doi:10.1038/sdata.2017.12
Case, J., Mungai, J., Sakwa, V., Zavodsky, B.T., Srikishen, J., Limaye, A.S., Blankenship, C.B., 2016: Transitioning Enhanced Land Surface Initialization and Model Verification Capabilities to the Kenya Meteorological Service. American Meteorological Society Fall Meeting, New Orleans.