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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 #334823

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: A quasi-global approach to improve day-time satellite surface soil moisture anomalies through land surface temperature input

item PARINUSSA, R.M. - University Of New South Wales
item DE JEU, R.A.M. - Bennekom, The Netherlands
item VAN DER SCHALKE, R. - Bennekom, The Netherlands
item Crow, Wade
item LEI, FANGNI - University Of Wuhan
item HOLMES, T. - Nasa Goddard Institute For Space Studies

Submitted to: Climate Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2016
Publication Date: 12/1/2016
Citation: Parinussa, R., De Jeu, R., Van Der Schalke, R., Crow, W.T., Lei, F., Holmes, T. 2016. A quasi-global approach to improve day-time satellite surface soil moisture anomalies through land surface temperature input. Climate Research. 4(4):50. doi:10.3390/cli4040050.

Interpretive Summary: Remotely-sensed surface soil moisture retrievals can potentially benefit a wide variety of agricultural applications including irrigation scheduling, drought monitoring and the optimization of fertilizer application. One of the key remaining challenges in developing such applications is the need for land surface temperature information as an input into soil moisture retrieval algorithms. Such temperature information must frequently be acquired from ancillary satellite sources or numerical weather prediction models. The reliability of these outside estimates of surface temperature (and thus the eventual impact of surface temperature errors on soil moisture retrievals) is largely unknown due to a shortage of ground-based validation sites for both surface temperature and soil moisture. This paper attempts to solve this problem by applying new evaluation techniques (which require less ground-based instrumentation) to global-scale surface soil moisture products generated using a variety of surface temperature datasets. In this way, we gain an understanding of which surface temperature datasets produce the best global soil moisture products. We also evaluate the benefit of various pre-processing techniques which can be applied to surface temperature datasets in order to minimize their error and produce the highest-quality surface soil moisture data sets possible. The results of this study will eventually be used to improve the quality of global soil moisture data sets and thus their utility for important agricultural applications.

Technical Abstract: Passive microwave observations from various space borne sensors have been linked to soil moisture of the Earth’s surface layer. The new generation passive microwave sensors are dedicated to retrieving this variable and make observations in the single, theoretically optimal L-band frequency (1-2 GHz). The previous generation passive microwave sensors make observations in a range of frequencies allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature which exerts a unique impact on the radiative transfer equation and has an influence on the final quality of soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation based verification technique, the so-called Rvalue. Based on initial verification results, a uniform relationship between high frequency observations (Ka-band; 36.5 GHz) and land surface temperature was recalibrated. We specifically focus on the relative quality of the day time (1.30 PM) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) as these are theoretically most challenging due to the thermal equilibrium theory and existing studies indicate larger possible gains for these observations compared their night time (1.30 AM) equivalent. Soil moisture retrievals used in this study were retrieved through the Land Parameter Retrieval Model (LPRM) and both satellite paths show, in line with theory, a unique and distinct degradation as a function of vegetation density. Both the ascending (1.30 PM) and descending (1.30 AM) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to recalibrate the existing linear regression resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the recalibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5% which indicates a better performance obtained through the recalibration. This number was confirmed through a statistical verification technique, the Triple Collocation, executed over the same domain indicating an average root mean square error reduction of 15.3 %. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the recalibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E’s day- and night-time observations.