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United States Department of Agriculture

Agricultural Research Service

Research Project: DEVELOP AND TRANSFER IRRIGATED AND NON-IRRIGATED PEANUT MANAGEMENT TECHNOLOGIES

Location: Peanut Research

Title: Use of aerial remote sensing imagery for estimating peanut ground cover and leaf area index

Authors
item Rajan, Nithya -
item Nuti, Russell
item Maas, Stephan -
item PAYTON, PAXTON
item Puppala, Naveen -

Submitted to: American Peanut Research and Education Society Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: July 12, 2010
Publication Date: N/A

Interpretive Summary: none required.

Technical Abstract: Leaf area index (LAI) and ground cover (GC) are important parameters as they are directly related light interception, plant growth, and yield. However determination of LAI and GC are often tedious processes and, for LAI require destructive sampling. Hence, remote sensing can be a tool for determining LAI and GC non-destructively. Numerous spectral-based models are available in the literature for estimating LAI. Many of these spectral-based models depend on the empirical relationships between LAI and vegetation indices, which sometimes make them site- and sensor-specific. We have conducted a study in a peanut field in Brownfield, TX to develop a procedure based on the Perpendicular Vegetation Index (PVI) to estimate GC and LAI. Aerial images were collected three times during the growing season using the Texas Tech Airborne Multispectral Remote Sensing System (TTAMRSS) at an altitude of approximately 3000 m. As the first step, vegetation cover is estimated from the ratio of the PVI for an image pixel to the PVI of full vegetation canopy (100% ground cover). In the second step, vegetation cover is converted to LAI using a model relating GC to LAI. The major advantages of using PVI compared to other indices such as Normalized Difference Vegetation Index (NDVI) is that that this method does not rely on empirical relationships.

Last Modified: 7/28/2014
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