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Proceedings Paper

Temporal variation (seasonal and interannual) of vegetation indices of maize and soybeans across multiple years in central Iowa
Author(s): J. H. Prueger; J. L. Hatfield
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Paper Abstract

Remotely sensed reflectance parameters from corn and soybean surfaces can be correlated to crop production. Surface reflectance of a typical Upper Midwest corn /soybean region in central Iowa across multiple years reveal subtle dynamics in vegetative surface response to a continually varying climate. From 2006 through 2014 remotely sensed data have been acquired over production fields of corn and soybeans in central IA, U.S.A. with the fields alternating between corn and soybeans. The data have been acquired using ground-based radiometers with 16 wavebands covering the visible, near infrared, shortwave infrared wavebands and combined into a series of vegetative indices. These data were collected on clear days with the goal of collecting data at a minimum of once per week from prior to planting until after fall tillage operations. Within each field, five sites were established and sampled during the year to reduce spatial variation and allow for an assessment of changes in the vegetative indices throughout the growing season. Ancillary data collected for each crop included the phenological stage at each sampling date along with biomass sampled at the onset of the reproductive stage and at physiological maturity. Evaluation of the vegetative indices for the different years revealed that patterns were related to weather effects on corn and soybean growth. Remote sensing provides a method to evaluate changes within and among growing seasons to assess crop growth and development as affected by differences in weather variability.

Paper Details

Date Published: 4 September 2015
PDF: 13 pages
Proc. SPIE 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII, 96100K (4 September 2015); doi: 10.1117/12.2187049
Show Author Affiliations
J. H. Prueger, Agricultural Research Service (United States)
J. L. Hatfield, Agricultural Research Service (United States)


Published in SPIE Proceedings Vol. 9610:
Remote Sensing and Modeling of Ecosystems for Sustainability XII
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

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