Management and disturbances have significant effects on grassland forage production. When using satellite remote sensing to monitor climate impacts such as drought stress on annual forage production, minimizing these effects provides a clearer climate signal in the productivity data. The research objectives are to (1) estimate biomass expected at a certain location under specific weather conditions, (2) determine which drought indices explain the majority of inter-annual variability in the study area and (3) develop a model that estimates annual biomass early in the growing season. This study uses an established methodology to determine an expected ecosystem performance (EEP) in the Nebraska Sandhills, USA, representing the annual forage with the effects of non-climatic influences removed. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000 – 2016) of annual EEP were calculated using piecewise regression tree models of site potential and weather data. Expected biomass (EB), EEP converted to biomass in kg ha/yr, then determined the appropriate indices and date of the growing season to monitor annual expected grassland biomass. Validation revealed that EB was positively related to field-based biomass production in two upland Sandhills locations (R-squared = 0.66 and 0.57) and to regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the US Drought Monitor (USDM) that represented moisture conditions during May, June and July explained the majority of the inter-annual biomass variability in this grassland system. EB was then used to develop a spring biomass estimate model that applies several drought indices to estimate annual biomass production early in the growing season. This unique approach considers only the climate-related drought signal on productivity. The spring biomass model estimated annual EB at the end of May, giving land managers opportunities to make informed decisions about stocking rates, hay purchase needs, and other management decisions early in the growing season, minimizing their potential losses from drought. These datasets were produced through a USGS Cooperative Agreement with the University of Nebraska (Agreement Number G19AC00074) and the National Drought Mitigation Center “Drought Risk Management Research Center,’’ and is supported by the National Oceanic and Atmospheric Administration Competitive Grant no. NA15OAR4310110 in support of the National Integrated Drought Information System. Further methodological details and applications of this data and be found in Poděbradská et al. 2019, referenced above.