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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

Authors
item Parinussa, Robert -
item Holmes, Thomas
item Crow, Wade

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 11, 2011
Publication Date: December 1, 2011
Citation: Parinussa, R., Holmes, T.R., Crow, W.T. 2011. The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations. Hydrology and Earth System Sciences. 15(10):3135-3151.

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: For several years passive microwave observations have been used to retrieve soil moisture from the Earth’s surface. Low frequency observations have the most sensitivity to soil moisture, therefore the modern Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth’s surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and Windsat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and Windsat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and Windsat to retrieve surface temperature retrievals required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer. Because of this, ancillary - and potentially less accurate - sources of surface temperature information (e.g. reanalysis data from operational weather prediction centers) must be sought to produce surface soil moisture retrievals. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the Rvalue data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature predictions on the accuracy of Windsat and AMSR-E surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of radiometric land surface temperature leads to better soil moisture anomaly estimates compared to those retrieved using MERRA land surface temperature predictions. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates retrieved using MERRA land surface temperature are superior. In addition, the surface temperature phase shifting approach is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a high degree of consistency is noted between evaluation results produced by the TC and Rvalue soil moisture verification approaches.

Last Modified: 9/21/2014
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