Location: Hydrology and Remote Sensing Laboratory
Project Number: 8042-13610-029-84-I
Project Type: Interagency Reimbursable Agreement
Start Date: Sep 8, 2014
End Date: Aug 31, 2017
Satellite-based estimates of surface energy fluxes are valuable for a number of agricultural and water resource applications including: drought monitoring, yield forecasting and irrigation schedule. Past USDA ARS research has developed advanced systems for the large-scale monitoring of surface energy fluxes using thermal infrared (TIR) satellite observations. Despite this success, TIR-based retrieval methods suffer from a range of known deficiencies – especially temporal gaps in coverage arising from cloud cover. This project will explore techniques for addressing these deficiencies using microwave remote sensing techniques. The over-arching objective is to define a dual-frequency (i.e., both microwave and TIR) algorithm for estimating surface energy fluxes which is superior to existing TIR-only approaches.
The temperature of the small layer that interfaces between the land and the atmosphere is an important parameter in the processes that control the exchange of energy and water. The change in morning land surface temperature (LST) is used as a direct observational indicator of the partitioning of incoming energy into latent and sensible heat fluxes in the Atmosphere-Land Exchange Inverse (ALEXI) model, thus limiting exposure to uncertainties in the measurement of absolute land surface temperature (LST) while maintaining high sensitivity to actual changes related to heat fluxes. Current applications of ALEXI rely on thermal infrared observations from either MODIS or geostationary satellites, and temporal frequency is constrained by data gaps in LST due to clouds. As the application of ALEXI moves to daily time-steps, clouds become an increasing limitation on the parameterization of the dual source model. Current gap-filling methods involve a suboptimal propagation of the evaporative fraction as determined on clear days. Therefore, it is proposed to explore other methods for gap-filling, including the use of microwave observations with less sensitivity to clouds. One approach that may be used for gap filling is the dual-temperature difference based on the equations underlying the two-source model and therefore ALEXI. By formulating dual temperature difference (DTD) in terms of the temperature rate of change in both radiative temperature and air temperature, it reduces the sensitivity to absolute errors in temperature and, unlike ALEXI, avoids the need for atmospheric boundary layer modeling. The objective of this project is to develop and demonstrate a reliable global method that uses the DTD method for estimating heat fluxes under cloudy scenarios with microwave-based LST estimates as the input. The results will be compared to current gap-filling methods used in ALEXI, and validated against Fluxnet tower observations.