Soil Moisture Performance Prediction for the NPOESS Microwave Imager/Sounder (MIS)

Soil moisture is a key environmental variable in the global water, energy and carbon cycles and in environmental assessment and prediction. It greatly affects a broad range of scientific and operational applications in hydrology, climate studies and agriculture. Soil moisture is also a desired input parameter to Numerical Weather Prediction (NWP) models since it controls the land-atmosphere interaction, such as dust emission and heating/moistening of the lower atmosphere. It is also a critical battlespace environment variable affecting military operations. The soil moisture content is critically related to trafficability as well as being a vital determinant of thermal and electromagnetic signatures that are vital to the operational ground mission in C4ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance).

The National Polar-orbiting Operational Environmental Satellite System's (NPOESS) Microwave Imager/Sounder (MIS) instrument is in development, with soil moisture sensing depth as one of the two Key Performance Parameters (KPPs). The other one is ocean surface wind speed precision. Based on the current design, the MIS sensor shares many channel configurations similar to the WindSat instrument, which provides an opportunity to predict MIS soil moisture performance using WindSat data.

The WindSat land surface algorithm is a physically-based algorithm used to retrieve simultaneously the soil moisture, land surface temperature and vegetation water content for a range of surface types except for snow, frozen, rainy and flood surfaces. The algorithm has been rigorously validated against global in-situ data and has demonstrated great science potential in study of soil moisture response to precipitation, ITCZ (Intertropical Convergence Zone) propagation, drought detection, and heat wave evolution. The evaluation results suggest that the WindSat data products meet IORD II threshold soil moisture requirements.

To approximate MIS performance, a land algorithm was adapted from WindSat to run on WindSat proxy data, which simulate MIS SDRs by adding Gaussian noise to WindSat SDRs according to their differences in effective NEDTs. MIS effective NEDTS are derived from 45 km CFOV (Composite Field of View) sizes that meet soil moisture HCS requirements. This proxy data approach cannot accommodate the EIA (Earth-Incidence-Angle) increase from WindSat to MIS; but a simplified analytical estimation indicates that the impact of such an EIA increase on soil moisture performance varies from low, for bare soil, to moderate, for dense vegetation. The simulation results suggest that the soil moisture performance meets the MIS algorithm specification with 40% margin given that the RFI impacts can be effectively mitigated. There is virtually no difference between MIS and WindSat performance in soil moisture retrieval based on the current MIS design.