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Article

Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model

1
Department of Statistics, Iowa State University of Science and Technology, Ames, IA 50011, USA
2
Department of Agronomy, Iowa State University of Science and Technology, Ames, IA 50011, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 827; https://doi.org/10.3390/rs12050827
Received: 30 January 2020 / Revised: 28 February 2020 / Accepted: 29 February 2020 / Published: 3 March 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt. View Full-Text
Keywords: SMOS; VOD; crop development; Bayesian estimation; asymmetric Gaussian SMOS; VOD; crop development; Bayesian estimation; asymmetric Gaussian
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MDPI and ACS Style

Lewis-Beck, C.; Walker, V.A.; Niemi, J.; Caragea, P.; Hornbuckle, B.K. Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model. Remote Sens. 2020, 12, 827. https://doi.org/10.3390/rs12050827

AMA Style

Lewis-Beck C, Walker VA, Niemi J, Caragea P, Hornbuckle BK. Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model. Remote Sensing. 2020; 12(5):827. https://doi.org/10.3390/rs12050827

Chicago/Turabian Style

Lewis-Beck, Colin, Victoria A. Walker, Jarad Niemi, Petruţa Caragea, and Brian K. Hornbuckle 2020. "Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model" Remote Sensing 12, no. 5: 827. https://doi.org/10.3390/rs12050827

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