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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #137041

Title: COMBINING REMOTE SENSING AND CROP GROWTH MODELS TO ESTIMATE WITHIN-FIELD VARIABILITY

Author
item HONG, S
item Sudduth, Kenneth - Ken
item Kitchen, Newell
item FRAISSE, C
item PALM, H
item WIEBOLD, W

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/17/2002
Publication Date: 12/1/2002
Citation: HONG, S.Y., SUDDUTH, K.A., KITCHEN, N.R., FRAISSE, C.W., PALM, H.L., WIEBOLD, W.J. COMBINING REMOTE SENSING AND CROP GROWTH MODELS TO ESTIMATE WITHIN-FIELD VARIABILITY. PROCEEDINGS 6TH INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE. 2002. CD-ROM (UNPAGINATED). AMERICAN SOCIETY OF AGRONOMY. MADISON, WI.

Interpretive Summary: Evaluation of within-field variability in crop growth and yield is an important aspect of precision farming. A number of different techniques have been used to evaluate yield variability and relate it to potential causes. One such technique is the use of remote sensing images of fields; another is the use of crop growth simulation models. Remote sensing provides good visualization of differences at the field scale, and remotely sensed data can be related to factors of agronomic importance, such as crop biomass and soil moisture. Crop models provide the ability to relate crop growth and yield to yield limiting factors but require large amounts of data to provide accurate results over multiple within-field locations. Combining crop models with data obtained from remote sensing images might be one way to obtain good information without a prohibitive amount of input data collection. In this study we evaluated the potential for determining leaf area index (LAI) from remote sensing images of corn and soybean fields. LAI, the amount of leaf area per ground area, is an important variable in crop modeling. We found that LAI determined from remote sensing images was well-correlated with LAI measured by in-field sampling. This is a first step toward integrating remote sensing and crop modeling at the within-field scale and will benefit other researchers working toward this goal. Producers and their advisors will also benefit from such integration, which will provide them with tools to better understand and manage within-field variations in crop growth and yield.

Technical Abstract: The overall objective of this study was to estimate leaf area index (LAI) as a function of image-derived vegetation indices and to use this derived data to improve site-specific model-based estimation of crop growth and yield across an entire field. The first research phase, reported herein, addresses the estimation of LAI based on vegetation indices and discusses the various methods for integration of the derived values with crop growth models. Remote sensing data were used for quantifying biophysical variability at several within-field monitoring sites for corn (Zea mays L.) and soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial and spectral resolutions were acquired using airborne and satellite platforms to investigate the relationship of spectral signatures and their ratios to leaf area index (LAI) for two central Missouri experimental fields under a corn-soybean rotation. Measured LAI could be expressed as a function of image-derived vegetation indices such as NDVI, RVI, and SAVI both for corn and soybean. Vegetation indices over-estimated LAI in early growth stages and under-estimated LAI after the grain or pod filling stage. CERES-Maize and CROPGRO-Soybean models were used to simulate site-specific crop growth and development. The CERES-Maize model over-predicted LAI in all monitoring sites. The CROPGRO-Soybean model generally showed good agreement between simulated and observed LAI.