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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 #374716

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Estimating the number of reference sites necessary for the validation of global soil moisture products

item MONTZKKA, C. - Juelich Research Center
item BOGENA, H. - Juelich Research Center
item HERBST, M. - Juelich Research Center
item Cosh, Michael
item JAGHUBER, T. - Collaborator
item VEREECKEN, H. - Juelich Research Center

Submitted to: Geoscience and Remote Sensing Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2020
Publication Date: 7/9/2020
Citation: Montzkka, C., Bogena, H., Herbst, M., Cosh, M.H., Jaghuber, T., Vereecken, H. 2020. Estimating the number of reference sites necessary for the validation of global soil moisture products. Geoscience and Remote Sensing Letters. 99:1-5.

Interpretive Summary: International standards for soil moisture remote sensing product validation is necessary to provide confidence and context for the application of these products into decision support systems and operations. Because of the variability of soil moisture at the surface, a large number of ground validation stations are necessary, depending on the heterogeneity of the surface. A methodology was developed for determining sufficient ground validation in relation to a 36km satellite product. This study will benefit future network development of soil moisture networks for monitoring and validation.

Technical Abstract: The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across satellite-derived products from different platforms, sensors, and algorithms with reference measurements from in situ networks. Soil moisture exhibits a high variability in space that challenges in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in soil texture. By machine learning methods utilizing soil profile measurements and remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the SMAP 36 km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate coarse soil moisture products. For most SMAP grid cells three to four stations are adequate to estimate the mean soil moisture for validation, however, also regions were identified where 80 stations are necessary.