Satellite-based GNSS-R observations from TDS-1 for soil moisture studies in agricultural vegetation landscapes

Abstract:

Soil moisture (SM) is a critical factor governing the water and energy fluxes at the land surface that are important for near-term climate forecasting, drought monitoring, crop yield estimation, and better water resources management. Remotely sensed observations at microwave frequencies are the most sensitive to changes of water in the soil. Particularly, frequencies at L-band (1-2 GHz) have been widely used for SM studies under the vegetated land covers because of their minimal atmospheric interference and attenuation by vegetation, allowing observations from the soil surface. In addition to current satellite based microwave sensors, such as the Soil Moisture Active Passive (SMAP) missions, the Global Navigation Satellite System-Reflectometry technique is capable of observing the GNSS signal reflected from the terrain that contains combined information of soil and vegetation characteristics. The technique has recently attracted attention for global SM monitoring because its receiver is small in size and light weight and can be on board the low orbit, small satellites with low power consumption and low cost. Therefore the GNSS-R remote sensing may lead to affordable multi-satellite constellations that enable improved temporal resolution for highly dynamic hydrologic conditions. The current UK Technology Demonstration Satellite (TDS-1) has been providing global GNSS-R observations since September 2014 for experimental purposes and the receiver is accessed and operated for 2 days during every 8-day cycle. In the near future, the NASA Cyclone GNSS (CYGNSS) mission, scheduled to be launched in 2016, will consist of 8 satellites observing GPS L1 signal at a frequency of 1.5754 GHz with a spatial resolution of 10-25 km and a temporal resolution of < 12 hours.

The goal of this study is to understand the impacts of SM and characteristics of agricultural vegetation on the forward scattering mechanisms of satellite-based GNSS-R observations. The GNSS-R observations from TDS-1 will be used to investigate the correlation of the GNSS-R signals to SMAP, ALOS-2 PALSAR-2, vegetation indices from MODIS, and the in-situ SM observations in an agricultural region in the Midwestern US. This study will provide insights into SM estimation in the agricultural region using satellite-based GNSS-R observations.