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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #316933
Title: Applications of Land Surface Temperature from Microwave Observations

Author
item HOLMES, T. - Collaborator
item Crow, Wade
item HAIN, C. - Collaborator
item Anderson, Martha
item DE JU, R.A.M - Collaborator

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 7/1/2015
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Land surface temperature (LST) is a key input for physically-based retrieval algorithms of hydrological states and fluxes. Yet, it remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observations and passive microwave observations (MW). TIR is the most commonly used approach and the method of choice to provide standard LST products for various satellite missions. MW-based LST retrievals on the other hand are not as widely adopted for land applications; currently their principle use is in soil moisture retrieval algorithms (Owe, De Jeu, and Holmes 2008). MW and TIR technologies present two highly complementary and independent means of measuring LST. MW observations have a high tolerance to clouds but a low spatial resolution, and TIR has a high spatial resolution with temporal sampling restricted to clear skies. The nature of the temperature at the very surface layer of the land makes it difficult to Combine temperature estimates between different methods. The skin temperature is characterized by a strong diurnal cycle that is dependant in timing and amplitude on the exact sensing depth and thermal properties of the vegetation. This paper builds on recent progress in characterizing the main structural components of the DTC that explain differences in TIR and MW estimates of LST (Holmes, Crow, and Hain 2013; Holmes et al. 2015). Spatial patterns in DTC timing (phase lag with solar noon) and DTC amplitude have been calculated for TIR, MW and compared to weather prediction estimates. Based on these comparisons MW LST can be matched to the TIR record. This way a global dataset of MW-based LST can be generated that is consistent with the standard TIR products. In this paper we will present results of a validation of MW LST with in situ data and applications of the improved MW LST to retrievals of soil moisture and evaporation. The impact of the new MW-LST on soil moisture retrieval is further tested within the framework of the land parameter retrieval model LPRM), a microwave based soil moisture retrieval model. LPRM soil moisture is widely distributed and used in hydrological studies.However, because of existing biases between day and night retrievals, users are currently advised to either use the day or the night observations (when using AMSR-E data). These biases are attributed to estimation errors in the effective temperature. Especially in dry soils when the sensing depth is large the effective temperature is likely to be overestimated during the day and underestimated during the night, resulting in biased soil moisture (day: too wet, night too dry). By utilization the new multi-satellite MW-LST we can show that sensing depth differences can be accounted for in a dynamic way, such that day/night differences in soil moisture are resolved.