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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 #316052
Title: An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model

Author
item FANG, L. - Collaborator
item HAIN, C. - Collaborator
item ZHAN, X. - National Oceanic & Atmospheric Administration (NOAA)
item Anderson, Martha

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/28/2015
Publication Date: 6/1/2016
Citation: Fang, L., Hain, C., Zhan, X., Anderson, M.C. 2016. An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model. International Journal of Applied Earth Observation and Geoinformation. 48:37-50. https://doi.org/10.1016/j.jag.2015.10.006.
DOI: https://doi.org/10.1016/j.jag.2015.10.006

Interpretive Summary: Reliable maps of soil moisture at regional to global scales will significantly benefit agricultural management and monitoring activities. There are many ways to estimate SM over landscapes. Landsurface models can be used to compute soil moisture content, if rainfall rates, soil texture and evapotranspiration are known accurately over the modeling domain. Satellite imagery collected in the microwave wavebands is also sensitive to surface moisture content, and is often used in soil moisture mapping. A less familiar, yet promising, technique uses satellite imagery collected in the thermal infrared bands, used to estimate landsurface temperature and evapotranspiration. The rate of water loss to the atmosphere via transpiration and soil evaporation is directly related to soil moisture content in the soil rootzone and surface layer, respectively. This paper compares soil moisture estimates from these three general methods with each other and with in-situ soil moisture measurements from the North American Soil Moisture Database. The results show that while the modeled values are less noisy, the satellite-based methods provide useful spatially detailed information about actual soil moisture that can be missed by landsurface models. We also find that the thermal technique performed as well, and in some cases better than the standard microwave soil moisture mapping approach. The three methods have complementary advantages and disadvantages; therefore, an optimal approach moving forward may be two combine these independent estimates.

Technical Abstract: Significant advances have been achieved in generating soil moisture (SM) products from satellite remote sensing and/or land surface modeling with reasonably good accuracy in recent years. However, the discrepancies among the different SM data products can be considerably large, which hampers their usage in various applications. The bias of one SM product from another is well recognized in the literature. Bias estimation and correction methods have been documented for assimilating satellite SM product into land surface and hydrologic models. Nevertheless, understanding the characteristics of each of these SM data products is required for many applications where the most accurate data products are desirable. This study inter-compares five SM data products from three different sources with each other, and evaluates them against in situ SM measurements over14-year period from 2000 to 2013. Specifically, three microwave (MW) satellite based data sets provided by the European Space Agency's Climate Change Initiative (CCI) (CCI-merged, -active and -passive products), one thermal infrared (TIR) satellite based product (ALEXI), and the Noah land surface model (LSM) simulations. The insitu SM measurements are collected from the North American Soil Moisture Database (NASMD), which involves more than 600 ground sites from a variety of networks. They are used to evaluate the accuracies of these five SM data products. In general, each of the five SM products is capable of capturing the dry/wet patterns over the study period. However, the absolute SM values among the five products vary significantly. SM simulations from Noah LSM are more stable relative to the satellite-based products. All TIR and MW satellite based products are relatively noisier than the Noah LSM simulations. Even though MW satellite based SM retrievals have been predominantly used in the past years, SM retrievals of the ALEXI model based on TIR satellite observations demonstrate skills equivalent to all the MW satellite retrievals and even slightly better over certain regions. Compared to the individual active and passive MW products, the merged CCI product exhibits higher anomaly correlation with both Noah LSM simulations and in-situ SM measurements.